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  • 1.
    Liu, B.
    et al.
    Department of Management Science, University of Strathclyde, Glasgow, United Kingdom.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Zhang, Liangwei
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 94941-94943, artikel-id 8762155Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper develops a dynamic maintenance strategy for a system subject to aging and degradation. The influence of degradation level and aging on system failure rate is modeled in an additive way. Based on the observed degradation level at the inspection, repair or replacement is carried out upon the system. Previous researches assume that repair will always lead to an improvement in the health condition of the system. However, in our study, repair reduces the system age but on the other hand, increases the degradation level. Considering the two-fold influence of maintenance actions, we perform reliability analysis on system reliability as a first step. The evolution of system reliability serves as a foundation for establishing the maintenance model. The optimal maintenance strategy is achieved by minimizing the long-run cost rate in terms of the repair cycle. At each inspection, the parameters of the degradation processes are updated with maximum a posteriori estimation when a new observation arrives. The effectiveness of the proposed model is illustrated through a case study of locomotive wheel-sets. The maintenance model considers the influence of degradation and aging on system failure and dynamically determines the optimal inspection time, which is more flexible than traditional stationary maintenance strategies and can provide better performance in the field.

  • 2.
    Chen, Jiayu
    et al.
    Beihang University, Beijing, China.
    Zhou, Dong
    Beihang University, Beijing, China.
    Guo, Ziyue
    Beihang University, Beijing, China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    LYU, Chuan
    Beihang University, Beijing, China.
    LU, Chen
    Beihang University, Beijing, China.
    An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 9022-9031Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    It is crucial to implement an effective and accurate fault diagnosis of a gearbox for mechanical systems. However, being composed of many mechanical parts, a gearbox has a variety of failure modes resulting in the difficulty of accurate fault diagnosis. Moreover, it is easy to obtain raw vibration signals from real gearbox applications, but it requires significant costs to label them, especially for multi-fault modes. These issues challenge the traditional supervised learning methods of fault diagnosis. To solve these problems, we develop an active learning strategy based on uncertainty and complexity. Therefore, a new diagnostic method for a gearbox is proposed based on the present active learning, empirical mode decomposition-singular value decomposition (EMD-SVD) and random forests (RF). First, the EMD-SVD is used to obtain feature vectors from raw signals. Second, the proposed active learning scheme selects the most valuable unlabeled samples, which are then labeled and added to the training data set. Finally, the RF, trained by the new training data, is employed to recognize the fault modes of a gearbox. Two cases are studied based on experimental gearbox fault diagnostic data, and a supervised learning method, as well as other active learning methods, are compared. The results show that the proposed method outperforms the two common types of methods, thus validating its effectiveness and superiority.

  • 3.
    An, Bolun
    et al.
    School of Civil Engineering, Beijing Jiaotong University, Beijing, China. Beijing Key Laboratory of Track Engineering, Beijing, China. Beijing Engineering Research Centre of Rail Traffic Line Safety and Disaster, Beijing, China.
    Gao, Liang
    School of Civil Engineering, Beijing Jiaotong University, Beijing, China. Beijing Key Laboratory of Track Engineering, Beijing , China. Beijing Engineering Research Centre of Rail Traffic Line Safety and Disaster, Beijing, China.
    Xin, Tao
    School of Civil Engineering, Beijing Jiaotong University, Beijing, China. Beijing Key Laboratory of Track Engineering, Beijing, China. Beijing Engineering Research Centre of Rail Traffic Line Safety and Disaster, Beijing, China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    An approach to evaluate wheel-rail match properties considering the flexibility of ballastless track: Comparison of rigid and flexible track models in wheel-rail profile matching2019Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, nr 3, s. 5-13Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many different wheel/rail profiles are used in the China high-speed railway, and vehicle operation safety and comfort will decrease if the inappropriate wheel-rail profile pair is used. To solve the problem of estimating the wheel-rail match, many numerical models, including vehicle system dynamic models and wheel-rail rolling contact models, have been established to analyse the wheel-rail dynamic responses. Both methods have less consideration of the flexibility and vibration characteristics of ballastless track, leading to deviations in the calculation of middle and high frequency vibration. This paper proposes a vehicle-flexible track coupling model and compares it with the vehicle dynamic model (vehicle-rigid track model). In the rigid track model, only the track irregularities are considered in the track module; the vibrations and deformations of rails, track slab and the foundation are considered in the flexible track model. Taking Chinese CRH3 series wheel profile S1002CN and rail profile CHN60 as examples and considering different track excitations, the two models are compared. The wheel-rail interaction forces, wheel-rail wear depths, wear volumes and vehicle accelerations are chosen as analysis indices for the comparative study.

    The results show that the wheel-rail forces of the flexible track model are larger than the rigid track model in the frequency range from 70 to 120Hz, while they decrease obviously in the frequency range above 150Hz. The differences in wear depths and volumes between the two models exceed 10%. Therefore, the flexible track model should be considered when studying the match properties of different wheel-rail pairs.

  • 4.
    Cai, Baoping
    et al.
    China University of Petroleum, Qingdao, Shandong, China.
    Kong, Xiangdi
    China University of Petroleum, Qingdao, Shandong, China.
    Liu, Yonghong
    China University of Petroleum, Qingdao, Shandong, China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. China University of Petroleum, Qingdao, Shandong, China.
    Yuan, Xiaobing
    China University of Petroleum, Qingdao, Shandong, China.
    Xu, Hongqi
    Rongsheng Machinery Manufacture Ltd. of Huabei Oilfield, Hebei, Renqiu, China.
    Ji, Renjie
    China University of Petroleum, Qingdao, Shandong, China.
    Application of Bayesian Networks in Reliability Evaluation2019Ingår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, nr 4, s. 2146-2157Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

  • 5.
    Bo, Lin
    et al.
    The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
    Xu, Guanji
    The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
    Liu, Xiaofeng
    The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 37611-37619Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The texture feature tensor established from a subband time–frequency image (TFI) was extracted and used to identify the fault states of a rolling bearing. The TFI of adaptive optimal-kernel distribution was optimally partitioned into TFI blocks based on the minimum frequency band entropy. The texture features were extracted from the co-occurrence matrix of each TFI block. Based on the order of the segmented frequency bands, the texture feature tensor was constructed using the multidimensional feature vectors from all the blocks; this preserved the inherent characteristic of the TFI structure and avoided the information loss caused by vectorizing multidimensional features. The linear support higher order tensor machine based on the feature tensor was applied to identify the fault states of the rolling bearing.

  • 6.
    Chi, Zhexiang
    et al.
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Chen, Ruoran
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Huang, Simin
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train2019Ingår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412XArtikel i tidskrift (Refereegranskat)
    Abstract [en]

    Environmental factors, like seasonality, have been proved to exert significant impact on reliability of China high-speed rail train wheels in this article. Most studies on polygonization of train wheels are based on physical models, mathematical models or simulation systems. Normally, characteristics and mechanisms of wheel polygonization are studied under ideal conditions without considering the impact of the environment. However, in practical use, there are many irregular wear wheels and irregular wear cannot be explained by theoretical models with assumptions of ideal conditions. We look at two possible factors in polygonization: seasonality and proximity to engines. Our analysis of field data shows the environmental factor has more impact on wheel polygonization than the mechanical factor. Based on the Bayesian models, the mean time to failure of the wheels under different operation conditions is conducted. A case study of China’s HSR train wheels is conducted to confirm the finding. The case study shows the degree of polygonal wear is much more severe in summer than other seasons. The finding may give a totally new research perspective on polygonization of train wheels. We use Bayesian analysis because this method is useful for small and incomplete data sets. We propose three Bayesian data-driven models.

  • 7.
    Zhang, Bo
    et al.
    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China.
    Liu, Xiubo
    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Identification of span of Multi-span Simply Supported Girders by the Longitudinal Level Waveforms2019Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, nr 3, s. 19-22Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In high-speed railways, the periodic component of longitudinal level on the top surface of the track can be observed for multi-span simply supported girders. To monitor this component, the paper proposes a method to determine the locating position of simply supported girders using longitudinal level waveforms. The method first applies time-frequency analysis to determine the locating position of simply supported girder bridges in the longitudinal level waveform. Then it adopts a local minimum detection strategy to identify the locating position of the girders. The method is evaluated using the longitudinal level data of a 100km high-speed railway in China; the results show good locating performance.

  • 8.
    Saari, Esi
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Liu, Bin
    Department of Management Science, University of Strathclyde, Glasgow, UK .
    Zhang, Liangwei
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China .
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data2019Ingår i: International Journal of Performability Engineering, ISSN 0973-1318, Vol. 15, nr 5, s. 1314-1325Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Assessment of system availability has been studied from the design stage to the operational stage in various system configurations using either analytic or simulation techniques. However, the former cannot handle complicated state changes, and the latter is computationally expensive. This study proposes a Bayesian approach to evaluate system availability. In this approach: 1) Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) are treated as distributions instead of being "averaged" to better describe real scenarios and overcome the limitations of data sample size; 2) Markov Chain Monte Carlo (MCMC) simulations are applied to take advantage of the analytical and simulation methods; and 3) a threshold is set up for Time to Failure (TTR) data and Time to Repair (TTR) data, and new datasets with right-censored data are created to reveal the connections between technical and "Soft" KPIs. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined by a Bayesian Weibull model and a Bayesian lognormal model, respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems). By comparing the results with and without considering the threshold for censoring data, we show the threshold can be used as a monitoring line for continuous improvement in the investigated mining company.

  • 9.
    Zhang, Shuangsheng
    et al.
    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China.Xuzhou Urban Water Resources Management Office, Xuzhou, China.
    Liu, Hanhu
    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China..
    Qiang, Jing
    School of Mathematics, China University of Mining and Technology, Xuzhou, China.
    Gao, Hongze
    GHD Services, Inc, Waterloo, Ontario, Canada.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem2019Ingår i: CMES - Computer Modeling in Engineering & Sciences, ISSN 1526-1492, E-ISSN 1526-1506, Vol. 119, nr 2, s. 373-394Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X0 , Y0) and release time (T0), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed. The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index. The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events. Based on the optimized monitoring well position and sampling frequency, the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach. The case study results show that the following parameters were obtained: 1) the optimal monitoring well position (D) is at (445, 200); and 2) the optimal monitoring frequency (Δt) is 7, providing that the monitoring events is set as 5 times. Employing the optimized monitoring well position and frequency, the mean errors of inverse modeling results in source parameters (M, X0 ,Y0 ,T0 ) were 9.20%, 0.25%, 0.0061%, and 0.33%, respectively. The optimized monitoring well position and sampling frequency can effectively safeguard the inverse modeling results in identifying the contamination source parameters. It was also learnt that the improved Metropolis-Hastings algorithm (a Markov chain Monte Carlo method) can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization, which significantly improved the accuracy and numerical stability of the inverse modeling results.

  • 10.
    Saari, Esi
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Zhang, Liangwei
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
    Liu, B.
    Department of Management Science, University of Strathclyde, Glasgow, United Kingdom.
    System availability assessment using a parametric Bayesian approach: a case study of balling drums2019Ingår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Assessment of system availability usually uses either an analytical (e.g., Markov/semi-Markov) or a simulation approach (e.g., Monte Carlo simulation-based). However, the former cannot handle complicated state changes and the latter is computationally expensive. Traditional Bayesian approaches may solve these problems; however, because of their computational difficulties, they are not widely applied. The recent proliferation of Markov Chain Monte Carlo (MCMC) approaches have led to the use of the Bayesian inference in a wide variety of fields. This study proposes a new approach to system availability assessment: a parametric Bayesian approach using MCMC, an approach that takes advantages of the analytical and simulation methods. By using this approach, mean time to failure (MTTF) and mean time to repair (MTTR) are treated as distributions instead of being “averaged”, which better reflects reality and compensates for the limitations of simulation data sample size. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined in a Bayesian Weibull model and a Bayesian lognormal model respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems)

  • 11.
    Källström, Elisabeth
    et al.
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Produkt- och produktionsutveckling. Volvo Construction Equipment, Eskilstuna, Sweden.
    Lindström, John
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Håkansson, Lars
    Linnaeus University, Växjö, Sweden.
    Karlberg, Magnus
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Produkt- och produktionsutveckling.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings2019Ingår i: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 10, artikel-id 014Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As more features are added to the heavy duty construction equipment, its complexity increases and early fault detection of certain components becomes more challenging due to too many fault codes generated when a failure occurs. Hence, the need to complement the present onboard diagnostic methods with more sophisticated diagnostic methods for adequate condition monitoring of the heavy duty construction equipment in order to improve uptime. Major components of the driveline (such as the gearbox, torque converter, bearings and axles) are such components. Failure of these major components of the driveline may results in the machine standing still until a repair is scheduled. In this paper, vibration based condition monitoring methods are presented with the purpose to provide a diagnostic framework possible to implement onboard for monitoring of critical driveline parts in order to reduce service cost and improve uptime. For the development of this diagnostic framework, sensor data from the gearbox, torque converter, bearings and axles are considered. Further, the feature extraction of the data collected has been carried out using adequate signal processing methods, which includes, Adaptive Line Enhancer, Order Power Spectrum respectively. In addition, Bayesian learning was utilized for adaptively learning of the extracted features for deviation detection. Bayesian learning is a powerful prediction method as it combines the prior information with knowlegde measured to make update. The results indicate that the vibration properties of the gearbox, torque converter, bearings and axle are relevant for early fault detection of the driveline. Furthermore, vibration provide information about the internal features of these components for detecting deviations from normal behavior.

    In this way, the developed methods may be implemented onboard for the continuous monitoring of these critical driveline parts of the heavy duty construction equipment so that if their health starts to degrade a service and/or repair may be scheduled well in advance of a potential failure and in that way the downtime of a machine may be reduced and costly replacements and repairs avoided.

  • 12.
    Liu, Bin
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Zhang, Liangwei
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan, 523808, China.
    Xie, Min
    Department of Industrial Engineering, Dongguan University of Technology, Dongguan.
    A Dynamic Maintenance Strategy for Prognostics and Health Management of Degrading Systems: Application in Locomotive Wheel-sets2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper develops a dynamic maintenance strategy for prognostics and health management (PHM) of a degrading system. The system under investigation suffers a continuous degradation process, modeled as a Gamma process. In addition to the degradation process, the system is subject to aging, which contributes to the increase of failure rate. An additive model is employed to describe the impact of degradation level and aging on system failure rate. Inspection is implemented upon the system so as to effectively avoid failure. At inspection, the system will be repaired or replaced in terms of the degradation level. Different from previous studies which assume that repair will always lead to an improvement on system degradation, in our study, however, the effect of repair is twofold. It will reduce the system age to 0 but will increase the degradation level. System reliability is analyzed as a first step to serve for the maintenance decision making. Based on the reliability evolution, a maintenance model is formulated with respect to the inspection time. The optimal decision is achieved by minimizing the expected cost rate in one repair cycle. Finally, a case study of locomotive wheel-sets is adopted to illustrate the effectiveness of the proposed model. Our approach incorporates the joint influence of aging and degradation process, and determines the optimal inspection time dynamically, which exhibits the advantage of flexibility and can achieve better performance in field use.

  • 13.
    Zhang, Liangwei
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems2018Ingår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 139, nr 1, s. 50-63Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents an unsupervised, density-based approach to anomaly detection. The purpose is to define a smooth yet effective measure of outlierness that can be used to detect anomalies in nonlinear systems. The approach assigns each sample a local outlier score indicating how much one sample deviates from others in its locality. Specifically, the local outlier score is defined as a relative measure of local density between a sample and a set of its neighboring samples. To achieve smoothness in the measure, we adopt the Gaussian kernel function. Further, to enhance its discriminating power, we use adaptive kernel width: in high-density regions, we apply wide kernel widths to smooth out the discrepancy between normal samples; in low-density regions, we use narrow kernel widths to intensify the abnormality of potentially anomalous samples. The approach is extended to an online mode with the purpose of detecting anomalies in stationary data streams. To validate the proposed approach, we compare it with several alternatives using synthetic datasets; the approach is found superior in terms of smoothness, effectiveness and robustness. A further experiment on a real-world dataset demonstrated the applicability of the proposed approach in fault detection tasks.

  • 14.
    Cai, Baoping
    et al.
    College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao.
    Huang, Lei
    College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Xie, Min
    Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon.
    Bayesian Networks in Fault Diagnosis: some research issues andchallenges2017Ingår i: Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden / [ed] Diego Galar, Dammika Seneviratne, Luleå: Luleå tekniska universitet, 2017, s. 26-32Konferensbidrag (Refereegranskat)
  • 15.
    Lin, Jing (Janet)
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    IN2CLOUD: A novel concept for collaborative management of big railway data2017Ingår i: Frontiers of Engineering Management, ISSN 2095-7513, Vol. 4, nr 4, s. 428-436Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the EU Horizon 2020 Shift2Rail Multi-Annual Action Plan, the challenge of railway maintenance is generating knowledge from data and/or information. Therefore, we promote a novel concept called “IN2CLOUD,” which comprises three sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning, and 3) collaborative management using asset-related data acquired from the intelligent hybrid cloud. The concept is developed under the assumption that organizations want/need to learn from each other (including domain knowledge and experience) but do not want to share their raw data or information. IN2CLOUD will help the movement of railway industry systems from “local” to “global” optimization in a collaborative way. The development of cutting-edge intelligent hybrid cloud-based solutions, including information technology (IT) solutions and related methodologies, will enhance business security, economic sustainability, and decision support in the field of intelligent asset management of railway assets.

  • 16.
    Yu, Huan
    et al.
    School of Reliability and Systems Engineering, Beihang University.
    Yang, Jun
    School of Reliability and Systems Engineering, Beihang University.
    Lin, Jing (Janet)
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Zhao, Yu
    School of Reliability and Systems Engineering, Beihang University.
    Reliability evaluation of non-repairable phased-mission common bus systems with common cause failures2017Ingår i: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 111, s. 445-457Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Phased-mission common bus (PMCB) systems are systems with a common bus structure, performing missions with consecutive and non-overlapping phases of operations. PMCB systems are found throughout industry, e.g., power generating systems, parallel computing systems, transportation systems, and are sometimes characterized by their common cause failures. Reliability evaluation of PMCB systems plays an important role in system design, operation, and maintenance. However, current studies have focused on either phased-mission systems or common bus systems because of their complexity. The challenge in practice is to consider phased-mission systems together with common bus structures and common cause failures. To solve this problem, we propose an evaluation algorithm for PMCB systems with common cause failures by coupling the structure function of a common bus performance sharing system and an existing recursive algorithm. To weigh the efficiency of the proposed algorithm, its complexity is discussed. To improve the reliability of PMCB systems, we adopt the genetic algorithm method to search for the optimal allocation strategies of the service elements. We use both analytical and numerical examples to illustrate the application of the proposed algorithm.

  • 17.
    Zhang, Liangwei
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Sliding Window-based Fault Detection from High-dimensional Data Streams2017Ingår i: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, Vol. 47, nr 2, s. 289-303, artikel-id 7509594Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    High-dimensional data streams are becoming increasingly ubiquitous in industrial systems. Efficient detection of system faults from these data can ensure the reliability and safety of the system. The difficulties brought about by high dimensionality and data streams are mainly the ``curse of dimensionality'' and concept drifting, and one current challenge is to simultaneously address them. To this purpose, this paper presents an approach to fault detection from nonstationary high-dimensional data streams. An angle-based subspace anomaly detection approach is proposed to detect low-dimensional subspace faults from high-dimensional datasets. Specifically, it selects fault-relevant subspaces by evaluating vectorial angles and computes the local outlier-ness of an object in its subspace projection. Based on the sliding window strategy, the approach is further extended to an online mode that can continuously monitor system states. To validate the proposed algorithm, we compared it with the local outlier factor-based approaches on artificial datasets and found the algorithm displayed superior accuracy. The results of the experiment demonstrated the efficacy of the proposed algorithm. They also indicated that the algorithm has the ability to discriminate low-dimensional subspace faults from normal samples in high-dimensional spaces and can be adaptive to the time-varying behavior of the monitored system. The online subspace learning algorithm for fault detection would be the main contribution of this paper.

  • 18.
    Asplund, Matthias
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Assessment of the data quality of wayside wheel profile measurements2016Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 19, nr 3, s. 19-25Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To evaluate the behaviour and the condition of a railway wheel in relation to performance and safety criteria, the wheel profile can be measured. This can be achieved using manual methods or automatic systems mounted along the railway track. Such systems have the advantage that they can measure a vast number of profiles, enabling new possibilities of performing statistical analyses of the results and pinpointing bad wheels at an early stage. These wayside measurement systems are, however, subjected to different environmental conditions that can affect the data quality of the measurement. If one is to be able to use automatic wheel profile measurements, the data quality has to be controlled in order to facilitate maintenance decisions. This paper proposes a method for the data quality assessment of an automatic wayside condition monitoring system measuring railway rolling stock wheels. The purpose of the assessment method proposed in this paper is to validate individual wheel profile measurements to ensure the accuracy of the wheel profile measurement data and hence the following data analysis. The method consists of a check routine based on the paired t-test, which uses a hypothesis test to verify if the null hypotheses are true. The check routine compares measurements of passing wheels rolling to a certain destination with measurements of the same wheels returning from that destination. The routine of comparing measurements of the same wheel, which is performed by four sensors (one on each side of each rail), will ensure that the sensors generate the same data for the same sample. A case study is presented which shows how the method can detect a faulty setup of the measurement system and prevent incorrect interpretations of the data from different measurement units in the same system. The paper ends with a discussion and conclusions concerning the improvements that are presented.

  • 19.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bayesian Reliability with MCMC: Opportunities and Challenges2016Ingår i: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, s. 575-585Konferensbidrag (Refereegranskat)
    Abstract [en]

    The recent proliferation of Markov Chain Monte Carlo (MCMC) approaches has led to the use of the Bayesian inference in a wide variety of fields, including reliability engineering. With the current (and future) proliferation of new products, old problems continue to hamper us, while new challenges keep appearing. In Bayesian reliability, these include but are not limited to: (1) achieving and making use of prior information; (2) applying small data sets or system operating/environmental (SOE) data with big and complex data; and (3) making posterior inferences from high-dimensional numerical integration. To deal with old problems while meeting new challenges, this paper proposes an improved procedure for Bayesian reliability inference with MCMC, discussing modern reliability data and noting some applications where the Bayesian reliability approach with MCMC can be used. It also explores opportunities to use Bayesian reliability models to create stronger statistical methods from prior to posterior. Finally, it outlines some practical concerns and remaining challenges for future research.

  • 20.
    Lin, Janet
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nordmark, Thomas
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Zhang, Liangwei
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data analysis of heavy haul wagon axle loads on Malmbanan line, Sweden: A case study for LKAB2016Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The research presented in this report was carried out by Operation and Maintenance Engineering at Luleå University of Technology (LTU) from November 2015 to April 2016. LKAB initiated the research study and provided financial support. The purpose of this research was to support LKAB and Trafikverket in their operational strategy review and optimization of future axle load implementations. It developed five research questions and answered them by analyzing the data for the Malmbanan iron ore train axle loads for 2015.Data analysis comprises four parts. In the first part (section 2), the analysis focuses on axle loads of all loaded trains operating at three different terminals: Kiruna, Malmberget, and Svappavaara. In addition, it examines the differences of three weighing locations in Kiruna, five weighing locations in Malmberget and four weighing locations in Svappavaara (12 weighing locations). Based on these results, the analysis in the second part (section 3) focuses on the heavy haul wagon. Wagon loads are evaluated and predicted for different loading rules (31.0 and 32.5 tons separately). To optimize the current loading rules, the third part of the analysis (section 4) proposes a novel approach to optimize the wagon axle loads: “three sigma prediction”. Under this approach, Kiruna, Malmberget and Svappavaara can set new target loads based on various risk levels. In the fourth and final part of the data analysis (section 5), a comparison study is carried out by collecting axle load data for the test train (with a 32.5 ton axle load) using three different measurement systems in Malmberget, Sävast and Sunderbyn. Finally, sections 6 and 7 summarize the results and make some recommendations for future work. The work presented in this report should give LKAB and Trafikverket a good overview of the load distribution for the ore trains operating on Malmbanan line. It can serve as input into the process of evaluating possible changes in axle load limits. It also gives LKAB a base from which to identify and work with optimization of the various loading places to load trains more efficiently and save costs.

  • 21.
    Norrbin, Per
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Energy efficiency optimization for railway switches & crossings: a case study in Sweden2016Konferensbidrag (Refereegranskat)
  • 22.
    Asplund, Matthias
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Evaluating the measurement capability of a wheel profile measurement system by using GR&R2016Ingår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 92, s. 19-27Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reliable data with less variation play a key role for acceptance of the usefulness of the measurement output of a wheel profile measurement system (WPMS) in a railway network. However, in practice, most studies are carried out without checking the reliability of data from such a system, which may lead to inappropriate maintenance strategies. To ensure the measurement capability of WPMS and to support robust maintenance in railway systems, this study has evaluated measurement data for the flange height, flange thickness, flange slope, and tread hollowing of rolling stock wheels by using gauge repeatability and reproducibility (GR&R). In this study, acceptance and rejection criteria for the precision-to-tolerance ratio (PTR), signal-to-noise ratio (SNR), and discrimination ratio (DR) have been employed to evaluate the measurement capabilities. For the purpose of illustration, we have implemented a new proposed approach. This approach involves both an analysis using graphs with four regions with a confidence interval (CI) of 95% and an analysis using a graph with three regions with only the predicted values; the latter type of graph represents an innovation made in this study. The results show that the measurements of the tread hollowing and flange slope are on an acceptable level, while those for the flange height and flange thickness have to be rejected as unacceptable. The action proposed to increase the quality of data on the flange height and flange thickness is to enhance the calibration of the WPMS. In conclusion, GR&R is a useful tool to evaluate the measurement capability of WPMS and to provide helpful support for maintenance decision making.

  • 23.
    Norrbin, Per
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Infrastructure robustness for railway systems2016Ingår i: International Journal of Performability Engineering, ISSN 0973-1318, Vol. 12, nr 3, s. 249-264, artikel-id 5Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the railway industry, most maintenance approaches are based on certain “specified conditions”, e.g., RAMS (Reliability, Availability, Maintainability and Safety) and Risk. But the reality is more complex. Instead of the assumed conditions, “unfavorable conditions” may occur from either natural or operational causes, where robustness can be an effective approach. To adequately consider “unfavorable conditions” and to reduce “uncertainties” in railway maintenance, this study conducts a holistic examination of railway infrastructure robustness. It gives an overview of robustness and discusses some relevant studies. It then develops a new road map for railway infrastructure robustness, including a novel definition and a new framework of robustness management, based on continuous improvement. It explores the opportunities of applying the road map to the infrastructure of railway systems and outlines some practical concerns and remaining challenges for future research. The results provide guidelines for other research into robust infrastructure in railway maintenance.

  • 24.
    Zhang, Liangwei
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    An Angle-based Subspace Anomaly Detection Approach to High-dimensional Data: With an Application to Industrial Fault Detection2015Ingår i: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 142, s. 482-497Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces.

  • 25.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bayesian Semi-parametric Analysis for Locomotive Wheel Degradation using Gamma Frailties2015Ingår i: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 229, nr 3, s. 237-247Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A reliability study based on a Bayesian semi-parametric framework is performed in order to explore the impact of the position of a locomotive wheel on its service lifetime and to predict its other reliability characteristics. A piecewise constant hazard regression model is used to analyse the lifetime of locomotive wheels using degradation data and taking into account the bogie on which the wheel is located. Gamma frailties are included in this study to explore unobserved covariates within the same group. The goal is to flexibly determine reliability for the wheel. A case study is performed using Markov chain Monte Carlo methods and the following conclusions are drawn. First, a polynomial degradation path is a better choice for the studied locomotive wheels; second, under given operational conditions, the position of the locomotive wheel, i.e. on which bogie it is mounted, can influence its reliability; third, a piecewise constant hazard regression model can be used to undertake reliability studies; fourth, considering gamma frailties is useful for exploring the influence of unobserved covariates; and fifth, the wheels have a higher failure risk after running a threshold distance, a finding which could be applied in optimisation of maintenance activities

  • 26.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nordmark, Thomas
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data analysis of wheel-sets' running surface wear based on re-profiling measurement: a case study at Malmbanan2015Ingår i: IHHA 2015 Conference proceedings: 21 – 24 June 2015, Perth, Australia, International Heavy Haul Association , 2015, s. 924-930Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, the wheel-sets’ running surface wear data based on re-profiling measurement from 16 bogies of heavy haul locomotives at Malmbanan (Sweden) are studied. The case study undertakes: reliability and degradation analysis, wear rate analysis and their comparison (including total wear rate, natural wear rate, re-profiling wear rate, the ratio of re-profiling and natural wear). The results show that: 1) for the studied group, a linear degradation path is more suitable; 2) following the linear degradation, the best life distribution is a 3-parameter Weibull distribution; 3) comparing the wearing data of the wheel-sets’ running surfaces is an effective way to optimize maintenance strategies; 4) more natural wear occurs for the wheels installed in axle 1 and axle 3, supportive evidence for other related studies at Malmbanan.

  • 27.
    Asplund, Matthias
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Enhancing the quality of data from a wheel profile measurement system: a proposed approach2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    This investigation proposes a method for increasing the quality of data from an automatic condition monitoring system for railway rolling stock wheels, in order to assure the right data quality for further use of the data. The data quality improvement is used to ensure a higher reliability of the data analysis and to propose a new check routine to ensure that the sensors generate the same data for the same sample. A case study on field data shows how the data from different measurement setups differ for three of four measurements and why this check routine is needed. The paper ends with a discussion and conclusions concerning the improvements that are presented.

  • 28.
    Aitomäki, Yvonne
    et al.
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Materialvetenskap.
    Allard, Christina
    Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Samhällsvetenskap.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Sandström, Anders
    Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.
    LTU Teaching guide to e-learning: how to clear the mist of teaching through the cloud2015Konferensbidrag (Övrigt vetenskapligt)
  • 29.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Pulido, Julio
    ReliaSoft Corporation.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Reliability Analysis for Preventive Maintenance based on Classical and Bayesian Semi-parametric Degradation Approaches using Locomotive Wheel-sets as a Case Study2015Ingår i: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 134, s. 143-156Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper undertakes a general reliability study using both classical and Bayesian semi-parametric degradation approaches. The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework. With the proposed classical approach, both Accelerated Life Tests (ALT) and Design of Experiments (DOE) technology are used to determine how each critical factor affects the prediction of performance. With the Bayesian semi-parametric approach, a piecewise constant hazard regression model is used to establish the lifetime using degradation data. Gamma frailties are included to explore the influence of unobserved covariates within the same group. Ideally, results from the classical and Bayesian approaches will complement each other. To demonstrate these approaches, this paper considers a case study of locomotive wheel-set reliability. The degradation data are prepared by considering an Exponential and a Power degradation path separately. The results show that both classical and Bayesian semi-parametric approaches are useful tools to analyse degradation data and can, therefore, support a company in decision making for preventive maintenance. The approach can be applied to other technical problems (e.g. other industries, other components).

  • 30.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    An Integrated Procedure for Bayesian Reliability Inference using Markov Chain Monte Carlo Methods2014Ingår i: Journal of Quality and Reliability Engineering, ISSN 2314-8055, E-ISSN 2314-8047, Vol. 2014, artikel-id 264920Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The recent proliferation of Markov chain Monte Carlo (MCMC) approaches has led to the use of the Bayesian inference in a wide variety of fields. To facilitate MCMC applications, this paper proposes an integrated procedure for Bayesian inference using MCMC methods, from a reliability perspective. The goal is to build a framework for related academic research and engineering applications to implement modern computational-based Bayesian approaches, especially for reliability inferences. The procedure developed here is a continuous improvement process with four stages (Plan, Do, Study, and Action) and 11 steps, including: (1) data preparation; (2) prior inspection and integration; (3) prior selection; (4) model selection; (5) posterior sampling; (6) MCMC convergence diagnostic; (7) Monte Carlo error diagnostic; (8) model improvement; (9) model comparison; (10) inference making; (11) data updating and inference improvement. The paper illustrates the proposed procedure using a case study.

  • 31.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Pulido, Julio
    ReliaSoft Corporation.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Analysis for Locomotive Wheels’ Degradation2014Ingår i: 2014 proceedings - Annual Reliability and Maintainability Symposium (RAMS 2014): Colorado Springs, CO; United States, 27 - 30 January 2014, Piscataway, NJ: IEEE Communications Society, 2014, artikel-id 6798521Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper undertakes a reliability study using both classical and Bayesian semi-parametric frameworks to explore the impact of a locomotive wheel's position on its service lifetime and to predict its other reliability characteristics. The goal is to illustrate how degradation data can be modeled and analyzed by using classical and Bayesian approaches. The adopted data in the case study have been collected from the Swedish company. The results show that: 1) an exponential degradation path is a better choice for the studied locomotive wheels; 2) both classical and Bayesian semi-parametric approaches are useful tools to analysis degradation data; 3) under given operation conditions, the position of the locomotive wheel could influence its reliability

  • 32.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Comparison study of heavy haul locomotive wheels' running surfaces wearing2014Ingår i: Eksploatacja i Niezawodnosc, ISSN 1507-2711, Vol. 16, nr 2, s. 276-287Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The service life of railway wheels can differ significantly depending on their installed position, operating conditions, re-profiling characteristics, etc. This paper compares the wheels on two selected locomotives on the Iron Ore Line in northern Sweden to explore some of these differences. It proposes integrating reliability assessment data with both degradation data and re-profiling performance data. The following conclusions are drawn. First, by considering an exponential degradation path and given operation condition, the Weibull frailty model can be used to undertake reliability studies; second, among re-profiling work orders, rolling contact fatigue (RCF) is the principal reason; and third, by analysing re-profiling parameters, both the wear rate and the re-profiling loss can be monitored and investigated, a finding which could be applied in optimisation of maintenance activities.

  • 33.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data Analysis of Heavy Haul Locomotive Wheel-sets’ Running Surface Wear at Malmbanan2014Rapport (Övrigt vetenskapligt)
  • 34.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data analysis of heavy haul locomotive wheel-sets’ running surface wear at Malmbanan, Sweden2014Ingår i: Newsletter of European Safety and Reliability AssociationArtikel i tidskrift (Övrig (populärvetenskap, debatt, mm))
  • 35.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Reliability analysis for degradation of locomotive wheels using parametric Bayesian approach2014Ingår i: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 30, nr 5, s. 657-667Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper undertakes a reliability study using a Bayesian survival analysis framework to explore the impact of a locomotive wheel's installed position on its service lifetime and to predict its reliability characteristics. The Bayesian Exponential Regression Model, Bayesian Weibull Regression Model and Bayesian Log-normal Regression Model are used to analyze the lifetime of locomotive wheels using degradation data and taking into account the position of the wheel. This position is described by three different discrete covariates: the bogie, the axle and the side of the locomotive where the wheel is mounted. The goal is to determine reliability, failure distribution and optimal maintenance strategies for the wheel. The results show that: (i) under specified assumptions and a given topography, the position of the locomotive wheel could influence its reliability and lifetime; (ii) the Bayesian Log-normal Regression Model is a useful tool.

  • 36.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bayesian Integrated Reliability Analysis for Locomotive Wheels2013Ingår i: Newsletter of European Safety and Reliability Association, nr June, s. 5-7Artikel i tidskrift (Övrig (populärvetenskap, debatt, mm))
  • 37.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Asplund, Matthias
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bayesian parametric analysis for reliability study of locomotive wheels2013Ingår i: Proceedings on the 59th Annual Reliability and Maintainability Symopsium (RAMS 2013), 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a new approach to study reliability of locomotive wheels with Bayesian framework, utilizing locomotive wheel degradation data sets that can be small or incomplete. In our study, a linear degradation path is assumed and locomotive wheels’ installation positions are considered as covariates. A Markov Chain Monte Carlo (MCMC) computational method is also implemented. In the case study, data were collected from a Swedish railway company. This data includes, the diameter measurements of the locomotive wheels, total distances corresponding to their “time to maintenance”, and the wheels’ bill of material (BOM) data. During this study, likelihood functions were constructed for Expontional regression models, Weibull regression models, and lognormal regression models. The results show that the locomotive wheels’ lifetimes are dependent on installation positions. For the studied locomotive wheels data, the Lognormal regression model is a better choice, because the model obtained the lowest Deviance Information Criterion (DIC) values. In addition, under current operation situation (e.g. topography) and current maintenance strategies (re-profiled, lubrication, etc.), the locomotive wheels installed in the second bogie have longer lifetimes than those installed in the first bogie; the wheels installed on the “back” axle have longer lifetimes than those on the “front” axle; and the right side wheels’ lifetime is shorter than that for the left side under a given running situation.

  • 38.
    Palo, Mikael
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Larsson-Kråik, Per-Olof
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance performance Improvement for rolling stock wheels2013Ingår i: PHM2013: 2013 Prognostic and System Health Management: Milan 8-11 September 2013 / [ed] Enrico Zio; Piero Baraldi, AIDIC Servizi S.r.l. , 2013, s. 727-732Konferensbidrag (Refereegranskat)
    Abstract [en]

    The service life of a railway wagon wheel can be significantly reduced through failure or damage, leading to excessive costs and accelerated deterioration. In order to monitor the performance of wheels on heavy haul wagons, this paper proposes implementing the Plan, Do, Study, and Act (PDSA) maintenance performance improvement process. As a case study, it looks at wheels on the heavy haul wagons of a Swedish company, considering all factors that may influence the need for maintenance. After investigating the PDSA process, it proposes the use of Key Performance Indicators (KPIs) for both risk and economic reasons. The paper concludes that the PDSA process and KPIs are useful tools to improve the maintenance performance of railway wheels

  • 39.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Using integrated reliability analysis to optimise maintenance strategies: a Bayesian integrated reliability analysis of locomotive wheels2013Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The goal of the research presented in this report is to propose, develop and test an integrated reliability analysis to optimise the maintenance strategies of the railway industry. This integrated analysis applies traditional statistics theories as well as Bayesian statistics using Markov Chain Monte Carlo (MCMC) methodologies. Using the Bayesian inference leads to greater flexibility because such analysis can simultaneously accommodate the following: • Small sample data;• Incomplete data set, including censored or truncated data;• Complex operational environments. In this report, an integrated procedure for Bayesian reliability inference using MCMC is applied to a number of case studies using locomotive wheel degradation data from Iron Ore Line (Malmbanan), Sweden. The research explores the impact of a locomotive wheel’s installed position on its service lifetime and attempts to predict its reliability characteristics by using parametric models, non-parametric models, frailty factors, etc.

  • 40.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Information sharing in a spares demand system2012Ingår i: 2012 IEEE International Technology Management Conference: Dallas,USA, 24 June - 27 June, 2012, Piscataway, MJ: IEEE Communications Society, 2012, s. 36-44Konferensbidrag (Refereegranskat)
    Abstract [en]

    Spare parts inventory management differs from other manufacturing inventory managements mainly due to its speciality in function with maintenance. Furthermore, spares demand management shows more complexities and differs from other productions’ SCM (Supply Chain Management). Recently, studies on information sharing in SCM attract more and more researchers’ attention because of its ability to counter the so-called “bullwhip effect”. Based on the authors’ consultant experiences and studies in practice, information sharing also plays a pivotal role on optimizing the effectiveness of the spares demand system. However, discussions those stand in the point of spares optimization management are limited, which should be a better combination of practical as maintenance engineers and managers making decision support for spares management. Therefore, this paper aims to help them to understand the information sharing in a spares demand system, to illuminate how to optimize the systems’ performance with it, and to elaborate how to improve information sharing process itself. First, a spares demand system was promoted, and information sharing in the system was investigated from a downstream and an upstream process separately. Second, performances of information sharing in such spares demand system were discussed from both qualitative and quantitative viewpoints. Third, the effective ways regarding information sharing utilization to optimize the spares demand system came into question. And finally, how to improve information sharing process in the mentioned system was discussed.

  • 41.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A step-by-step model to improve delivery assurance: a case study in mining industry2011Ingår i: 2011 IEEE International Conference on Quality and Reliability: ICQR 2011, Bangkok 14 September 2011 -17 September 2011, Piscataway, NJ: IEEE Communications Society, 2011, s. 36-40Konferensbidrag (Refereegranskat)
    Abstract [en]

    Achieving a high level of Delivery Assurance is critical, but a difficult task for plant managers and engineers on site. In this paper, a step-by-step model was proposed to help making decisions for improving delivery assurance. After introducing the model in the second part, steps were discussed with general inputs, methods & techniques and outputs, separately. Besides, a case study was shown in the fourth part, to demonstrate its use in mining industry

  • 42.
    Zhu, Huiming
    et al.
    College of Business Administration, Hunan University.
    Guan, Haoyun
    College of Business Administration, Hunan University.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Yu, Keming
    Brunel University.
    Zeng, Zhaofa
    College of Business Administration, Hunan University.
    Bayesian multivariate monitoring models for process mean vectors based on multistage predictive distributions2011Ingår i: Hunan Daxue Xuebao (Ziran Kexue Ban), ISSN 1674-2974, Vol. 38, nr 3, s. 82-86Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The aim of this paper is to utilize sample information in different stages and solve the parameter uncertainty risk in statistical process control. This paper introduces a reference prior distribution for the parameters in quality models, and constructs the warning lines and action lines to monitor the mean vectors change according to the predictive distribution as well as the relationship between the multivariate t distribution and F distribution. When the current stage is under control, the parametric posterior distribution is considered to be their priori distribution in the next stage, in which the sequential Bayesian mean vector control method is established.

  • 43.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nordenvaad, Magnus Lundberg
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Zhu, Huiming
    College of Business Administration, Hunan University.
    Bayesian survival analysis in reliability for complex system with a cure fraction2011Ingår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 7, nr 2, s. 109-120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis. To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model

  • 44.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    House of maintenance management in mining industry2011Ingår i: 2011 IEEE International Conference on Quality and Reliability: ICQR 2011: Bamgkok, 14 September 2011 - 17 September 2011, Piscataway, NJ: IEEE Communications Society, 2011, s. 46-51Konferensbidrag (Refereegranskat)
    Abstract [en]

    In practice, both maintenance engineers on site and maintenance managers should have a clear idea of how the maintenance plans are being carried out, and they will be improved continuously in the future. In this paper, the House of Maintenance Management (HOMM) is proposed to describe that what should be included in, for maintenance management. With HOMM, a continuous improvement process PDSA (Plan, Do, Study, Act) for maintenance management could be implemented. Focusing on mining industry, the details for mapping HOMM are discussed for the roof, ceiling, walls, floor, doors & windows, separately. Additionally, how to support decision making with this HOMM map has been demonstrated with a case study.

  • 45.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance spares inventory management: performance measurement using a HOMM2011Ingår i: MPMM 2011: Maintenance Performance Measurement & Management: Conference Proceedings / [ed] Diego Galar; Aditya Parida; Håkan Schunnesson; Uday Kumar, Luleå: Luleå tekniska universitet, 2011, s. 77-83Konferensbidrag (Refereegranskat)
    Abstract [en]

    Spare parts inventory management differs from both work-in-process inventory management and finished product inventory management mainly due to its unique aspects in function with maintenance. Furthermore, its inventory management shows more complexities and the performance measurement differs from other productions’ either. Studies both in theoretical research and in practice have shown that only some concrete figures had been considered in traditional KPI’s for spare parts inventory management, including the total stock value, cost of keeping stock, critical spares stock-outs, operational downtime due to stock-outs, rate of circulation, etc. However, such KPI’s may only reflect limited results from spares inventory management. In another word, they can not help to find out the root causes of which aspects are the management’s bottlenecks, or from which aspects it can be improved step-by-step. This paper aims to propose a new way to measure the performance of spares inventory management from the perspective of a House of Maintenance Management (HOMM). First, the HOMM-Spares with PDSA (Plan, Do, Study, Act) thinking will be promoted with the consideration of spares management. Second, management review for spares inventory using the HOMM-Spares will be discussed in details and the performance measurement will be clarified. Obviously, with the new promoted measurement system, we can not only review its performance from a more systematic standing point, but also, the continuously improvement plan with a more scientific analysis will be achieved simultaneously. How to support decision making with this spares performance measurement system is demonstrated as well with a case study.

  • 46.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nordenvaad, Magnus Lundberg
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Spares demand system with consideration of integration management and optimization2011Ingår i: 2011 International Conference on Mechanical, Industrial, and Manufacturing Engineering: MIME 2011, Melbourne, Australia, 15 January-16 January 2011, 2011, s. 1-4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Inventory management differs from other manufacturing inventory managements, mainly due to its specialists in function with maintenance. So far, enormous attention has been paid by standing on spares’ manufacturingfactories, sales companies, end users’ purchasing departments, or maintenance engineers, separately. However, not only “bullwhip effect” in forecasting spares demands, but also deteriorated relationships among the spares supply chains have shown that, spares optimization strategies with isolated consideration couldonly bring short-term or partial improvements. In this paper, the spares demand system with consideration of integration management is promoted, the new Solid-Net relationships among four main components are elaborated. Then, the root causes of ineffective in spares demand system are analyzed. Also, distinctoptimization policies are illustrated. What’s more, successful stories in practice are cited.

  • 47. Lin, Jing
    et al.
    Dong, Liang
    SKF.
    Zhang, Liangwei
    SKF.
    Plan for Spares Management and its Application with PDCA process2010Ingår i: China Plant Engineering, ISSN 1671-0711, Vol. 264, nr 1, s. 25-27Artikel i tidskrift (Refereegranskat)
  • 48. Lin, Jing
    Requirements Predictive Models for Spares toward to Customers2010Ingår i: Chinese Journal of Science and Technology Innovation Herald, ISSN 1674-098X, nr 145, s. 14-15Artikel i tidskrift (Refereegranskat)
  • 49.
    Zhu, Huiming
    et al.
    Hunan University.
    Lin, Jing
    Bayesian Econometrics2009Bok (Refereegranskat)
  • 50. Lin, Jing
    et al.
    Zhu, Huiming
    Hunan University.
    A Cure Rate Model in Reliability for Complex System2008Ingår i: 2008 IEEE International Conference on Industrial Engineering and Engineering Management: 2008 IEEE IEEM: Singapore, December8-December11 2008, IEEE Communications Society, 2008, s. 1395-1399Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a new approach to do reliability analysis for complex system, where a certain fraction of the subsystems is defined as a ¿cure fraction¿ under the consideration that such subsystems are ¿longevous¿ compared with the entire system. Including introducing environment covariates and the joint power prior, the proposed model is developed with the Bayesian survival analysis method, and thus the problems for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameters¿ posterior distribution. Finally, a numeric example is discussed to demonstrate the proposed model.

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