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  • 1.
    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 cloud2015Konferansepaper (Annet vitenskapelig)
    Fulltekst (pdf)
    FULLTEXT01
  • 2.
    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 matching2019Inngår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, nr 3, s. 5-13Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 3.
    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&R2016Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 92, s. 19-27Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 4.
    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 measurements2016Inngår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 19, nr 3, s. 19-25Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 5.
    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 approach2015Konferansepaper (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    FULLTEXT01
  • 6.
    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 Tensor2019Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 37611-37619Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 7.
    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 andchallenges2017Inngår 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. 26-32Konferansepaper (Fagfellevurdert)
    Fulltekst (pdf)
    Proceedings
  • 8.
    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 Evaluation2019Inngår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, nr 4, s. 2146-2157Artikkel i tidsskrift (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    fulltext
  • 9.
    Chen, Haizhou
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. College of Electromechanical Engineering, Qingdao University of Science and Technology, 266061 Qingdao, People’s Republic of China.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Division of Product Realization, Mälardalen University, 63220 Eskilstuna, Sweden.
    Chen, Nian
    College of Economics and Management, Qingdao University of Science and Technology, 266061 Qingdao, People’s Republic of China.
    Xu, Guanji
    Qingdao Huihe Zhongcheng Intelligent Technology Ltd, 266108 Qingdao, People’s Republic of China.
    An integrated approach to evaluate the measurement capability and acceptability of acoustic emission sensors2024Inngår i: Measurement science and technology, ISSN 0957-0233, E-ISSN 1361-6501, Vol. 35, nr 2, artikkel-id 025132Artikkel i tidsskrift (Fagfellevurdert)
  • 10.
    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 Diagnosis2019Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 9022-9031Artikkel i tidsskrift (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    fulltext
  • 11.
    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 train2020Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 149, artikkel-id 107022Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 12.
    Chi, Zhexiang
    et al.
    Department of Industrial Engineering, Tsinghua University, Beijing, China .
    Yang, Lijian
    Center for Statistical Science, Tsinghua University, Beijing, China .
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Huang, Simin
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Prognostics of polygonalization of high-speed railway train wheels using a generalized additive model smoothed by spline-backfitted kernel2019Inngår i: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), IEEE, 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A method for the prognosis of polygonalization in high-speed railway train wheels is developed based on a generalized additive model. Unlike most previous studies, this study uses field data, so findings can help improve practical maintenance efficiency. A spline-backfitted kernel is used to improve computation efficiency when figuring out model parameters. This prognostics method can be applied to practical railway management decisions.

  • 13.
    Fan, Dongming
    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.
    Cai, Baoping
    College of Mechanical and Electronic Engineering, China University of Petroleum, PR China.
    Liu, Bin
    Department of Management Science, University of Strathclyde, Glasgow G1 1XQ, U.K.
    Robustness of maintenance support service networks: attributes, evaluation and improvement2021Inngår i: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 210, artikkel-id 107526Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Maintenance support service network (MSSN) is used to provide maintenance services and maintain the operational status of equipment. However, the performance of MSSN has been significantly influenced by inevitable disturbance, which makes it vital to maintain its robustness. Existing research on robustness of MSSN mainly focuses on single-layer rather than two-layer network, which imposes constraints on the disturbances and limits its application. To solve these issues, this study develops a two-layer MSSN, consisting of a directed entity-layer and an undirected cyber-layer focusing on supporting maintenance service. A definition of robustness for two-layer MSSN is proposed, and effect propagation models are established to evaluate its robustness of MSSN, followed by its improvement strategies. In particular, two strategies applied in the single-layer MSSN are modified to adapt to the two-layer MSSN, and a novel greedy partnership building approach is proposed to find an optimal strategy under cascading failure, to maintain the robustness of MSSN from a complex network perspective. Finally, numerical examples are presented to illustrate the effectiveness of the proposed approach.

  • 14.
    Fan, Dongming
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China.
    Ren, Yi
    School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China.
    Feng, Qiang
    School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China.
    Liu, Yiliu
    Department of Mechanical and Industrial Engineering, Norwegian University of Science & Technology, Trondheim, Norway.
    Wang, Zili
    School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, PR China.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Restoration of smart grids: Current status, challenges, and opportunities2021Inngår i: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 143, artikkel-id 110909Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Smart grids, which constitute intelligent infrastructure in smart cities, provide reliable, energy-efficient, and high-quality power to improve the standard of living while maintaining a sustainable environment. For self-healing, restoration is considered an effective and intelligent way to ensure that parts of the system remain active and even reconfigure automatically with external failures or attacks. Restoration has recently received considerable attention from researchers and engineers. Regarding the technical route of restoration, this survey reveals some common properties and identifies the research gaps in modeling frameworks, reconfiguration technologies, and optimization approaches. It presents a summary of the challenges and opportunities in restoration of smart grids. It is expected to facilitate the researchers in the advancement of effective and intelligent restoration of smart grids.

  • 15.
    Han, Yong
    et al.
    Nanjing University of Science & Technology.
    Lin, Jing
    Han, Yuqi
    Nanjing University of Science & Technology.
    Demand Risk's Evaluation for Supply Chain of Multinational Rag Trade Company2008Inngår i: Huaiyin Shifan Xueyuan Xuebao (Zhexue Shehui Kexue Ban), ISSN 1007-8444, s. 378-381Artikkel i tidsskrift (Fagfellevurdert)
  • 16.
    He, Zhiyi
    et al.
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Shao, Haidong
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Wang, Ping
    AECC Hunan Aviation Powerplant Research Institute. AECC Key Laboratory of Aero-engine Vibration Technology.
    Lin, Jing (Janet)
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Cheng, Junsheng
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Yang, Yu
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University.
    Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples2020Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 191, artikkel-id 105313Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.

  • 17. Kong, Qinghua
    et al.
    Lin, Jing
    Han, Yuqi
    Evaluation of Multivariate Customer Lifetime Value for the Third Part Logistics Enterprises Based on the Cox Regression Model2005Inngår i: China International Logistics Conference: Conference Proceedings: Fuzhou, China, 19 May-20 May, 2005, 2005, s. 244-249Konferansepaper (Fagfellevurdert)
  • 18.
    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 Bearings2019Inngår i: International Journal of Prognostics and Health Management, E-ISSN 2153-2648, Vol. 10, artikkel-id 014Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 19.
    Li, Xin
    et al.
    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China.
    Li, Yong
    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, PR China.
    Yan, Ke
    Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, 117566, Singapore.
    Shao, Haidong
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, PR China.
    Lin, Janet (Jing)
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna 63220, Sweden.
    Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging2023Inngår i: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 230, artikkel-id 108921Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.

  • 20.
    Lin, Janet
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bayesian Reliability with MCMC: Opportunities and Challenges2016Inngår 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. 575-585Konferansepaper (Fagfellevurdert)
    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.

  • 21.
    Lin, Janet (Jing)
    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.
    Guest editorial2019Inngår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, nr 3, s. 3-3Artikkel i tidsskrift (Annet vitenskapelig)
  • 22.
    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 (Annet vitenskapelig)
    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.

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  • 23. Lin, Jing
    A Bayesian Change Point Analysis of Reliability Data based on the Markov Chain Monte Carlo Aproach2006Inngår i: 5th Annual Hawaii International Conference on Statistics, Mathematics and Related Fields: Conference Proceedings, ISSN 1550-3747, Honolulu, Hawaii, USA, 16 January-18 january 2006, USA, 2006, s. 1134-1140Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Due to the defect in a system or design, some elements of one system may often failure unconventionally, which caused by “early failure”. Usually, we eliminate these obvious extraordinary data before analysis them in reliability trials to reduce interfere. However, this approach was so subjectively. Change point models are usually used to study for the time points, which change suddenly in the system models, and are important in many applications. In this paper, we are focusing on finding out those change points that can disturb the evaluation for the reliability model. We bring forward a Bayesian change point model, which is very popular in clinical trials. By this model, we use the time lagged regression function, which is based on the stochastic process, to do the change point analysis for the system reliability data under two hypothesis, by which we can evaluate the covariates’ influence on the life distribution for the system more correctly. What’s more, in this paper we make use of the Markov chain Monte Carlo (MCMC) approach based on Gibbs sampler to simulate dynamically the Markov Chain of the parameters’ posterior distribution. And also, we give out the parameters’ Bayesian estimation in the condition of the random truncated test. Finally, we utilize the result of the data simulation to prove the objectivity and validity of the model by using the WinBUGS package, and the univeriate example cited here could be extended to multivariate data and also be helpful for the study of “ bathtub curve”.

  • 24. Lin, Jing
    A Two-stage Failure Model for Bayesian Change Point Analysis2008Inngår i: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 57, nr 2, s. 388-393Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a new approach for detecting certain change-points, which may disturb the evaluation of reliability models with covariates, via a two-stage failure model, and stochastic time-lagged regression functions. 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.

    Fulltekst (pdf)
    fulltext
  • 25.
    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 Methods2014Inngår i: Journal of Quality and Reliability Engineering, ISSN 2314-8055, E-ISSN 2314-8047, Vol. 2014, artikkel-id 264920Artikkel i tidsskrift (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    FULLTEXT01
  • 26.
    Lin, Jing
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Bayesian Integrated Reliability Analysis for Locomotive Wheels2013Inngår i: Newsletter of European Safety and Reliability Association, nr June, s. 5-7Artikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
  • 27. Lin, Jing
    Bayesian Survival Analysis Based on MCMC Method and its Application in Reliability2008Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Quality is the topic of the day in the 21st century. As one of the pivotal taches for quality assurance, reliability considerations are playing a particularly important role. With the execution of “Integrated Product and Process Design (IPPD)”, “ method” and “Maintenance Free Operating Period (MFOP)”, reliability considerations today are facing greater challenges than ever. It raises a higher requirement for reliability estimation methods simultaneity because of increased product complexity, increasingly inhospitable environment of operation, as well as the requirements of shortening the manufacturing period and reducing the cost. The goal of this thesis is to combine Bayesian theory and survival analysis with Markov Chain Monte Carlo (MCMC) method, by that the lifetime data in reliability trails will be analyzed.With the background that, nowadays the theory of Bayesian survival analysis was still developing and its application studies at present were mainly limited in clinic trails or biostatistics, firstly, the main logic flow for doing Bayesian reliability analysis with MCMC method was proposed in this thesis by discussing systematically the knowledge such as priors choosing, posterior sampling, convergence diagnosing, MC error, model comparative and the like. And then, with another background that, studies for the moment were mostly utilizing Bayesian analysis or survival analysis separately, the study with MCMC method here were applied to do reliability analysis including parameter models, semi-parameter models, frailty models and other unfamiliar models. What’s more, the reliability trails and the environmental trails in which the trials had been done in wide variety of environments were integrated well.From the study perspective, the advantages both of Bayesian analysis and survival analysis for analyzing lifetime data in reliability trails were emerged coinstantaneous by synthetically applying the two methodologies. From the study approach, the difficulties of the high-dimension numerical integral had been resolved better by making use of MCMC with Gibbs sampler. From the study contents, the theory of reliability analysis had been substantiated which had based upon conjoining survival analysis and reliability theory, especially including the study on frailty factors and other unclassical survival models in reliability analysis. The following innovation works were included in this thesis:(1) The logic framework of doing reliability evaluation using Bayesian survival analysis with MCMC method was proposed.(2) The theory of Bayesian survival analysis was introduced to do analysis for lifetime data in reliability trails by the numbers, which had been showed concretely in: Several model structures were studied with framework for Bayesian analysis using MCMC method.  The semi-parameter methods were utilized to decrease the limits for both model priors and hazard functions. “Cure rate fraction” for systems was introduced to exposure the existent of comparable “longevity” subsystems in some complex systems. The “two-phase hypothesis” for units’ failure was proposed. Through that, the signification of “early failure change point” as well as its contribution for non- monotone hazard rate had been displayed. Frailty models with frailty factors were applied to describe those unknown and unmeasured random effects in reliability trails. The results were showed that: several problems in reliability analysis such as small sample, incomplete data, and complex running environments had been settled well; The theory of reliability analysis for small sample and for complex system was enriched based on Bayesian survival analysis; Also, the effectiveness and maneuverability had been improved by using MCMC method due to its powerful calculate capability.

  • 28.
    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 (Annet vitenskapelig)
    Fulltekst (pdf)
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  • 29.
    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, Sweden2014Inngår i: Newsletter of European Safety and Reliability AssociationArtikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
  • 30. Lin, Jing
    Requirements Predictive Models for Spares toward to Customers2010Inngår i: Chinese Journal of Science and Technology Innovation Herald, ISSN 1674-098X, nr 145, s. 14-15Artikkel i tidsskrift (Fagfellevurdert)
  • 31.
    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 (Annet vitenskapelig)
    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.

    Fulltekst (pdf)
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  • 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.
    Bayesian Semi-parametric Analysis for Locomotive Wheel Degradation using Gamma Frailties2015Inngår 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. 237-247Artikkel i tidsskrift (Fagfellevurdert)
    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

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  • 33.
    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 wearing: [Badania PoróWnawcze Zużycia Powierzchni Bieżnych Kół Lokomotyw DużEj Mocy]2014Inngår i: Eksploatacja i Niezawodność – Maintenance and Reliability, ISSN 1507-2711, E-ISSN 2956-3860, Vol. 16, nr 2, s. 276-287Artikkel i tidsskrift (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    fulltext
  • 34.
    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 Malmbanan2015Inngår i: IHHA 2015 Conference proceedings: 21 – 24 June 2015, Perth, Australia, International Heavy Haul Association , 2015, s. 924-930Konferansepaper (Fagfellevurdert)
    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.

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    FULLTEXT01
  • 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.
    Bayesian parametric analysis for reliability study of locomotive wheels2013Inngår i: Proceedings on the 59th Annual Reliability and Maintainability Symopsium (RAMS 2013), 2013Konferansepaper (Fagfellevurdert)
    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.

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  • 36.
    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 approach2014Inngår i: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 30, nr 5, s. 657-667Artikkel i tidsskrift (Fagfellevurdert)
    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.

    Fulltekst (pdf)
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  • 37.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Cai, Baoping
    College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao, China.
    Wang, Lihui
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Prognostic and health management through collaborative maintenance2021Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 61, s. 712-713Artikkel i tidsskrift (Annet vitenskapelig)
  • 38. Lin, Jing
    et al.
    Chen, Jie
    Nanjing University of Science & Technology.
    Semi-parametric Shared Frailty Model Based on MCMC Method and its Application in Reliability2008Inngår i: Chinese Journal of Electronic Product Reliability and Environmental Testing, ISSN 1672-5468, Vol. 26, nr 153, s. 51-57Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To mitigate the limitation of the traditional hypothesis that the lifetimes of the individuals obey the independent identically distribution, the semi-parametric shared frailty model based on the developmental proportional hazards model with frailty was constructed, which can reflect the influence caused by the random effect . In this model, the baseline hazard was supposed to be piecewise constant hazard. Then the MCMC method based on Gibbs sampling to simulate dynamically the Markov Chain of the parameters’ posterior distribution was brought forward. By that the parameters’ Bayesian estimations under the condition of the random truncated test and the prior distribution of the frailty belongs to the Gamma distribution were given out. Also the martingale process of the baseline hazard was introduced , as well as the precision of the numeration was improved. What’s more, an application of these techniques was illustrated by an example.

  • 39. Lin, Jing
    et al.
    Chen, Jie
    Nanjing University of Science & Technology.
    Han, Yuqi
    Nanjing University of Science & Technology.
    Bayesian analysis of constant stress AFT for Weibull distribution using Gibbs sampling2007Inngår i: 2007 IEEE International Conference on Grey Systems and Intelligent Services: 2007 IEEE GSIS: Nanjing, China, 18 November-20 Novermber, 2007, Piscataway, NJ: IEEE Communications Society, 2007, s. 1492-1496Konferansepaper (Fagfellevurdert)
    Abstract [en]

    To mitigate the limitations of the traditional methods for reliability analysis such as BLUE, the model of Weibull constant-stress accelerated failure test (AFT)is considered here. Based on the theory of Bayesian survival analysis and informative prior hypothesis for model’s parameters, Markov chain Monte Carlo(MCMC) method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameters’ posterior distribution, under the conditionthat the lifetime’s distribution is Weibull. Through that, the parameters’ Bayesian estimations in the constant-stress AFT model were given. BUGS package is used here as an example. The example data is first used by Shisong Mao (2003). And, two result sets are presented based on two assumptions, one of which is that the truncated data in the AFT is viewed as the right censored data in survival analysis. The other assumption is that the truncated data in the AFT is viewed as just failure data but not truncated. The two data sets are used to be compared with the results provided by Mao using BLUE method. The results indicate that, the estimation given by BLUE is close to the results given under the assumptionthat the truncated data is just failure data. However, when we consider the truncated data, the results far differ from the former. The limitations of BLUE will be presented once more, and the significance and the effectiveness of the model will be illustrated.

  • 40. Lin, Jing
    et al.
    Dong, Liang
    SKF.
    Zhang, Liangwei
    SKF.
    Plan for Spares Management and its Application with PDCA process2010Inngår i: China Plant Engineering, ISSN 1671-0711, Vol. 264, nr 1, s. 25-27Artikkel i tidsskrift (Fagfellevurdert)
  • 41.
    Lin, Jing
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Division of Product Realization, Mälardalen University, 63220, Eskilstuna, Sweden.
    Fan, Dongming
    School of Transportation Science and Engineering, Beihang University, Beijing, China.
    Management of Aging Assets: Overview, Challenges, and Opportunities2024Inngår i: Frontiers of Performability Engineering / [ed] Durga Rao Karanki, Springer Nature, 2024, 1, s. 175-194Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 42.
    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 industry2011Inngår 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. 36-40Konferansepaper (Fagfellevurdert)
    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

  • 43.
    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 system2012Inngår i: 2012 IEEE International Technology Management Conference: Dallas,USA, 24 June - 27 June, 2012, Piscataway, MJ: IEEE Communications Society, 2012, s. 36-44Konferansepaper (Fagfellevurdert)
    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.

  • 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.
    Maintenance spares inventory management: performance measurement using a HOMM2011Inngår i: MPMM 2011: Maintenance Performance Measurement & Management: Conference Proceedings / [ed] Diego Galar; Aditya Parida; Håkan Schunnesson; Uday Kumar, Luleå: Luleå tekniska universitet, 2011, s. 77-83Konferansepaper (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    FULLTEXT01
  • 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.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    House of maintenance management in mining industry2011Inngår 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. 46-51Konferansepaper (Fagfellevurdert)
    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.

  • 46. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    A Bayesian Emendation Model for Claim Frequency Based on MCMC Method2005Inngår i: Journal of Quantitative & Technical Economics, ISSN 1000-3894, Vol. 22, nr 10, s. 92-99Artikkel i tidsskrift (Fagfellevurdert)
  • 47. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Application of a cure rate model in reliability analysis for masked data2007Inngår i: Chinese Journal of Mathematics in Practice and Theory, ISSN 1000-0984, Vol. 7, s. 69-75Artikkel i tidsskrift (Fagfellevurdert)
  • 48. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Application of Change Point Model Based on the MCMC Method in the Analysis of Reliability Data2006Inngår i: China Journal of mechanical engineering, ISSN 1004-132X, Vol. 17, nr 14, s. 1451-1455Artikkel i tidsskrift (Fagfellevurdert)
  • 49. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Application of Poly-Weibull Regression Model in Analysis with Competing Causes of Failure2006Inngår i: Chinese Journal of system simulation, ISSN 1004-731X, s. 199-202Artikkel i tidsskrift (Fagfellevurdert)
  • 50. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Bayesian analysis for randomly truncated constant-stress accelerated life testing2007Inngår i: Journal of Systems Engineering and Electronics, ISSN 1001-506X, Vol. 29, nr 2, s. 320-323Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aimed at the fault of the traditional numeration methods, the Weibull model, which is used widely in the family of Bayesian accelerated failure-time model was discussed. Markov chain Monte Carlo method based on Gibbs sampling was discussed, which were used to simulate dynamically the Markov Chain of the parameters’ posterior distribution. Also, the parameters’ Bayesian estimations were given out with prior suppose for its parameters. What’s more, the results of the data’s simulation were utilized to show the process of setting the model by using the BUGS package. It proves the objectivity and validity of the model.

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