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  • 1.
    Aitomäki, Yvonne
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Allard, Christina
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Social Sciences.
    Lin, Janet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sandström, Anders
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    LTU Teaching guide to e-learning: how to clear the mist of teaching through the cloud2015Conference paper (Other academic)
  • 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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    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 matching2019In: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, no 3, p. 5-13Article in journal (Refereed)
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lin, Janet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Evaluating the measurement capability of a wheel profile measurement system by using GR&R2016In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 92, p. 19-27Article in journal (Refereed)
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lin, Janet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Assessment of the data quality of wayside wheel profile measurements2016In: International Journal of COMADEM, ISSN 1363-7681, Vol. 19, no 3, p. 19-25Article in journal (Refereed)
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lin, Janet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Enhancing the quality of data from a wheel profile measurement system: a proposed approach2015Conference paper (Refereed)
    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.

  • 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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 37611-37619Article in journal (Refereed)
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Xie, Min
    Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon.
    Bayesian Networks in Fault Diagnosis: some research issues andchallenges2017In: 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, p. 26-32Conference paper (Refereed)
  • 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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. 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 Evaluation2019In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, no 4, p. 2146-2157Article in journal (Refereed)
    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.

  • 9.
    Chen, Jiayu
    et al.
    Beihang University, Beijing, China.
    Zhou, Dong
    Beihang University, Beijing, China.
    Guo, Ziyue
    Beihang University, Beijing, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    LYU, Chuan
    Beihang University, Beijing, China.
    LU, Chen
    Beihang University, Beijing, China.
    An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 9022-9031Article in journal (Refereed)
    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.

  • 10.
    Chi, Zhexiang
    et al.
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    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 train2020In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 149, article id 107022Article in journal (Refereed)
    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.

  • 11.
    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 Company2008In: Huaiyin Shifan Xueyuan Xuebao (Zhexue Shehui Kexue Ban), ISSN 1007-8444, p. 378-381Article in journal (Refereed)
  • 12. 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 Model2005In: China International Logistics Conference: Conference Proceedings: Fuzhou, China, 19 May-20 May, 2005, 2005, p. 244-249Conference paper (Refereed)
  • 13.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment, Eskilstuna, Sweden.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Håkansson, Lars
    Linnaeus University, Växjö, Sweden.
    Karlberg, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings2019In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 10, article id 014Article in journal (Refereed)
    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.

  • 14.
    Lin, Janet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bayesian Reliability with MCMC: Opportunities and Challenges2016In: 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, p. 575-585Conference paper (Refereed)
    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.

  • 15.
    Lin, Janet
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Nordmark, Thomas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Zhang, Liangwei
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data analysis of heavy haul wagon axle loads on Malmbanan line, Sweden: A case study for LKAB2016Report (Other academic)
    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.

  • 16. Lin, Jing
    A Bayesian Change Point Analysis of Reliability Data based on the Markov Chain Monte Carlo Aproach2006In: 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, p. 1134-1140Conference paper (Refereed)
    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”.

  • 17. Lin, Jing
    A Two-stage Failure Model for Bayesian Change Point Analysis2008In: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 57, no 2, p. 388-393Article in journal (Refereed)
    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.

  • 18.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    An Integrated Procedure for Bayesian Reliability Inference using Markov Chain Monte Carlo Methods2014In: Journal of Quality and Reliability Engineering, ISSN 2314-8055, E-ISSN 2314-8047, Vol. 2014, article id 264920Article in journal (Refereed)
    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.

  • 19.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bayesian Integrated Reliability Analysis for Locomotive Wheels2013In: Newsletter of European Safety and Reliability Association, no June, p. 5-7Article in journal (Other (popular science, discussion, etc.))
  • 20. Lin, Jing
    Bayesian Survival Analysis Based on MCMC Method and its Application in Reliability2008Doctoral thesis, comprehensive summary (Other academic)
    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.

  • 21.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data Analysis of Heavy Haul Locomotive Wheel-sets’ Running Surface Wear at Malmbanan2014Report (Other academic)
  • 22.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data analysis of heavy haul locomotive wheel-sets’ running surface wear at Malmbanan, Sweden2014In: Newsletter of European Safety and Reliability AssociationArticle in journal (Other (popular science, discussion, etc.))
  • 23. Lin, Jing
    Requirements Predictive Models for Spares toward to Customers2010In: Chinese Journal of Science and Technology Innovation Herald, ISSN 1674-098X, no 145, p. 14-15Article in journal (Refereed)
  • 24.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Using integrated reliability analysis to optimise maintenance strategies: a Bayesian integrated reliability analysis of locomotive wheels2013Report (Other academic)
    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.

  • 25.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bayesian Semi-parametric Analysis for Locomotive Wheel Degradation using Gamma Frailties2015In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 229, no 3, p. 237-247Article in journal (Refereed)
    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 University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Comparison study of heavy haul locomotive wheels' running surfaces wearing2014In: Eksploatacja i Niezawodnosc, ISSN 1507-2711, Vol. 16, no 2, p. 276-287Article in journal (Refereed)
    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.

  • 27.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Nordmark, Thomas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data analysis of wheel-sets' running surface wear based on re-profiling measurement: a case study at Malmbanan2015In: IHHA 2015 Conference proceedings: 21 – 24 June 2015, Perth, Australia, International Heavy Haul Association , 2015, p. 924-930Conference paper (Refereed)
    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.

  • 28.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bayesian parametric analysis for reliability study of locomotive wheels2013In: Proceedings on the 59th Annual Reliability and Maintainability Symopsium (RAMS 2013), 2013Conference paper (Refereed)
    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.

  • 29.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Asplund, Matthias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Parida, Aditya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Reliability analysis for degradation of locomotive wheels using parametric Bayesian approach2014In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 30, no 5, p. 657-667Article in journal (Refereed)
    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.

  • 30. Lin, Jing
    et al.
    Chen, Jie
    Nanjing University of Science & Technology.
    Semi-parametric Shared Frailty Model Based on MCMC Method and its Application in Reliability2008In: Chinese Journal of Electronic Product Reliability and Environmental Testing, ISSN 1672-5468, Vol. 26, no 153, p. 51-57Article in journal (Refereed)
    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.

  • 31. 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 sampling2007In: 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, p. 1492-1496Conference paper (Refereed)
    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.

  • 32. Lin, Jing
    et al.
    Dong, Liang
    SKF.
    Zhang, Liangwei
    SKF.
    Plan for Spares Management and its Application with PDCA process2010In: China Plant Engineering, ISSN 1671-0711, Vol. 264, no 1, p. 25-27Article in journal (Refereed)
  • 33.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    A step-by-step model to improve delivery assurance: a case study in mining industry2011In: 2011 IEEE International Conference on Quality and Reliability: ICQR 2011, Bangkok 14 September 2011 -17 September 2011, Piscataway, NJ: IEEE Communications Society, 2011, p. 36-40Conference paper (Refereed)
    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

  • 34.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Information sharing in a spares demand system2012In: 2012 IEEE International Technology Management Conference: Dallas,USA, 24 June - 27 June, 2012, Piscataway, MJ: IEEE Communications Society, 2012, p. 36-44Conference paper (Refereed)
    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.

  • 35.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Maintenance spares inventory management: performance measurement using a HOMM2011In: MPMM 2011: Maintenance Performance Measurement & Management: Conference Proceedings / [ed] Diego Galar; Aditya Parida; Håkan Schunnesson; Uday Kumar, Luleå: Luleå tekniska universitet, 2011, p. 77-83Conference paper (Refereed)
    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.

  • 36.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    House of maintenance management in mining industry2011In: 2011 IEEE International Conference on Quality and Reliability: ICQR 2011: Bamgkok, 14 September 2011 - 17 September 2011, Piscataway, NJ: IEEE Communications Society, 2011, p. 46-51Conference paper (Refereed)
    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.

  • 37. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    A Bayesian Emendation Model for Claim Frequency Based on MCMC Method2005In: Journal of Quantitative & Technical Economics, ISSN 1000-3894, Vol. 22, no 10, p. 92-99Article in journal (Refereed)
  • 38. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Application of a cure rate model in reliability analysis for masked data2007In: Chinese Journal of Mathematics in Practice and Theory, ISSN 1000-0984, p. 69-75Article in journal (Refereed)
  • 39. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Application of Change Point Model Based on the MCMC Method in the Analysis of Reliability Data2006In: China Journal of mechanical engineering, ISSN 1004-132X, Vol. 17, no 14, p. 1451-1455Article in journal (Refereed)
  • 40. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Application of Poly-Weibull Regression Model in Analysis with Competing Causes of Failure2006In: Chinese Journal of system simulation, ISSN 1004-731X, p. 199-202Article in journal (Refereed)
  • 41. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Bayesian analysis for randomly truncated constant-stress accelerated life testing2007In: Journal of Systems Engineering and Electronics, ISSN 1001-506X, Vol. 29, no 2, p. 320-323Article in journal (Refereed)
    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.

  • 42. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Bayesian Credibility Model for Experience Rating based on MCMC Method2006In: Chinese Journal of management Science, ISSN 1003-207X, Vol. 14, no 2, p. 33-38Article in journal (Refereed)
  • 43. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Evaluation for Weibull constant-stress accelerated failure test with MCMC method2007In: Journal of Statistics and Decision, ISSN 1002-6487, p. 70-71Article in journal (Refereed)
  • 44. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Exponential Regression Model Based on MCMC Method and its Application2005In: Journal of Operations Research & Management Science, ISSN 1007-3221, Vol. 14, no 4, p. 95-100Article in journal (Refereed)
  • 45. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Gamma Process Priors Model Based on MCMC Simulation and Its Application in Reliability2007In: Chinese Journal of system simulation, ISSN 1004-731X, Vol. 19, no 22, p. 1099-1103Article in journal (Refereed)
  • 46. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Weibull Shared Frailty Model Based on MCMC Method and its Application in Reliability2006In: China Journal of Engineering Science, ISSN 1009-1742, p. 55-60Article in journal (Refereed)
  • 47. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Chen, Jie
    Nanjing University of Science & Technology.
    Weibull Regression Model Based on MCMC Method and Its Application in Reliability2006In: Chinese Journal of system simulation, ISSN 1004-731X, p. 1161-1164Article in journal (Refereed)
  • 48. Lin, Jing
    et al.
    Han, Yuqi
    Zhu, Huiming
    Wang, Ye
    Reliability Models of Ammunition Storage Based on MCMC Simulation Method2007In: Journal of ACTA Armamentar, ISSN 1000-1093, Vol. 28, no 3, p. 315-318Article in journal (Refereed)
  • 49.
    Lin, Jing (Janet)
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    IN2CLOUD: A novel concept for collaborative management of big railway data2017In: Frontiers of Engineering Management, ISSN 2095-7513, Vol. 4, no 4, p. 428-436Article in journal (Refereed)
    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.

  • 50.
    Lin, Jing
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Spares demand system with consideration of integration management and optimization2011In: 2011 International Conference on Mechanical, Industrial, and Manufacturing Engineering: MIME 2011, Melbourne, Australia, 15 January-16 January 2011, 2011, p. 1-4Conference paper (Refereed)
    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.

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