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Publications (10 of 67) Show all publications
Chen, J., Zhou, D., Guo, Z., Lin, J., LYU, C. & LU, C. (2019). An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis. IEEE Access, 7, 9022-9031
Open this publication in new window or tab >>An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 9022-9031Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Active learning, gearbox fault diagnosis, uncertainty and complexity, supervised learning
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-72603 (URN)10.1109/ACCESS.2019.2890979 (DOI)000456910200001 ()2-s2.0-85060708128 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-01-29 (svasva)

Available from: 2019-01-17 Created: 2019-01-17 Last updated: 2019-02-11Bibliographically approved
Cai, B., Kong, X., Liu, Y., Lin, J., Yuan, X., Xu, H. & Ji, R. (2019). Application of Bayesian Networks in Reliability Evaluation. IEEE Transactions on Industrial Informatics, 15(4), 2146-2157
Open this publication in new window or tab >>Application of Bayesian Networks in Reliability Evaluation
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2019 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, no 4, p. 2146-2157Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Software reliability, Hardware, Software, Object oriented modeling, Power system reliability, Reliability engineering
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71739 (URN)10.1109/TII.2018.2858281 (DOI)
Note

Validerad;2019;Nivå 2;2019-04-12 (oliekm)

Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2019-04-24Bibliographically approved
Bo, L., Xu, G., Liu, X. & Lin, J. (2019). Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor. IEEE Access, 7, 37611-37619
Open this publication in new window or tab >>Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 37611-37619Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Texture feature tensor, frequency band entropy, linear support higher-order tensor machine, bearing fault intelligent diagnosis.
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73640 (URN)10.1109/ACCESS.2019.2902344 (DOI)000463639600001 ()
Note

Validerad;2019;Nivå 2;2019-04-15 (svasva)

Available from: 2019-04-14 Created: 2019-04-14 Last updated: 2019-04-17Bibliographically approved
Liu, B., Lin, J., Zhang, L. & Xie, M. (2018). A Dynamic Maintenance Strategy for Prognostics and Health Management of Degrading Systems: Application in Locomotive Wheel-sets. In: : . Paper presented at IEEE 9th International Conference on Prognostics and Health Management (ICPHM), Seattle, WA, USA, 11-13 June 2018.. IEEE
Open this publication in new window or tab >>A Dynamic Maintenance Strategy for Prognostics and Health Management of Degrading Systems: Application in Locomotive Wheel-sets
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71047 (URN)10.1109/ICPHM.2018.8448740 (DOI)978-1-5386-1165-4 (ISBN)
Conference
IEEE 9th International Conference on Prognostics and Health Management (ICPHM), Seattle, WA, USA, 11-13 June 2018.
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-19Bibliographically approved
Zhang, L., Lin, J. & Karim, R. (2018). Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems. Knowledge-Based Systems, 139(1), 50-63
Open this publication in new window or tab >>Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems
2018 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 139, no 1, p. 50-63Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
maintenance modelling, fault detection, unsupervised learning, nonlinear data, kernel density
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-60425 (URN)10.1016/j.knosys.2017.10.009 (DOI)000417773400005 ()2-s2.0-85031504017 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-11-21 (andbra)

Available from: 2016-11-15 Created: 2016-11-15 Last updated: 2017-12-28Bibliographically approved
Cai, B., Huang, L., Lin, J. & Xie, M. (2017). Bayesian Networks in Fault Diagnosis: some research issues andchallenges. In: Diego Galar, Dammika Seneviratne (Ed.), Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden. Paper presented at Maintenance Performance and Measurement and Management 2016(MPMM 2016). November 28, Luleå, Sweden (pp. 26-32). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Bayesian Networks in Fault Diagnosis: some research issues andchallenges
2017 (English)In: 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, Published paper (Refereed)
Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-63486 (URN)978-91-7583-841-0 (ISBN)
Conference
Maintenance Performance and Measurement and Management 2016(MPMM 2016). November 28, Luleå, Sweden
Available from: 2017-05-22 Created: 2017-05-22 Last updated: 2017-11-24Bibliographically approved
Yu, H., Yang, J., Lin, J. (. & Zhao, Y. (2017). Reliability evaluation of non-repairable phased-mission common bus systems with common cause failures. Computers & industrial engineering, 111, 445-457
Open this publication in new window or tab >>Reliability evaluation of non-repairable phased-mission common bus systems with common cause failures
2017 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 111, p. 445-457Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65001 (URN)10.1016/j.cie.2017.08.002 (DOI)000410468600036 ()2-s2.0-85027888677 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-16 (rokbeg)

Available from: 2017-08-10 Created: 2017-08-10 Last updated: 2018-07-10Bibliographically approved
Zhang, L., Lin, J. & Karim, R. (2017). Sliding Window-based Fault Detection from High-dimensional Data Streams (ed.). IEEE Transactions on Systems, Man & Cybernetics. Systems, 47(2), 289-303, Article ID 7509594.
Open this publication in new window or tab >>Sliding Window-based Fault Detection from High-dimensional Data Streams
2017 (English)In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, Vol. 47, no 2, p. 289-303, article id 7509594Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-12680 (URN)10.1109/TSMC.2016.2585566 (DOI)000396231100008 ()2-s2.0-85009962444 (Scopus ID)bd869fb2-6668-49d9-9511-535c94003f4f (Local ID)bd869fb2-6668-49d9-9511-535c94003f4f (Archive number)bd869fb2-6668-49d9-9511-535c94003f4f (OAI)
Note

Validerad; 2017; Nivå 2; 2017-01-23 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-09-14Bibliographically approved
Asplund, M., Lin, J. & Rantatalo, M. (2016). Assessment of the data quality of wayside wheel profile measurements (ed.). International Journal of COMADEM, 19(3), 19-25
Open this publication in new window or tab >>Assessment of the data quality of wayside wheel profile measurements
2016 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 19, no 3, p. 19-25Article in journal (Refereed) Published
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.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-10887 (URN)2-s2.0-84987680545 (Scopus ID)9c3cbb22-ce80-4a35-8f12-e4f456f6a51b (Local ID)9c3cbb22-ce80-4a35-8f12-e4f456f6a51b (Archive number)9c3cbb22-ce80-4a35-8f12-e4f456f6a51b (OAI)
Note

Validerad; 2016; Nivå 1; 2016-10-26 (inah)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Lin, J. (2016). Bayesian Reliability with MCMC: Opportunities and Challenges (ed.). In: (Ed.), Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde (Ed.), Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective. Paper presented at International Conference ICRESH-ARMS 2015 : 01/06/2015 - 04/06/2015 (pp. 575-585). Encyclopedia of Global Archaeology/Springer Verlag
Open this publication in new window or tab >>Bayesian Reliability with MCMC: Opportunities and Challenges
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Encyclopedia of Global Archaeology/Springer Verlag, 2016
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-39619 (URN)10.1007/978-3-319-23597-4_41 (DOI)2-s2.0-85043794030 (Scopus ID)e7259d28-6d42-4c73-b563-f373b27ea506 (Local ID)978-3-319-23596-7 (ISBN)978-3-319-23597-4 (ISBN)e7259d28-6d42-4c73-b563-f373b27ea506 (Archive number)e7259d28-6d42-4c73-b563-f373b27ea506 (OAI)
Conference
International Conference ICRESH-ARMS 2015 : 01/06/2015 - 04/06/2015
Note

Godkänd; 2016; Bibliografisk uppgift: Containing selected papers from the ICRESH-ARMS 2015 conference in Lulea, Sweden, collected by editors with years of experiences in Reliability and maintenance modeling, risk assessment, and asset management, this work maximizes reader insights into the current trends in Reliability, Availability, Maintainability and Safety (RAMS) and Risk Management. Featuring a comprehensive analysis of the significance of the role of RAMS and Risk Management in the decision making process during the various phases of design, operation, maintenance, asset management and productivity in Industrial domains, these proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for those in the field. ; 20151223 (andbra)

Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-03-29Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-7458-6820

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