Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 15) Show all publications
Liu, B., Lin, J., Zhang, L. & Kumar, U. (2019). A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation. IEEE Access, 7, 94941-94943, Article ID 8762155.
Open this publication in new window or tab >>A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 94941-94943, article id 8762155Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Aging and degradation process, dynamic maintenance strategy, locomotive wheel-sets, prescriptive maintenance, sequential schedule
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75614 (URN)10.1109/ACCESS.2019.2928587 (DOI)000478676600061 ()2-s2.0-85070235312 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-08-20 (svasva)

Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-28Bibliographically approved
Saari, E., Lin, J., Liu, B., Zhang, L. & Karim, R. (2019). Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data. International Journal of Performability Engineering, 15(5), 1314-1325
Open this publication in new window or tab >>Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data
Show others...
2019 (English)In: International Journal of Performability Engineering, ISSN 0973-1318, Vol. 15, no 5, p. 1314-1325Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Totem Publisher, Inc., 2019
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75095 (URN)10.23940/ijpe.19.05.p7.13141325 (DOI)2-s2.0-85067024398 (Scopus ID)
Note

Validerad;2019;Nivå 1;2019-06-27 (johcin)

Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-06-27Bibliographically approved
Saari, E., Lin, J., Zhang, L. & Liu, B. (2019). System availability assessment using a parametric Bayesian approach: a case study of balling drums. International Journal of Systems Assurance Engineering and Management, 10(4), 739-745
Open this publication in new window or tab >>System availability assessment using a parametric Bayesian approach: a case study of balling drums
2019 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 10, no 4, p. 739-745Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Asset management, System availability, Reliability, Maintainability, Bayesian statistics, Markov Chain Monte Carlo (MCMC), Mining industry
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75363 (URN)10.1007/s13198-019-00803-y (DOI)000489742800023 ()2-s2.0-85068994430 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-10-29 (johcin)

Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2019-10-29Bibliographically 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
Famurewa, S. M., Zhang, L. & Asplund, M. (2017). Data analytics for condition based wheel maintenance. In: : . Paper presented at 11th International Heavy Haul Association Conference, Cape Town, South Africa. 2–6 September 2017. Cape Town, South Africa
Open this publication in new window or tab >>Data analytics for condition based wheel maintenance
2017 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Cape Town, South Africa: , 2017
Keywords
maintenance wheel health analytics monitoring system classification scheme
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Sustainable transportation (AERI); Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65938 (URN)
Conference
11th International Heavy Haul Association Conference, Cape Town, South Africa. 2–6 September 2017
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-06-25Bibliographically approved
Famurewa, S. M., Zhang, L. & Asplund, M. (2017). Maintenance analytics for railway infrastructure decision support. Journal of Quality in Maintenance Engineering, 23(3), 310-325
Open this publication in new window or tab >>Maintenance analytics for railway infrastructure decision support
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 310-325Article in journal (Refereed) Published
Abstract [en]

Purpose

This purpose of this article is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The article also presents principal component analysis (PCA) and local outlier factor (LOF) methods for detecting anomalous rail wear occurrences using field measurement data.

Design/methodology/approach

The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructure maintenance. The diagnotics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for 9 sharp curves on the heavy haul line in Sweden.

Findings

The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T2 and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations.

Practical implications

A practical implication of this article is that the framework and the diagnostic tool can be considered as an integral part of eMaintenance solution. It can be easily adapted as online or onboard maintenance analytic tool with data from automated vehicle based measurement system.

Originality/value

This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2017
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65099 (URN)10.1108/JQME-11-2016-0059 (DOI)2-s2.0-85027972757 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-28 (andbra)

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2018-06-25Bibliographically 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
Zhang, L. (2016). Big Data Analytics for Fault Detection and its Application in Maintenance. (Doctoral dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Big Data Analytics for Fault Detection and its Application in Maintenance
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Big Data Analytics för Feldetektering och Applicering inom Underhåll
Abstract [en]

Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems and reduce the risk of unplanned breakdowns.

Complexities in the data, including high dimensionality, fast-flowing data streams, and high nonlinearity, impose stringent challenges on fault detection applications. From the data modelling perspective, high dimensionality may cause the notorious “curse of dimensionality” and lead to deterioration in the accuracy of fault detection algorithms. Fast-flowing data streams require algorithms to give real-time or near real-time responses upon the arrival of new samples. High nonlinearity requires fault detection approaches to have sufficiently expressive power and to avoid overfitting or underfitting problems.

Most existing fault detection approaches work in relatively low-dimensional spaces. Theoretical studies on high-dimensional fault detection mainly focus on detecting anomalies on subspace projections. However, these models are either arbitrary in selecting subspaces or computationally intensive. To meet the requirements of fast-flowing data streams, several strategies have been proposed to adapt existing models to an online mode to make them applicable in stream data mining. But few studies have simultaneously tackled the challenges associated with high dimensionality and data streams. Existing nonlinear fault detection approaches cannot provide satisfactory performance in terms of smoothness, effectiveness, robustness and interpretability. New approaches are needed to address this issue.

This research develops an Angle-based Subspace Anomaly Detection (ABSAD) approach to fault detection in high-dimensional data. The efficacy of the approach is demonstrated in analytical studies and numerical illustrations. Based on the sliding window strategy, the approach is extended to an online mode to detect faults in high-dimensional data streams. Experiments on synthetic datasets show the online extension can adapt to the time-varying behaviour of the monitored system and, hence, is applicable to dynamic fault detection. To deal with highly nonlinear data, the research proposes an Adaptive Kernel Density-based (Adaptive-KD) anomaly detection approach. Numerical illustrations show the approach’s superiority in terms of smoothness, effectiveness and robustness.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2016. p. 72
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Big Data analytics, eMaintenance, fault detection, high-dimensional data, stream data mining, nonlinear data
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-60423 (URN)978-91-7583-769-7 (ISBN)978-91-7583-770-3 (ISBN)
Public defence
2017-01-27, F1031, Luleå University of Technology, 971 87, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2016-11-15 Created: 2016-11-15 Last updated: 2018-08-17Bibliographically approved
Lin, J., Nordmark, T. & Zhang, L. (2016). Data analysis of heavy haul wagon axle loads on Malmbanan line, Sweden: A case study for LKAB (ed.). Paper presented at . Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Data analysis of heavy haul wagon axle loads on Malmbanan line, Sweden: A case study for LKAB
2016 (English)Report (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.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2016
Series
Technical report / Luleå University of Technology, ISSN 1402-1536
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-24113 (URN)9bbc981e-b5b8-440c-8399-56486fadd26f (Local ID)978-91-7583-593-8 (ISBN)978-91-7583-594-5 (ISBN)9bbc981e-b5b8-440c-8399-56486fadd26f (Archive number)9bbc981e-b5b8-440c-8399-56486fadd26f (OAI)
Note

Godkänd; 2016; 20160502 (linjan)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Zhang, L., Lin, J. & Karim, R. (2015). An Angle-based Subspace Anomaly Detection Approach to High-dimensional Data: With an Application to Industrial Fault Detection (ed.). Paper presented at . Reliability Engineering & System Safety, 142, 482-497
Open this publication in new window or tab >>An Angle-based Subspace Anomaly Detection Approach to High-dimensional Data: With an Application to Industrial Fault Detection
2015 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 142, p. 482-497Article in journal (Refereed) Published
Abstract [en]

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

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-13556 (URN)10.1016/j.ress.2015.05.025 (DOI)000359172400045 ()2-s2.0-84936765529 (Scopus ID)ccb88d21-0f57-4412-9035-a6b9f78de9c7 (Local ID)ccb88d21-0f57-4412-9035-a6b9f78de9c7 (Archive number)ccb88d21-0f57-4412-9035-a6b9f78de9c7 (OAI)
Note
Validerad; 2015; Nivå 2; 20150531 (liazha)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7310-5717

Search in DiVA

Show all publications