Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 72) 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)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: 2019-08-20Bibliographically approved
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
Show others...
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
Show others...
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)000467095500029 ()
Note

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

Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2019-06-17Bibliographically 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
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
Zhang, S., Liu, H., Qiang, J., Gao, H., Galar, D. & Lin, J. (2019). Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem. CMES - Computer Modeling in Engineering & Sciences, 119(2), 373-394
Open this publication in new window or tab >>Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem
Show others...
2019 (English)In: CMES - Computer Modeling in Engineering & Sciences, ISSN 1526-1492, E-ISSN 1526-1506, Vol. 119, no 2, p. 373-394Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Tech Science Press, 2019
Keywords
Contamination source identification, monitoring well optimization, Bayes’ Theorem, information entropy, differential evolution algorithm, Metropolis Hastings algorithm, Latin hypercube sampling
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-74424 (URN)10.32604/cmes.2019.03825 (DOI)000466023600010 ()2-s2.0-85065227402 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-06-12 (johcin)

Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-06-25Bibliographically 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
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 0976-4348Article in journal (Refereed) Epub ahead of print
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)
Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2019-07-25
Källström, E., Lindström, J., Håkansson, L., Karlberg, M. & Lin, J. (2019). Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings. International Journal of Prognostics and Health Management, 10, Article ID 014.
Open this publication in new window or tab >>Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings
Show others...
2019 (English)In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 10, article id 014Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
PHM Society, 2019
Keywords
Automatic Transmission, Adaptive Filtering, Adaptive Line Enhancer, Axle, Bearings, Bayesian Learning, Gearbox, Order Analysis, Order Power Spectrum, Torque Converter and Vibration
National Category
Other Mechanical Engineering Other Civil Engineering Information Systems, Social aspects
Research subject
Computer Aided Design; Operation and Maintenance; Information systems
Identifiers
urn:nbn:se:ltu:diva-68353 (URN)
Note

Validerad;2019;Nivå 1;2019-08-15 (johcin)

Available from: 2018-04-15 Created: 2018-04-15 Last updated: 2019-08-15Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7458-6820

Search in DiVA

Show all publications