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Chi, Z., Lin, J., Chen, R. & Huang, S. (2020). Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train. Measurement, 149, Article ID 107022.
Open this publication in new window or tab >>Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train
2020 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 149, article id 107022Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
railway safety, prognostics and health management, mean time to failure, Bayesian methods, polygonization, wheel-sets
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75933 (URN)10.1016/j.measurement.2019.107022 (DOI)000490131400013 ()2-s2.0-85072207003 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-09-23 (johcin)

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-11-06Bibliographically approved
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
Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X. & Wei, M. (2019). A Review on Deep Learning Applications in Prognostics and Health Management. IEEE Access, 7, 162415-162438
Open this publication in new window or tab >>A Review on Deep Learning Applications in Prognostics and Health Management
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 162415-162438Article in journal (Refereed) Published
Abstract [en]

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Condition-based maintenance, deep learning, fault detection, fault diagnosis, prognosis
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76709 (URN)10.1109/ACCESS.2019.2950985 (DOI)
Note

Validerad;2019;Nivå 2;2019-11-25 (johcin)

Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-11-25Bibliographically 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
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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
An, B., Gao, L., Xin, T. & Lin, J. (2019). 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 matching. International Journal of COMADEM, 22(3), 5-13
Open this publication in new window or tab >>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 matching
2019 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, no 3, p. 5-13Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
COMADEM International, 2019
Keywords
High-speed railway, Wheel-rail profile matching, Flexible track, Vehicle-track coupling interaction
National Category
Infrastructure Engineering Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75757 (URN)
Note

Validerad;2019;Nivå 1;2019-09-03 (johcin)

Available from: 2019-08-29 Created: 2019-08-29 Last updated: 2019-09-03Bibliographically 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)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
Zhang, B., Liu, X. & Lin, J. (2019). Identification of span of Multi-span Simply Supported Girders by the Longitudinal Level Waveforms. International Journal of COMADEM, 22(3), 19-22
Open this publication in new window or tab >>Identification of span of Multi-span Simply Supported Girders by the Longitudinal Level Waveforms
2019 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 22, no 3, p. 19-22Article in journal (Refereed) Published
Abstract [en]

In high-speed railways, the periodic component of longitudinal level on the top surface of the track can be observed for multi-span simply supported girders. To monitor this component, the paper proposes a method to determine the locating position of simply supported girders using longitudinal level waveforms. The method first applies time-frequency analysis to determine the locating position of simply supported girder bridges in the longitudinal level waveform. Then it adopts a local minimum detection strategy to identify the locating position of the girders. The method is evaluated using the longitudinal level data of a 100km high-speed railway in China; the results show good locating performance.

Place, publisher, year, edition, pages
COMADEM International, 2019
Keywords
Span identification, multi-span simply supported girders, longitudinal level waveform, time-frequency analysis
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75758 (URN)
Note

Validerad;2019;Nivå 1;2019-09-03 (johcin)

Available from: 2019-08-29 Created: 2019-08-29 Last updated: 2019-09-03Bibliographically 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
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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
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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
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7458-6820

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