Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework

Zhou, Taotao; Han, Te; Droguett, Enrique López

doi:10.1016/j.ress.2022.108525

Cited by 119 publications

(24 citation statements)

References 48 publications

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“…Nevertheless, inspired by the seminal work in [68], probabilistic deep-learning models are emerging [69], and early results combining deep-learning with Bayesian modelling approaches have been proposed, e.g. [70,71]. This suggests that the proposed framework may have a broader impact with the incorporation of additional probabilistic prediction methods.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Aizpurua

Stewart²,

McArthur³

et al. 2022

Reliability Engineering & System Safety

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Section: Discussionmentioning

confidence: 99%

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Aizpurua

Stewart²,

McArthur³

et al. 2022

Reliability Engineering & System Safety

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“…is helps avoid overfitting issues and shows its advantages to enhance the trustworthiness of the deep learning-based approaches [26,27]. Generally, these methods can be adopted to estimate various reliability parameters in case of failure data in reliability test.…”

Section: Literature Reviewmentioning

confidence: 99%

Reliability Assessment Model and Simulation of Journal Bearing of Railway Freight Cars Based on Bayesian Method under Small Sample Sizes

Yang

Len

Wang

et al. 2022

Journal of Advanced Transportation

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Generally, the box bearing of railway freight cars has no bearing sample failure data at the end of the time-terminated reliability test. However, it is expensive and has high service reliability requirements. Given a small sample size and zero-failure data, the traditional failure probability calculation formula based on a large sample size and the reliability modeling technique cannot easily assess the reliability of rolling bearings accurately. Considering the applicability of the bearing of railway freight cars, this study integrated the prior information of samples and the simulation test information according to Bayes statistical theory, deduced the mathematical model of cumulative failure probability under failure-free data, calculated the distribution parameters using the least square method, and established the reliability estimation model of rolling bearings on the basis of Weibull distribution. The failure-free simulation data of rolling bearings were produced according to the Monte Carlo simulation, and the reliability of the journal bearing of railway freight cars was simulated and assessed by three methods. Simulation results demonstrate that the proposed reliable Bayes multilayer estimation method could not only meet the design requirements of the ISO 281 rolling bearing standards on that basis of the failure-free data and small sample size of the time-terminated simulation, but also assess the reliability of the rolling bearing of railway freight cars.

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“…e existing fault feature extraction methods, including [20,21], will inevitably produce errors in the process of broadband signal processing. It is difficult to extract the broadband fault feature information of low-speed hub bearing signal from nonstationary strong noise.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

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Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework

Cited by 119 publications

References 48 publications

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study

Reliability Assessment Model and Simulation of Journal Bearing of Railway Freight Cars Based on Bayesian Method under Small Sample Sizes

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

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