IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023000960.html
   My bibliography  Save this article

A prognostic driven predictive maintenance framework based on Bayesian deep learning

Author

Listed:
  • Zhuang, Liangliang
  • Xu, Ancha
  • Wang, Xiao-Lin

Abstract

Recent years have witnessed prominent advances in predictive maintenance (PdM) for complex industrial systems. However, the existing PdM literature predominately separates two inter-related stages—prognostics and maintenance decision making—and either studies remaining useful life (RUL) prognostics without considering maintenance issues or optimizes maintenance plans based on given/assumed prognostic information. In this paper, we propose a prognostic driven dynamic PdM framework by integrating the two stages. In the prognostic stage, we characterize the latent structure between degradation features and RULs through a Bayesian deep learning model. By doing so, the framework is capable of generating a predictive RUL distribution that can well describe prognostic uncertainties. In the maintenance decision-making stage, we dynamically update maintenance and spare-part ordering decisions with the latest predictive RUL information, while satisfying operational constraints. The advantage of the proposed PdM framework is validated by comparison with several benchmark polices, based on the famous C-MAPSS turbofan engine data set.

Suggested Citation

  • Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000960
    DOI: 10.1016/j.ress.2023.109181
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000960
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109181?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    2. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Listou Ellefsen, André & Bjørlykhaug, Emil & Æsøy, Vilmar & Ushakov, Sergey & Zhang, Houxiang, 2019. "Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 240-251.
    4. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    5. Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Xu, Huyang & Fard, Nasser & Fang, Yuanchen, 2020. "Time series chain graph for modeling reliability covariates in degradation process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    8. Huynh, K.T., 2021. "An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Wang, Jiantai & Longyan, Tan & Ma, Xiaobing & Gao, Kaiye & Jia, Heping & Yang, Li, 2023. "Prognosis-driven reliability analysis and replacement policy optimization for two-phase continuous degradation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    14. Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    15. Lee, Juseong & Mitici, Mihaela, 2023. "Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. de Pater, Ingeborg & Reijns, Arthur & Mitici, Mihaela, 2022. "Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    17. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    18. Minhee Kim & Kaibo Liu, 2020. "A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics," IISE Transactions, Taylor & Francis Journals, vol. 53(3), pages 326-340, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Essam A. Ahmed & Mahmoud El-Morshedy & Laila A. Al-Essa & Mohamed S. Eliwa, 2023. "Statistical Inference on the Entropy Measures of Gamma Distribution under Progressive Censoring: EM and MCMC Algorithms," Mathematics, MDPI, vol. 11(10), pages 1-30, May.
    2. Raydonal Ospina & João A. M. Gondim & Víctor Leiva & Cecilia Castro, 2023. "An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil," Mathematics, MDPI, vol. 11(14), pages 1-18, July.
    3. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    4. Keshun, You & Guangqi, Qiu & Yingkui, Gu, 2024. "Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Lijun Shang & Baoliang Liu & Kaiye Gao & Li Yang, 2023. "Random Warranty and Replacement Models Customizing from the Perspective of Heterogeneity," Mathematics, MDPI, vol. 11(15), pages 1-22, July.
    6. Eleftheroglou, Nick & Galanopoulos, Georgios & Loutas, Theodoros, 2024. "Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    8. Juan Bucay-Valdiviezo & Pedro Escudero-Villa & Jenny Paredes-Fierro & Manuel Ayala-Chauvin, 2023. "Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment," Sustainability, MDPI, vol. 15(21), pages 1-13, November.
    9. Haiping Ren & Xue Hu, 2023. "Bayesian Estimations of Shannon Entropy and Rényi Entropy of Inverse Weibull Distribution," Mathematics, MDPI, vol. 11(11), pages 1-16, May.
    10. Amel Abd-El-Monem & Mohamed S. Eliwa & Mahmoud El-Morshedy & Afrah Al-Bossly & Rashad M. EL-Sagheer, 2023. "Statistical Analysis and Theoretical Framework for a Partially Accelerated Life Test Model with Progressive First Failure Censoring Utilizing a Power Hazard Distribution," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    11. den Heijer, Frank & Kok, Matthijs, 2024. "Risk-based portfolio planning of dike reinforcements," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    12. Huda M. Alshanbari & Zubair Ahmad & Hazem Al-Mofleh & Clement Boateng Ampadu & Saima K. Khosa, 2023. "A New Probabilistic Approach: Estimation and Monte Carlo Simulation with Applications to Time-to-Event Data," Mathematics, MDPI, vol. 11(7), pages 1-30, March.
    13. Zhengyang Fan & Wanru Li & Kuo-Chu Chang, 2023. "A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
    14. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Vladimir Rykov & Olga Kochueva & Elvira Zaripova, 2023. "Renewable k -Out-of- n System with the Component-Wise Strategy of Preventive System Maintenance," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    16. Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Nguyen, Khanh T.P. & Medjaher, Kamal & Gogu, Christian, 2022. "Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    8. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Mitici, Mihaela & de Pater, Ingeborg & Barros, Anne & Zeng, Zhiguo, 2023. "Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Lin, Yan-Hui & Chang, Liang & Guan, Lu-Xin, 2024. "Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    11. Xiao, Lei & Tang, Junxuan & Zhang, Xinghui & Bechhoefer, Eric & Ding, Siyi, 2021. "Remaining useful life prediction based on intentional noise injection and feature reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    15. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    17. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    18. Huang, Zhifu & Yang, Yang & Hu, Yawei & Ding, Xiang & Li, Xuanlin & Liu, Yongbin, 2023. "Attention-augmented recalibrated and compensatory network for machine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. González-Muñiz, Ana & Díaz, Ignacio & Cuadrado, Abel A. & García-Pérez, Diego, 2022. "Health indicator for machine condition monitoring built in the latent space of a deep autoencoder," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    20. Zhang, Yuru & Su, Chun & Wu, Jiajun & Liu, Hao & Xie, Mingjiang, 2024. "Trend-augmented and temporal-featured Transformer network with multi-sensor signals for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.