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Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study

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  • Azar, Kamyar
  • Hajiakhondi-Meybodi, Zohreh
  • Naderkhani, Farnoosh

Abstract

Recent increased enthusiasm towards Industrial Artificial Intelligence (IAI) coupled with advancements in smart sensor technologies have resulted in simultaneous incorporation of several Condition Monitoring (CM) technologies within manufacturing/industrial sectors. Smart utilization of CM data leads to enhanced safety, reliability and availability of manufacturing systems. Conventional system monitoring techniques, however, cannot efficiently cope with such rich CM information content. In this regard, the paper proposes a novel hybrid Maintenance Decision Support System (MDSS) for fault diagnostic and prognostics considering event-triggered CM data. The proposed MDSS is a hybrid framework designed by coupling Machine Learning (ML)-based models and statistical techniques. More specifically, the MDSS is a time-dependent Proportional Hazard Model (PHM) augmented with semi-supervised ML approaches and Reinforcement Learning (RL) to find an optimal maintenance policy for systems subject to stochastic degradations with focus on cost minimization. The developed hybrid model is capable of inferring and fusing high-volume CM data sources in an adaptive and autonomous fashion to recommend optimal maintenance decisions without human intervention, which is a step-forward contribution in the maintenance context. To evaluate the structure and performance of the proposed model, comprehensive ML-based solutions are developed based on a dataset provided by NASA containing run-to-failure and CM data associated with aircraft engines.

Suggested Citation

  • Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000783
    DOI: 10.1016/j.ress.2022.108405
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    7. Zhang, Yongchao & Ji, J.C. & Ren, Zhaohui & Ni, Qing & Gu, Fengshou & Feng, Ke & Yu, Kun & Ge, Jian & Lei, Zihao & Liu, Zheng, 2023. "Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Sánchez, Luciano & Costa, Nahuel & Couso, Inés, 2023. "Simplified models of remaining useful life based on stochastic orderings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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