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

• Published in: Reliability engineering & system safety Vol. 222; p. 108405
• Main Authors: Azar, Kamyar, Hajiakhondi-Meybodi, Zohreh, Naderkhani, Farnoosh
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.06.2022; Elsevier BV
• Subjects: Aircraft engines; Artificial intelligence; Clustering; Condition monitoring; Condition-based maintenance; Decision support systems; Fault diagnosis; Hybrid systems; Machine learning; Maintenance; Maintenance decision support system; Manufacturing; Mathematical models; Optimization; Prognostic and health management; Proportional hazard model; Reliability engineering; Smart sensors; Statistical analysis; Statistical models; Prognostic and health management; Condition-based maintenance; Maintenance decision support system; Machine learning; Proportional hazard model
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2022.108405

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. •A novel hybrid Maintenance Decision Support System (MDSS) is proposed.•The MDSS framework is designed by coupling Machine Learning (ML) and statistical models.•Semi-supervised learning along with RL are applied for optimizing CBM policy.