A holistic multi-failure mode prognosis approach for complex equipment. (December 2018)

• Record Type: Journal Article
• Authors: Blancke, Olivier; Tahan, Antoine; Komljenovic, Dragan; Amyot, Normand; Lévesque, Mélanie; Hudon, Claude
• Is Part Of: Reliability engineering & system safety. Volume 180(2018)
• Page Start: 136
• Page End: 151
• Publication Date: 2018-12
• Subjects: System-level prognostics -- Diagnostics -- Failure mechanisms -- Multi-state systems -- Complex systems -- Expert elicitation -- Hydrogenerator; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.07.006
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: The approach takes into account the complexity of failure mechanisms as a system and integrates both expert knowledge and diagnostic information. The diagnostic algorithm enables to detect active failure mechanisms and track their progression based on diagnostic information from different sources. The prognostic algorithm enables to predict the occurrence of failure modes dynamically as new information becomes available. The simplicity of the algorithms and graphical representation of the results helps to build decision-makers' trust. A case study on hydroelectric generator stator is proposed. Abstract: The aim of this paper is to propose a holistic multi-failure mode prognosis approach that takes into account the complexity of failure mechanisms as a system. Model assumptions are first proposed by experts and then formalized using graph theory and stochastic models. The prognosis approach relies on a diagnostic algorithm that combines diagnostic information from different sources (e.g., measurements and inspections) to detect active failure mechanisms and track their progression, and a prognostic algorithm that predicts failure mode occurrences dynamically as new information becomes available. Furthermore, the approach identifies undetectable failure mechanisms where no symptoms have yet been measured. The relative simplicity of the algorithms and graphical representation of the results helps to build decision-makers' trust. In addition, the approach is a means of capturing acquired knowledge and available data. A case study of a hydroelectric generator stator is proposed. The resulting multi-state degradation model identified more than 150 failure mechanisms discretized in 70 physical states and leading to three failure modes. Three historical failure and one online case studies are presented, based on diagnostic data from Hydro-Québec's generating fleet. In two of the case studies, the failure mode occurrence could have been predicted more than eight years in advance.