Remaining useful life estimation based on discriminating shapelet extraction. (October 2015)

• Record Type: Journal Article
• Authors: Malinowski, Simon; Chebel-Morello, Brigitte; Zerhouni, Noureddine
• Is Part Of: Reliability engineering & system safety. Volume 142(2015:Oct.)
• Page Start: 279
• Page End: 288
• Publication Date: 2015-10
• Subjects: Prognostics and health Management -- Pattern extraction -- Remaining useful life -- Data driven technique; 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.2015.05.012
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: In the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task. Various research topics including data acquisition, fusion, diagnostics and prognostics are involved in this domain. This paper presents an approach, based on shapelet extraction, to estimate the RUL of equipment. This approach extracts, in an offline step, discriminative rul-shapelets from an history of run-to-failure data. These rul-shapelets are patterns that are selected for their correlation with the remaining useful life of the equipment. In other words, every selected rul-shapelet conveys its own information about the RUL of the equipment. In an online step, these rul-shapelets are compared to testing units and the ones that match these units are used to estimate their RULs. Therefore, RUL estimation is based on patterns that have been selected for their high correlation with the RUL. This approach is different from classical similarity-based approaches that attempt to match complete testing units (or only late instants of testing units) with training ones to estimate the RUL. The performance of our approach is evaluated on a case study on the remaining useful life estimation of turbofan engines and performance is compared with other similarity-based approaches. Abstract : Highlights: A data-driven RUL estimation technique based on pattern extraction is proposed. Patterns are extracted for their correlation with the RUL. The proposed method shows good performance compared to other techniques.