Fatigue crack fault diagnosis and prognosis based on hidden semi‐Markov model. Issue 13 (17th December 2018)

• Record Type: Journal Article
• Authors: Fan, Lei; Wang, Shaoping; Duan, Haibin; Ran, Hongliang
• Is Part Of: Journal of engineering. Volume 2019:Issue 13(2019)
• Page Start: 406
• Page End: 410
• Publication Date: 2018-12-17
• Subjects: fault diagnosis -- gears -- remaining life assessment -- helicopters -- fatigue cracks -- hidden Markov models -- condition monitoring -- exponential distribution; fatigue crack fault diagnosis -- hidden semiMarkov model -- helicopter main gearbox planet carrier -- exponential distribution -- remaining useful life prediction -- prognostic accuracy; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8960
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : Aiming at the fault diagnosis and remaining useful life (RUL) prediction of fatigue cracks of a helicopter main gearbox planet carrier, this article proposes a hidden semi‐Markov model (HSMM) methodology, which introduces the explicit state durational distribution parameters into the traditional hidden Markov model (HMM), thus overcoming the limitation of exponential distribution in HMM, retaining strong pattern recognition and classification ability, and improving the diagnostic and prognostic accuracy, and the effectiveness of the method was verified through experiments.