Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance

• Published in: Mechanical systems and signal processing Vol. 28; pp. 585 - 596
• Main Authors: Sun, Jianzhong, Zuo, Hongfu, Wang, Wenbin, Pecht, Michael G.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.04.2012; Elsevier
• Subjects: Applied sciences; Computer simulation; Degradation; Exact sciences and technology; Failure; Fracture mechanics (crack, fatigue, damage...); Fundamental areas of phenomenology (including applications); Gas turbines; Health; Health index; Industrial metrology. Testing; Maintenance; Markov processes; Mathematical models; Mechanical engineering. Machine design; Monte Carlo methods; Performance degradation; Physics; Prognostics; Sequential Monte Carlo; Solid mechanics; State space model; Structural and continuum mechanics; Markov processes; State space model; Sequential Monte Carlo; Health index; Performance degradation; Prognostics; State space; Modeling; Uncertain system; Model matching; Sequential method; State estimation; Bayes estimation; Monte Carlo method; Linear regression; Measurement sensor; Rupture; Durability; Regression analysis; State space method; Forecasting; Preventive maintenance; Break up time; Sheet moulding compound; Gas turbine; Reliability

This paper presents the application of a state space model (SSM) for prognostics of an engineering system subject to degradation. A health index (HI) is inferred from a set of sensor signals to characterize the hidden health state of the system. Bayesian state estimation and prediction formulas, on...