A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing. (May 2018)

• Record Type: Journal Article
• Authors: Susto, Gian Antonio; Schirru, Andrea; Pampuri, Simone; Beghi, Alessandro; De Nicolao, Giuseppe
• Is Part Of: Control engineering practice. Volume 74(2018)
• Page Start: 84
• Page End: 94
• Publication Date: 2018-05
• Subjects: Decision support system -- Gamma distribution -- Industry 4.0 -- Monte Carlo methods -- Particle filters -- Predictive maintenance -- Prognostic and health management -- Semiconductor manufacturing; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2018.02.011
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: In the context of Smart Monitoring and Fault Detection and Isolation in industrial systems, the aim of Predictive Maintenance technologies is to predict the happening of process or equipment faults. In order for a Predictive Maintenance technology to be effective, its predictions have to be both accurate and timely for taking strategic decisions on maintenance scheduling, in a cost-minimization perspective. A number of Predictive Maintenance technologies are based on the use of "health factors", quantitative indicators associated with the equipment wear that exhibit a monotone evolution. In real industrial environment, such indicators are usually affected by measurement noise and non-uniform sampling time. In this work we present a methodology, formulated as a stochastic filtering problem, to optimally predict the evolution of the aforementioned health factors based on noisy and irregularly sampled observations. In particular, a hidden Gamma process model is proposed to capture the nonnegativity and nonnegativity of the derivative of the health factor. As such filtering problem is not amenable to a closed form solution, a numerical Monte Carlo approach based on particle filtering is here employed. An adaptive parameter identification procedure is proposed to achieve the best trade-off between promptness and low noise sensitivity. Furthermore, a methodology to identify the risk function associated to the observed equipment based on previous maintenance data is proposed. The present study is motivated and tested on a real industrial Predictive Maintenance problem in semiconductor manufacturing, with reference to a dry etching equipment. Highlights: A filtering and prediction approach is presented for industrial Health Factor (HF). The hidden gamma process model describes monotonicity and noisy measures in HF. A stochastic filter exploiting Monte Carlo approaches is proposed. Procedures for adaptive parameter identification and failures risks are provided. A real semiconductor manufacturing dry etching problem is tackled.