Time-adaptive support vector data description for nonstationary process monitoring. (February 2018)

• Record Type: Journal Article
• Authors: Lee, Seulki; Kim, Seoung Bum
• Is Part Of: Engineering applications of artificial intelligence. Volume 68(2017:Aug.)
• Page Start: 18
• Page End: 31
• Publication Date: 2018-02
• Subjects: Multivariate control chart -- Support vector data description -- Time-varying process -- Process control -- Machine learning -- Nonstationary process; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.10.016
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing. Highlights: We propose a time-adaptive support vector data description-based control chart. Proposed chart can adaptively monitor the time-varying and nonnormal processes. Proposed chart traces the natural changes, yet detects real faults effectively. Simulation and real-case data demonstrate the efficiency of the proposed chart.