Monitoring strategy for a multistage manufacturing facility using K-means clustering technique

Swarnambuj Suman and Anupam Das

International Journal of Industrial and Systems Engineering, 2020, vol. 36, issue 3, 301-315

Abstract: The article proposes a process monitoring strategy for a multistage manufacturing facility. In past, researchers have attempted to develop monitoring strategies for multistage processes using multi-block principal component analysis (MBPCA) which requires comprehensive process knowledge. But in most of the cases, the complete acquaintance with the process knowledge may not be possible. Hence, in such cases, a suitable clustering algorithm-based classification of process parameters may be performed. The current article proposes a methodology articulated via amalgamation of the K-means clustering algorithm and traditional MBPCA for monitoring of a process. K-means clustering algorithm is capable of clubbing the process parameters into relevant blocks without any prior process knowledge. In order to validate the devised strategy, a case study related to a multistage manufacturing facility engaged in production of rail blooms is considered. The outcome of the devised strategy has been compared with the outcome of regular MBPCA-based monitoring strategy which is used when the process knowledge is available.

Keywords: K-means clustering algorithm; MBPCA; multistage manufacturing facility; fault detection; fault diagnosis; principal component analysis; PCA; process monitoring. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:36:y:2020:i:3:p:301-315

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