Stratified statistical monitoring strategy for a multi-product manufacturing facility with early detection approach. (April 2019)

• Record Type: Journal Article
• Authors: Suman, Swarnambuj; Das, Anupam
• Is Part Of: Computers & industrial engineering. Volume 130(2019)
• Page Start: 551
• Page End: 564
• Publication Date: 2019-04
• Subjects: Stratified monitoring -- Multiblock principal component analysis -- Fault diagnosis -- Early detection -- Multi-product monitoring; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.03.018
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Development of a single process representation for monitoring of multiple portfolios of products. Proposing a fault diagnostic statistic, which would encapsulate the contribution of primary as well as associate variables. The development of a MBPCA latent variable based monitoring statistic for early detection of developing faults. Abstract: In this article, an attempt has been made to devise a multi layered or multi strata statistical process monitoring strategy. The stratification of the monitoring strategy implies to the key notion of carrying out model development and diagnosis of the detected fault in a hierarchical or stratified way. The nominal model that has been developed is a three strata or three level process representation based on the more illustrious two level multi-block principal component analysis. The article highlighted the issue of monitoring of multiple product portfolios via a single process representation or model; proposing new fault diagnostic approach encompassing the relative contribution of primary as well as associated characteristics towards the detected fault and lastly the article proposes a monitoring statistic for early detection of developing faults. The monitoring of all the product portfolios via a single unified model was done with the intention of cutting down on the resources mainly time and effort. The proposed diagnostic statistic encapsulated the contribution of associate characteristics where associate characteristics are the characteristics having significant correlation primary characteristics which are the main contributor towards the detected fault. The monitoring statistic used for early detection of the developing fault is a latent variable score based exponential weighted moving average (EWMA) chart. The unified model developed herein, the latent variable score based EWMA statistic proposed and the new fault diagnostic statistic devised were able to perform their functions satisfactorily with good model fit value, correct detection of developing fault and reasonably accurate diagnosis results. The devised monitoring strategy has been validated via a case study pertaining to an integrated steel plant (ISP).