Stratified statistical monitoring strategy for a multi-product manufacturing facility with early detection approach

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).

[1]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[2]  S.J. Qin,et al.  Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis , 2006, IEEE Transactions on Semiconductor Manufacturing.

[3]  Michael J. Piovoso,et al.  On unifying multiblock analysis with application to decentralized process monitoring , 2001 .

[4]  Elsayed A. Elsayed,et al.  Monitoring and control of beta-distributed multistage production processes , 2019 .

[5]  Victor E. Sower,et al.  β Charts for Short Run Statistical Process Control , 1994 .

[6]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[7]  Farid Kadri,et al.  Improved principal component analysis for anomaly detection: Application to an emergency department , 2015, Comput. Ind. Eng..

[8]  Xuefeng Yan,et al.  A Novel Decentralized Process Monitoring Scheme Using a Modified Multiblock PCA Algorithm , 2017, IEEE Transactions on Automation Science and Engineering.

[9]  Abdelmalek Kouadri,et al.  Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme , 2017 .

[10]  Hong Zhou,et al.  Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares , 2010, IEEE Transactions on Industrial Informatics.

[11]  Wojtek J. Krzanowski,et al.  Principal Component Analysis in the Presence of Group Structure , 1984 .

[12]  Massimo Pacella,et al.  Unsupervised classification of multichannel profile data using PCA: An application to an emission control system , 2018, Comput. Ind. Eng..

[13]  Anupam Das,et al.  Monitoring of an ingot making facility via principal component analysis and its variants , 2019 .

[14]  Hazem Nounou,et al.  Multiscale PLS-based GLRT for fault detection of chemical processes , 2017 .

[15]  Kaixiang Peng,et al.  A New Hierarchical Framework for Detection and Isolation of Multiple Faults in Complex Industrial Processes , 2019, IEEE Access.

[16]  Xuefeng Yan,et al.  Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring , 2015 .

[17]  Sadigh Raissi,et al.  Online monitoring of auto correlated linear profiles via mixed model , 2013, Int. J. Manuf. Technol. Manag..

[18]  H. Shi,et al.  Parallel quality-related dynamic principal component regression method for chemical process monitoring , 2019, Journal of Process Control.

[19]  Erik Vanhatalo,et al.  Multivariate process monitoring of an experimental blast furnace , 2010, Qual. Reliab. Eng. Int..

[20]  B. Flury.  Common Principal Components in k Groups , 1984 .

[21]  ChangKyoo Yoo,et al.  Statistical Process Monitoring with Multivariate Exponentially Weighted Moving Average and Independent Component Analysis , 2003 .

[22]  Jhareswar Maiti,et al.  Process monitoring strategy for a steel making shop: a partial least squares regression-based approach , 2008 .

[23]  Chung-I Li,et al.  Monitoring the Process Quality for Multistage Systems with Multiple Characteristics , 2018 .

[24]  Xuefeng Yan,et al.  Plant-wide process monitoring based on mutual information-multiblock principal component analysis. , 2014, ISA transactions.

[25]  Ji-Gui Sun,et al.  Clustering Algorithms Research , 2008 .

[26]  Zhiqiang Ge,et al.  Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach , 2017 .

[27]  Anupam Das,et al.  Framework-based on SQC and DMAIC to monitor die casting process parameters , 2016 .