A multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections. (June 2022)

• Record Type: Journal Article
• Authors: Guo, Qingxiu; Liu, Jianchang; Tan, Shubin; Yang, Dongsheng; Li, Yuan; Zhang, Cheng
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 44:Number 9(2022)
• Page Start: 1732
• Page End: 1743
• Publication Date: 2022-06
• Subjects: Variational inference -- Gaussian mixture model -- locality preserving projections -- multimode process -- fault detection; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312211060576
• Languages: English
• ISSNs: 0142-3312

Abstract

For multimode process monitoring, accurate mode information is difficult to be obtained, and each mode is monitored separately, which increases the complexity of the system. This paper proposes a multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections (IVIGMM-LPP). First, the raw data are projected to the feature space where samples still maintain the original neighbor structure. Second, a new discriminant condition is introduced to reduce the influence of the initial category parameter on the iteration results in the VIGMM model. Then, the data are updated utilizing modal information, so that the scales of different modes are adjusted to the same level. Next, the deviation vector is introduced to eliminate the multi-center structure of data. Finally, the statistic is built to monitor the process. IVIGMM-LPP establishes one model for monitoring the premise of knowing the mode information, which reduces the complexity of the monitoring process and improves the fault detection rate. The experimental results of a numerical case and the Tennessee Eastman (TE) process verify the effectiveness of IVIGMM-LPP.