High-Dimensional Process Monitoring and Fault Isolation via Variable Selection

Abstract

Both process monitoring and fault isolation are important and challenging tasks for quality control and improvement in high-dimensional processes. Under a practical assumption that not all variables would shift simultaneously, this paper proposes a variable-selection-based multivariate statistical process control (SPC) procedure for process monitoring and fault diagnosis. A forward-selection algorithm is first utilized to screen out potential out-of-control variables; a multivariate control chart is then set up to monitor suspicious variables. Therefore, detection of faulty conditions and isolation of faulty variables can be achieved in one step. Both simulation studies and a real example have shown the effectiveness of the proposed procedure.

• Forward Selection
• Linear Regression
• Multivariate Statistical Process Control
• T² Chart

Additional information

Notes on contributors

Kaibo Wang

Dr. Wang is an Assistant Professor in the Department of Industrial Engineering. His email address is [email protected].

Wei Jiang

Jiang is a Visiting Associate Professor in the Department of Industrial Engineering & Logistics Management. His email address is [email protected].