A NOVEL SCHEME FOR MULTIVARIATE STATISTICAL FAULT DETECTION WITH APPLICATION TO THE TENNESSEE EASTMAN PROCESS

Canonical correlation analysis (CCA) has gained great success for fault detection (FD) in recent years. However, it cannot preserve the prior information of the underlying process. To cope with these difficulties, this paper proposes an improved CCA-based ED scheme using a novel multivariate statistical technique, called sparse collaborative regression (SCR.). The core of the proposed method is to take the prior information as a supervisor, and then integrate it with CCA. Further, the l(2,1)-norm is employed to reduce redundancy and avoid overfitting, which facilitates its interpretability. In order to solve the proposed SCR, an efficient alternating optimization algorithm is developed with convergence analysis. Finally, some experimental studies on a simulated example and the benchmark Tennessee Eastman process are conducted to demonstrate the superiority over the classical CCA in terms of the false alarm rate and fault detection rate. The detection results indicate that the proposed method is promising.