An improved fault diagnosis approach based on support vector machine

Fault diagnosis is extremely important for guaranteeing safe and reliable operation of modern industrial process. As an active branch of fault diagnosis, data-driven methods attract more and more attention in recent years, because they solely depend on information collected in historical databases. The support vector machine (SVM), aims at minimizing the structural risk, exhibits superior generalization ability, and succeeds in the nonlinear classification problem. This paper proposes an improved SVM based fault diagnosis framework, which consists of two primary components: (1) feature extraction; (2) classification. More specifically, multi-scale principal component analysis (MSPCA) is performed to achieve multi-scale analysis and dimension reduction. Classification combines SVM classifier with parameters optimization method, which further encompasses grid search (GS) and particle swarm optimization (PSO). To demonstrate the accuracy and efficiency of our approach, we perform experiments on the classical Tennessee Eastman (TE) process.