Incremental Fault Diagnosis for Nonlinear Processes

Abstract

A new faults classification method based on on-line independent support vector machine (OISVM) is proposed for fault diagnosis in nonlinear processes. Fault diagnosis can be taken as a pattern recognition problem. As most processes are intrinsically nonlinear, support vector machines (SVMs) are one of the most popular and promising classification algorithms. The fatal drawbacks of standard SVM is the computing overhead grows with the number of training samples, where as training samples from real industrial processes are increasing with time grows. An incremental fault diagnosis approach based on OISVM is proposed in this work. Some related problem such as variable selection and parameter setting are also discussed in this work. Simulation results on the Tennessee Eastman process (TEP) demonstrate the effectiveness of the proposed method.