Fault diagnosis based on Fisher discriminant analysis and support vector machines

Abstract

The proficiencies of Fisher discriminant analysis (FDA), support vector machines (SVM), and proximal support vector machines (PSVM) for fault diagnosis (i.e. classification of multiple fault classes) are investigated. The Tennessee Eastman process (TEP) simulator was used to generate overlapping datasets to evaluate the classification performance. When all variables were used, the datasets were masked with irrelevant information, which resulted in poor classification. With key variables selected by genetic algorithms and the contribution charts, SVM and PSVM outperformed FDA and demonstrated the advantage of using nonlinear technique when data are overlapped. The overall misclassification for the testing data using FDA dropped from 38 to 18%; while those using SVM and PSVM dropped from 44–45 to 6%. The effectiveness of the proposed approach is increased in PSVM by saving significant computation time and memory requirement, while obtaining comparable classification results. For auto-correlated data, the incorporation of time lags into SVM and PSVM improved classification results. The added dimensions decreased the degree to which the data overlap and the overall misclassification for the testing set using SVM and PSVM decreased further to 3%.