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An Integrated Fault Diagnosis Method Based on the ICA-SVM

For sets of measurements does not follow a Gaussian distribution, the conventional principal component analysis (PCA) method has the disadvantage of low diagnostic yield. An integrated fault diagnosing method based on the independent component analysis (ICA) and support vector machine (SVM) was proposed. The observed data is preprocessed and feature extracted by ICA and a monitoring model was developed. When the fault is detected, SVM is adopted to classifying and diagnosing the type of faults. It is applied for fault diagnosing in the Three-Tank water level control system. The simulation results show that the fault diagnosis rates of this method is 99.8%, which can effectively detect and diagnose the fault.