An Integrated Fault Diagnosis Method Based On The ICA-SVM

With the constant expansion of industrial production scale, the complexity of theprocess is also increasing. A stable and orderly conduct of production process becomesespecially important. In the process of modernization, a large number of process dataparameters were collected and stored. The detection method based on data-driven wasused to monitor and analyze these process data. The anomaly present of the processsystem could be detected timely and effective, and classified the anomaly data.According to the classification results diagnosed the fault sources and took appropriatemeasures. The method can effectively reduce the possibility of failure, and hasinstructional meaning for the fault diagnosis of industrial process. The following is themajor coverage.Firstly, using the conventional principal component analysis (PCA) establish theTE process monitoring model, and analyze the monitoring result. Because of the TEprocess data does not obey the Gaussian distribution, it is difficult to achievesatisfactory detection results. The false alarm and the underreporting is quite serious inthe monitoring process. Therefore, on the basis of the PCA, the independent componentanalysis (ICA) is used for TE process. ICA can sensitively and effectively detect thefault, and has a better monitoring performance. The support vector machine (SVM) isused to classify for the extracting the fault information of the ICA. If more than onepossible fault source, the binary tree SVM is used to multi-classify. The fault source isdiagnosed one by one. Through the fault diagnosis of TE process, the accuracy andvalidity is verified of the integrated ICA-SVM algorithm.Then, the ICA-SVM fusion algorithm is applied to the three-tank liquid levelcontrol system. The algorithm is used to monitor and diagnose the running data of thesystem, and diagnose the specific type of the fault. The fusion method for thenon-Gaussian distribution data is very effective, and improves the timeliness of faultdiagnosis.Finally, a real-time monitoring interface is established in the VB environment. Thefault diagnosis monitoring platform is established by VB call matlab procedures. Theplatform realize monitoring, analyzing and diagnosing for the runtime system, andensure the normal operation of laboratory equipment.