Development of a Fault Detection Approach Based on SVM Apllied to Industrial Data

In existing production plants, sensor systems and other sources provide information about the plant condition. This paper presents methods for how data can be conveniently summarized, treated, and evaluated to retain characteristic features and allocate them to certain faults respectively to use them for monitoring purposes. This work details the development of a method to be applied to selected data sets, and which then can be expanded for use in the real environment. This paper details a procedure developed for automated selection and processing to reduce the time exposure of qualified personnel. A number of possible methods of analysis were tested for their ability to point out conspicuous events, especially Wavelet Transformation for feature extraction and Support Vector Machines for classification. Data sets that are correlated to the different conditions of the system are used for training and testing. After training, the algorithm will be able to detect different faults in time. In today’s practice, faults are analyzed after they have occurred. Applying the method, a major failure can be prevented by detecting contingency faults. Using real industrial data from the hot strip mill of ThyssenKrupp Steel Europe (TKSE), the developed approach will be tested offline for practical relevance.