Fault size classification of rotating machinery using support vector machine

Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults