Signal model-based fault detection and diagnosis for induction motors using features of vibration signal in two-dimension domain

In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.