Affiliations: [a] Faculty of Electrical Engineering, Sathyabama University, Chennai, Tamil Nadu, India | [b] School of Electronics Engineering, VIT University, Chennai, Tamil Nadu, India
Correspondence:
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Corresponding author. Research scholar, Faculty of Electrical Engineering, Sathyabama University, Chennai, Tamil Nadu 600119, India. Tel.: +91 507 114 80 E-mails: [email protected] (C.N. Gnanaprakasam), E-mail: [email protected] (K. Chitra).
Abstract: In this paper, a hybrid approach is proposed for detecting and classifying the vibration signal of induction motor. The proposed hybrid technique is the combination of S-transformation algorithm and adaptive neuro fuzzy inference system (ANFIS) method. Here, the proposed hybrid method contains two processes, such as, fault detection and classification process. Initially, the pre-processing is applied in the electric motor vibration signal. In the fault detection process, significant features from vibration signals are extracted through the S-transformation algorithm. Consequently, the ANFIS classification technique is employed to classify the signal into the faulty or the normal. The proposed hybrid technique is implemented in MATLAB working platform. The performance of the proposed hybrid technique is evaluated with five types of faulty vibration signals. The performance of the proposed hybrid method is compared with the existing method such as S-transform-RBFNN and S-transform-FFBNN. Analyze these methods with the help of statistical measures such as, accuracy, sensitivity and specificity value.
Keywords: Pre-processing, fault classification, S-transformation, RBFNN and ANFIS