Application of Time-Series Data Mining for Fault Diagnosis of Induction Motors

Abstract

The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces a technique to detect faults in induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is used to display the signals. Fourier transform is used to convert the signals onto frequency domain. After the signals have been converted, the features of the signals are extracted by the signal processing methods like the wavelet analysis, spectrum analysis, and other methods. The discovered features are entered to a pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, or other models. This paper describes the results of detecting fault using Fourier and wavelet analysis.