Fault detection based on continuous wavelet transform and sensor fusion in electric motors

Abstract

PurposeThe purpose of this paper is to extract features from vibration signals measured from induction motors subjected to accelerated aging of bearings by fluting tests.Design/methodology/approachAging tests were performed according to IEEE test procedures. The data acquisition involved the measurement of vibration signals using accelerometers that were installed on the bearings and on the motor casing. In this application, only two accelerometers, which were placed near the process end of the motor bearing, are used for data analysis and feature extraction studies. After the data collection, information from the two sensors was combined using simple sensor fusion method under the linearity conditions, and then spectral analysis and time‐scale analysis were performed. The fused vibration signal is decomposed into several scales using continuous wavelet transform (CWT) and its first scale is used to indicate the bearing degradation.FindingsBearing damage characterization was determined between 2‐4 kHz and some specific frequencies were calculated as harmonics of the bearing characteristic frequencies.Research limitations/implicationsThe bearing damage characteristics used in this study is occurred by the experimental study. In terms of the methodology, the use of the CWT shows the fault characteristics from the initial case.Practical implicationsThe experimental study and data acquisition are based on the accelerated aging of the motor bearings. Hence, the real aging is represented by the accelerated one. But, this situation reflects same properties of the aging occurred in industrial environments. The methodology is also applicable to the hardware application.Originality/valueThere are two important aspects of this research: the experimental study and the application of CWT to get the potential defects, which will appear as a failure in future, from the healthy case of the motor bearings.