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

Abstract

Purpose -The 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/approach -Aging 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. Findings -Bearing damage characterization was determined between 2-4 kHz and some specific frequencies were calculated as harmonics of the bearing characteristic frequencies.Research limitations/implications -The 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 implications -The 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/value -There 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.

Berthil cepstrum: a novel vibration analysis method based on marginal Hilbert spectrum applied to artificial motor aging

“…Numbers 1-4 denote electrical and mechanical measurement sensors, 6-10 are accelerometers. 11 The fluting run of 30 minutes consisted of the motor running with no load and with an external 27 A, 30V AC current to the shaft. In each cycle's end, the motor subject to aging was run on full load in order to measure its performance, recording data at a sampling frequency of 12kHz.…”

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

“…[8] proposed a fault signal classification method based on support vector machine (SVM) and short-time Fourier transform (STFT) for sensor data fusion, which combined vibration, current, voltage, sound and temperature information to detect and identify motor faults; Ref. [9] fused the vibration signal collected from two-direction of the motor, then the fused vibration signal is decomposed into multiple scales by continuous wavelet transform (CWT), and the first scale is used to represent the degradation of bearing. Although these methods above make up for the limitation of single signal by fusing two or more signal, the fault features were extracted from the output signal of the system without considering the influence of the input signal on the system, so it cannot fully reflect the non-linear characteristics of the system.…”

A Review Study on Mathematical Methods for Fault Detection Problems in Induction Motors

“…These results indicate that the signals have a normal distribution and the overall standard deviation has increased by a factor of about 6, showing the damage. Bearing faults can be diagnosed by frequency domain analysis of vibration signals (12)(13)(14) . When a fault occurs in any bearing components, this creates vibrations at characteristic frequencies defined by the bearing geometry.…”

“…In order to determine precisely which frequency band reflects the bearing fluting damage, the sub-band or the MRA of the faulty signals was performed by dividing them into eight subbands in the frequency range 0-6 kHz (7,12,14) . These are given in Table 3 in terms of details (di) and approximations (ai).…”

“…(1)(2)(3)(4)(5)(6)(7) This paper addresses fault detection methods in induction motors together with theory and applications on experimental data acquired during performance test of the motors subjected to accelerated aging (6)(7) . Detection of eccentricity fault (7)(8)(9) , bearing fault (10)(11)(12)(13)(14)(15)(16)(17)(18) , and stator insulation fault (19)(20) is considered. Applications of statistical methods, power spectral density analysis, coherence analysis, continuous and discrete wavelet transform, autoregressive modeling method, adaptive neuro-fuzzy inference system, artificial neural network is presented by means of the experimental data.…”