Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors

Abstract

In recent years, the number of outer rotor permanent magnet brushless direct current (PM BLDC) motor drives has been intensively growing. Due to the specifics of drive operation, bearing faults are especially common, which results in motor stoppage. In a number of these types of motor applications, the monitoring and diagnostics of bearing conditions is relatively rare. This article presents the results of research aimed at searching for simple and simultaneously effective methods for assessing the condition of bearings that can be built into the drive control system. In the experimental research, four vibration signal processing methods were analysed with regards to the identification accuracy of fault symptoms in the geometric elements of bearings (characteristic frequencies). The results are presented for three cases of bearing faults and compared with a new bearing, they were obtained based on a vibration signal analysis using the classical fast Fourier transform (FFT), Fourier transform of signal absolute values, Fourier transform of an envelope signal obtained using the Hilbert transform, and the Fourier transform of a signal filtered with the Teager–Kaiser energy operator (TKEO).

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

“…Most often, they result from improper assembly, often they are also caused by improper selection of the bearing to the drive requirements and its improper operation [26][27][28][29]. Depending on which element of the bearing is spot damaged, different damage symptoms appear in the spectrum of the mechanical vibration signal [13,21,24,25].…”

“…Vibration measurements were carried out for bearings with modelled damage to the outer and inner raceways and rolling elements, and for bearing without a defect, using the DeltaTron 4506 accelerometer by Brüel and Kjaer. Additionally, bearings under testing were damaged by a spark jump to the selected element (electric discharge between the electrode and the damaged element) similar to [1,8,13,31]. In the literature, bearing failures are often presented with a cut raceway or a hole made in the damaged raceway [7,13,17,18,23,25].…”

“…Another approach consists in the comparative analysis of several methods of processing one diagnostic signal. In Reference [13], the authors analysed the vibration acceleration signal to detect the damage to rolling bearings in Brushless Direct Current Motor (BLDCM) drive. In the presented research, the possibility of detecting a damaged bearing was assessed using four methods of signal processing: classical FFT of the signal, Fourier transform from the absolute value of the signal, Fourier transform from the envelope signal calculated using the Hilbert transform, and Fourier transform from the signal filtered by the Teager-Kaiser energy operator.…”

“…Thus, the amplitudes of the same fault can vary from machine to machine and we will get different amplitude values at different harmonic numbers describing the specific bearing. However, the methodology of fault diagnosis will be the same for rolling bearings of different machines [2,5,13,14]. The main task of the diagnostic system designer is to select those harmonics, which are most sensitive to selected fault types for a given drive system.…”

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

“…The fault detection methods mainly include model-driven and data-driven methods. Some scholars have established rolling bearings degradation models by analyzing the prior physical knowledge of rolling bearings [10,11], or completed the incipient fault detection of rolling bearings by analyzing the characteristic frequency of failure of rolling bearings [12,13]. However, the model-based method requires an accurate prior physical model of rolling bearings.…”