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).
Highlights
The permanent magnet brushless direct current motor (PM BLDC), due to its advantages, is increasingly used in various industrial drives and a large number of electrically-propelled or electrically supported vehicles
This paper presents the results of research on vibration in a PM BLDC motor embedded in a wheel directed to finding a simple method allowing to detect the faults in bearing construction elements
It was shown that the use of an additional signal processing method, based on the Hilbert transform, Teager–Kaiser energy operator (TKEO) filter or the absolute value transform, allows to better monitor bearing condition
Summary
The permanent magnet brushless direct current motor (PM BLDC), due to its advantages (high efficiency, high power factor, very good power to volume ratio, low maintenance requirements), is increasingly used in various industrial drives and a large number of electrically-propelled or electrically supported vehicles. The variety of surfaces on which vehicles move, resulting in the generation of pulse impact, high rotational speeds and changing atmospheric conditions, have a significant influence on the time of reliable operation of used rolling bearings This is why it becomes necessary to monitor bearing conditions on-line so as to be able to detect faults at their initial stage and plan service actions. The goal of this work is the comparison of four known and simple methods of vibration signal analysis in terms of assessing their suitability for detecting the symptoms of damages to rolling bearings of low-power PM BLDC motor embedded in a wheel. The analysis of measurement data using selected signal processing methods is shown in Section 5, followed by a short summary
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