Abstract
In this article, a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested. The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data. The evaluation of the bearing condition is made by a suitably trained neural network (NN), on the basis of the spectral and envelope analysis of the mechanical vibrations. The system was developed in the LabVIEW environment in such a way that it could be run on any PC. The functionality of the application has been tested on a real object. The study was conducted on a low-power IM equipped with a set of specially prepared bearings so as to model the different damages. In the designed computer system, a selected NN for detecting and identifying the defects of individual components of the induction motor’s bearings was implemented. The training data for NNs were obtained from real experiments. The magnitudes of the characteristic harmonics, obtained from the spectral analysis and the envelope analysis, were used for training and testing the developed neural detectors based on Matlab toolbox. The experimental test results of the developed monitoring and diagnosis system are presented in the article. The evaluation of the system’s ability to detect and identify the defects of individual components of bearings, such as the rolling element, and outer race and inner race, was made. It was also shown that the developed NN-based detectors are insensitive to other motor faults, such as short-circuits of the stator winding, broken rotor bars or motor misalignment.
Highlights
In the scientific research and exploitation practice of induction motor (IM) drives, it is necessary to find reliable solutions for diagnostic systems, which are expected to meet more and more difficult requirements
The signals for neural network (NN) testing were based on data obtained from the Electrical Engineering Laboratory of Case Western Reserve University Bearing Fault Database, and not on the measurement signals obtained on the test stand by the authors
In this case, it was not possible to check how the convolutional neural networks (CNN) works in the online mode and how it reacts to other damages occurring in the induction motor
Summary
In the scientific research and exploitation practice of induction motor (IM) drives, it is necessary to find reliable solutions for diagnostic systems, which are expected to meet more and more difficult requirements. The signals for NN testing were based on data obtained from the Electrical Engineering Laboratory of Case Western Reserve University Bearing Fault Database, and not on the measurement signals obtained on the test stand by the authors In this case, it was not possible to check how the CNN works in the online mode and how it reacts to other damages occurring in the induction motor. In Reference [30], the authors proposed a CNN to detect damage to rolling bearings based on the wavelet analysis of the vibration acceleration signal obtained from the above-mentioned database and from the BaoSteel MRO Management System. The neural detectors proposed in this paper are characterized by a very simple structure (they contain a maximum of five neurons in one hidden layer), and they indicate which structural element of the bearing is damaged and have a relatively high efficiency.
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