Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach

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.

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

“…The proposed NNs can be easily implemented using a low-budget integrated hardware platform based on, e.g., Arm Cortex-M or similar processors, or the diagnostics can be based on the measurement infrastructure existing in the industry and its extension with additional neural detectors. An exemplary concept of a cheap diagnostic system is presented in [14]. Based on the presented work, it can be concluded that classical NNs still are able to meet these requirements and are much less time-consuming in training and simpler in practical implementation than more popular deep-learning networks.…”

“…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.…”

“…Therefore, methods are being sought to eliminate this human factor and automate the detection process. At present, this task is more often performed by neural networks (NNs), which can act as detectors and/or classifiers of damages, minimizing human participation in the diagnostic process [1,5,[14][15][16][17][18][19][20][21][22][23].…”

Stator Winding Fault Phase Identification Using Piezoelectric Sensors in Three-Phase Induction Motors

“…Due to capacity to evaluate the frequency behavior of signals, FFT analysis has been used several times in the diagnosis of electrical machines [12,13]. Therefore, the proposed technique uses Fast Fourier Analysis to perform a diagnose of inter-turn short circuit fault in stator of induction machine.…”

Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

“…From these state-of-the-art papers it can be clearly stated the promising results provided by machine learning approaches, specially for the fault classification and fault severity indication by using several time-features. For instance, Ewert et al [27] proposed the application of an artificial neural network for the classification and identification of individual defects such as rolling element failures, outer race, and inner race faults. The results shown there demonstrates the effectiveness of such methods for the bearing fault diagnosis, which have allowed for real-time diagnosis schemes, optimal for its implementation on hardware.…”