Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach
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 measu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/9/1334 |
| _version_ | 1797557148671016960 |
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| author | Pawel Ewert Czeslaw T. Kowalski Teresa Orlowska-Kowalska |
| author_facet | Pawel Ewert Czeslaw T. Kowalski Teresa Orlowska-Kowalska |
| author_sort | Pawel Ewert |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:13:14Z |
| format | Article |
| id | doaj.art-7596e17e036e4ca2852cadce1b19c78c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T17:13:14Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-7596e17e036e4ca2852cadce1b19c78c2023-11-20T10:36:21ZengMDPI AGElectronics2079-92922020-08-0199133410.3390/electronics9091334Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network ApproachPawel Ewert0Czeslaw T. Kowalski1Teresa Orlowska-Kowalska2Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, wyb. Wyspianskiego 27, 50-370 Wroclaw, PolandDepartment of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, wyb. Wyspianskiego 27, 50-370 Wroclaw, PolandDepartment of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, wyb. Wyspianskiego 27, 50-370 Wroclaw, PolandIn 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.https://www.mdpi.com/2079-9292/9/9/1334neural fault detectordiagnostic system architecturerolling bearing faultsinduction motor drive |
| spellingShingle | Pawel Ewert Czeslaw T. Kowalski Teresa Orlowska-Kowalska Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach Electronics neural fault detector diagnostic system architecture rolling bearing faults induction motor drive |
| title | Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach |
| title_full | Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach |
| title_fullStr | Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach |
| title_full_unstemmed | Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach |
| title_short | Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach |
| title_sort | low cost monitoring and diagnosis system for rolling bearing faults of the induction motor based on neural network approach |
| topic | neural fault detector diagnostic system architecture rolling bearing faults induction motor drive |
| url | https://www.mdpi.com/2079-9292/9/9/1334 |
| work_keys_str_mv | AT pawelewert lowcostmonitoringanddiagnosissystemforrollingbearingfaultsoftheinductionmotorbasedonneuralnetworkapproach AT czeslawtkowalski lowcostmonitoringanddiagnosissystemforrollingbearingfaultsoftheinductionmotorbasedonneuralnetworkapproach AT teresaorlowskakowalska lowcostmonitoringanddiagnosissystemforrollingbearingfaultsoftheinductionmotorbasedonneuralnetworkapproach |