Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique
In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/1292190 |
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| author | Saud Altaf Muhammad Waseem Soomro Mirza Sajid Mehmood |
| author_facet | Saud Altaf Muhammad Waseem Soomro Mirza Sajid Mehmood |
| author_sort | Saud Altaf |
| collection | DOAJ |
| description | In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor. |
| format | Article |
| id | doaj-art-f5c980fc43d54a1ea51805d49def4c61 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-f5c980fc43d54a1ea51805d49def4c612025-02-03T01:10:31ZengWileyModelling and Simulation in Engineering1687-55911687-56052017-01-01201710.1155/2017/12921901292190Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling TechniqueSaud Altaf0Muhammad Waseem Soomro1Mirza Sajid Mehmood2Sensor Network and Smart Environment Research Centre, Auckland University of Technology, Auckland, New ZealandSchool of Professional Engineering, Manukau Institute of Technology, Auckland, New ZealandSajid Brothers Engineering Industries (Pvt.) Ltd., Gujranwala, PakistanIn this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.http://dx.doi.org/10.1155/2017/1292190 |
| spellingShingle | Saud Altaf Muhammad Waseem Soomro Mirza Sajid Mehmood Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique Modelling and Simulation in Engineering |
| title | Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique |
| title_full | Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique |
| title_fullStr | Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique |
| title_full_unstemmed | Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique |
| title_short | Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique |
| title_sort | fault diagnosis and detection in industrial motor network environment using knowledge level modelling technique |
| url | http://dx.doi.org/10.1155/2017/1292190 |
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