Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics
This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2012/847203 |
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| _version_ | 1832552630487875584 |
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| author | Achmad Widodo Djoeli Satrijo Toni Prahasto Gang-Min Lim Byeong-Keun Choi |
| author_facet | Achmad Widodo Djoeli Satrijo Toni Prahasto Gang-Min Lim Byeong-Keun Choi |
| author_sort | Achmad Widodo |
| collection | DOAJ |
| description | This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies. |
| format | Article |
| id | doaj-art-39b45f7dc8244765912f495dadbd9382 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-39b45f7dc8244765912f495dadbd93822025-02-03T05:58:11ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342012-01-01201210.1155/2012/847203847203Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault DiagnosticsAchmad Widodo0Djoeli Satrijo1Toni Prahasto2Gang-Min Lim3Byeong-Keun Choi4Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Professor Sudarto, Tembalang, Semarang 50275, IndonesiaDepartment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Professor Sudarto, Tembalang, Semarang 50275, IndonesiaDepartment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Professor Sudarto, Tembalang, Semarang 50275, IndonesiaDepartment of Mechanical and Automotive Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, Republic of KoreaDepartment of Energy and Mechanical Engineering, Institute of Marine Industry, Gyeongsang National University, 445 Inpyeong-dong, Gyeongnam-do, Tongyoung City 650-160, Republic of KoreaThis paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.http://dx.doi.org/10.1155/2012/847203 |
| spellingShingle | Achmad Widodo Djoeli Satrijo Toni Prahasto Gang-Min Lim Byeong-Keun Choi Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics International Journal of Rotating Machinery |
| title | Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics |
| title_full | Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics |
| title_fullStr | Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics |
| title_full_unstemmed | Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics |
| title_short | Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics |
| title_sort | confirmation of thermal images and vibration signals for intelligent machine fault diagnostics |
| url | http://dx.doi.org/10.1155/2012/847203 |
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