A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM
Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/2756284 |
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| _version_ | 1849686660246142976 |
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| author | Lu Yu Jianling Qu Feng Gao Yanping Tian |
| author_facet | Lu Yu Jianling Qu Feng Gao Yanping Tian |
| author_sort | Lu Yu |
| collection | DOAJ |
| description | Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods. |
| format | Article |
| id | doaj-art-b9e453d14fd04e56976d78b786f8723a |
| institution | DOAJ |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b9e453d14fd04e56976d78b786f8723a2025-08-20T03:22:38ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/27562842756284A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTMLu Yu0Jianling Qu1Feng Gao2Yanping Tian3Qingdao Branch of Naval Aviation University, Qingdao, ChinaQingdao Branch of Naval Aviation University, Qingdao, ChinaQingdao Branch of Naval Aviation University, Qingdao, ChinaQingdao Branch of Naval Aviation University, Qingdao, ChinaFaced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.http://dx.doi.org/10.1155/2019/2756284 |
| spellingShingle | Lu Yu Jianling Qu Feng Gao Yanping Tian A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM Shock and Vibration |
| title | A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM |
| title_full | A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM |
| title_fullStr | A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM |
| title_full_unstemmed | A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM |
| title_short | A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM |
| title_sort | novel hierarchical algorithm for bearing fault diagnosis based on stacked lstm |
| url | http://dx.doi.org/10.1155/2019/2756284 |
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