Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions
The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines ac...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816403/ |
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| _version_ | 1841563308654592000 |
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| author | Muhammad Ahsan Muhammad Waqar Hassan Jose Rodriguez Mohamed Abdelrahem |
| author_facet | Muhammad Ahsan Muhammad Waqar Hassan Jose Rodriguez Mohamed Abdelrahem |
| author_sort | Muhammad Ahsan |
| collection | DOAJ |
| description | The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings. |
| format | Article |
| id | doaj-art-96776f94d77e4fd09e56319b3e0a5f81 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-96776f94d77e4fd09e56319b3e0a5f812025-01-03T00:01:39ZengIEEEIEEE Access2169-35362025-01-01131026104510.1109/ACCESS.2024.352294810816403Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load ConditionsMuhammad Ahsan0https://orcid.org/0000-0003-2362-3297Muhammad Waqar Hassan1Jose Rodriguez2https://orcid.org/0000-0002-1410-4121Mohamed Abdelrahem3https://orcid.org/0000-0003-2923-2094Department of Measurements and Control Systems, Silesian University of Technology, Gliwice, PolandDepartment of Electrical Engineering and Industrial Automation, Silesian University of Technology, Gliwice, PolandCenter for Energy Transition, Universidad San Sebastián, Santiago, ChileElectrical Engineering Department, Assiut University, Asyut, EgyptThe presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.https://ieeexplore.ieee.org/document/10816403/Continuous wavelet transform (CWT)LeNet-5long short-term memory (LSTM)fault diagnosisvibration analysispredictive maintenance |
| spellingShingle | Muhammad Ahsan Muhammad Waqar Hassan Jose Rodriguez Mohamed Abdelrahem Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions IEEE Access Continuous wavelet transform (CWT) LeNet-5 long short-term memory (LSTM) fault diagnosis vibration analysis predictive maintenance |
| title | Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions |
| title_full | Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions |
| title_fullStr | Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions |
| title_full_unstemmed | Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions |
| title_short | Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions |
| title_sort | enhanced fault diagnosis in rotating machinery using a hybrid cwt lenet 5 lstm model performance across various load conditions |
| topic | Continuous wavelet transform (CWT) LeNet-5 long short-term memory (LSTM) fault diagnosis vibration analysis predictive maintenance |
| url | https://ieeexplore.ieee.org/document/10816403/ |
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