Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network
Abstract Taper roller bearing is a widely used moving component in heavy industrial machinery. Hence, early detection and repair of even minor faults in taper roller bearing is a fault diagnosis and prognosis strategy followed by modern industries. Although many methods for this exist today, the pen...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93514-3 |
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| author | A. Anwarsha Narendiranath Babu T |
| author_facet | A. Anwarsha Narendiranath Babu T |
| author_sort | A. Anwarsha |
| collection | DOAJ |
| description | Abstract Taper roller bearing is a widely used moving component in heavy industrial machinery. Hence, early detection and repair of even minor faults in taper roller bearing is a fault diagnosis and prognosis strategy followed by modern industries. Although many methods for this exist today, the penetration of artificial intelligence and big data analysis into modern industries opens up the possibility of developing better fault diagnosis methods. Such a fault diagnosis and fault classification strategy is going to be discussed in this article. For that, a Tunable Q-factor Wavelet Transform (TQWT) is employed for signal processing, and a Long–Short-Term Memory (LSTM) network is employed for fault classification in this work. It is clear from the experimental findings that the TQWT and LSTM combination can very efficiently and reliably diagnose the faults present in the bearings, and it can classify the types of faults with one hundred percent accuracy. Also, the superiority of the method proposed in this article is confirmed by the fact that it is able to produce better results when compared with the other four combinations of Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). |
| format | Article |
| id | doaj-art-651294c845c844b29b91a76d3bd6bae3 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-651294c845c844b29b91a76d3bd6bae32025-08-20T02:51:23ZengNature PortfolioScientific Reports2045-23222025-03-0115112710.1038/s41598-025-93514-3Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory networkA. Anwarsha0Narendiranath Babu T1School of Mechanical Engineering, Vellore Institute of TechnologySchool of Mechanical Engineering, Vellore Institute of TechnologyAbstract Taper roller bearing is a widely used moving component in heavy industrial machinery. Hence, early detection and repair of even minor faults in taper roller bearing is a fault diagnosis and prognosis strategy followed by modern industries. Although many methods for this exist today, the penetration of artificial intelligence and big data analysis into modern industries opens up the possibility of developing better fault diagnosis methods. Such a fault diagnosis and fault classification strategy is going to be discussed in this article. For that, a Tunable Q-factor Wavelet Transform (TQWT) is employed for signal processing, and a Long–Short-Term Memory (LSTM) network is employed for fault classification in this work. It is clear from the experimental findings that the TQWT and LSTM combination can very efficiently and reliably diagnose the faults present in the bearings, and it can classify the types of faults with one hundred percent accuracy. Also, the superiority of the method proposed in this article is confirmed by the fact that it is able to produce better results when compared with the other four combinations of Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN).https://doi.org/10.1038/s41598-025-93514-3Fault diagnosisTaper roller bearingTunable q-factor wavelet transformDeep learningLong–short-term memory network |
| spellingShingle | A. Anwarsha Narendiranath Babu T Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network Scientific Reports Fault diagnosis Taper roller bearing Tunable q-factor wavelet transform Deep learning Long–short-term memory network |
| title | Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network |
| title_full | Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network |
| title_fullStr | Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network |
| title_full_unstemmed | Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network |
| title_short | Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network |
| title_sort | fault detection of taper roller bearings using tunable q factor wavelet transform and fault classification using long short term memory network |
| topic | Fault diagnosis Taper roller bearing Tunable q-factor wavelet transform Deep learning Long–short-term memory network |
| url | https://doi.org/10.1038/s41598-025-93514-3 |
| work_keys_str_mv | AT aanwarsha faultdetectionoftaperrollerbearingsusingtunableqfactorwavelettransformandfaultclassificationusinglongshorttermmemorynetwork AT narendiranathbabut faultdetectionoftaperrollerbearingsusingtunableqfactorwavelettransformandfaultclassificationusinglongshorttermmemorynetwork |