Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning
Bearings play an integral role in ensuring the reliability and efficiency of rotating machinery — reducing friction and handling critical loads. Bearing failures that constitute up to 90% of mechanical faults highlight the imperative need for reliable condition monitoring and fault detection. This s...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000659 |
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| author | Tasfiq E. Alam Md Manjurul Ahsan Shivakumar Raman |
| author_facet | Tasfiq E. Alam Md Manjurul Ahsan Shivakumar Raman |
| author_sort | Tasfiq E. Alam |
| collection | DOAJ |
| description | Bearings play an integral role in ensuring the reliability and efficiency of rotating machinery — reducing friction and handling critical loads. Bearing failures that constitute up to 90% of mechanical faults highlight the imperative need for reliable condition monitoring and fault detection. This study proposes a multimodal bearing fault classification approach that relies on vibration and motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The method fuses features from multiple signals to enhance the accuracy of fault detection. Under the baseline condition (1500 rpm, 0.7 Nm load torque, and 1000 N radial force), the model reaches an accuracy of 96% with addition of L2 regularization. In addition, the model demonstrates robust performance across three distinct operating conditions by employing transfer learning (TL) strategies. Among the tested TL variants, the approach that preserves parameters up to the first max-pool layer and then adjusts subsequent layers achieves the highest performance. While this approach attains excellent accuracy across varied conditions, it requires more computational time due to its greater number of trainable parameters. To address resource constraints, less computationally intensive models offer feasible trade-offs, albeit at a slight accuracy cost. Overall, this multimodal 1D CNN framework with late fusion and TL strategies lays a foundation for more accurate, adaptable, and efficient bearing fault classification in industrial environments with variable operating conditions. |
| format | Article |
| id | doaj-art-6aada61b85f341ccaefca1bedeb0eaa5 |
| institution | Kabale University |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-6aada61b85f341ccaefca1bedeb0eaa52025-08-20T03:31:10ZengElsevierMachine Learning with Applications2666-82702025-09-012110068210.1016/j.mlwa.2025.100682Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learningTasfiq E. Alam0Md Manjurul Ahsan1Shivakumar Raman2Department of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA 11 https://www.ou.edu.Corresponding author.; Department of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA 11 https://www.ou.edu.Department of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA 11 https://www.ou.edu.Bearings play an integral role in ensuring the reliability and efficiency of rotating machinery — reducing friction and handling critical loads. Bearing failures that constitute up to 90% of mechanical faults highlight the imperative need for reliable condition monitoring and fault detection. This study proposes a multimodal bearing fault classification approach that relies on vibration and motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The method fuses features from multiple signals to enhance the accuracy of fault detection. Under the baseline condition (1500 rpm, 0.7 Nm load torque, and 1000 N radial force), the model reaches an accuracy of 96% with addition of L2 regularization. In addition, the model demonstrates robust performance across three distinct operating conditions by employing transfer learning (TL) strategies. Among the tested TL variants, the approach that preserves parameters up to the first max-pool layer and then adjusts subsequent layers achieves the highest performance. While this approach attains excellent accuracy across varied conditions, it requires more computational time due to its greater number of trainable parameters. To address resource constraints, less computationally intensive models offer feasible trade-offs, albeit at a slight accuracy cost. Overall, this multimodal 1D CNN framework with late fusion and TL strategies lays a foundation for more accurate, adaptable, and efficient bearing fault classification in industrial environments with variable operating conditions.http://www.sciencedirect.com/science/article/pii/S2666827025000659Bearing fault detection1D CNNTransfer learningMultimodal fusionL2 regularization |
| spellingShingle | Tasfiq E. Alam Md Manjurul Ahsan Shivakumar Raman Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning Machine Learning with Applications Bearing fault detection 1D CNN Transfer learning Multimodal fusion L2 regularization |
| title | Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning |
| title_full | Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning |
| title_fullStr | Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning |
| title_full_unstemmed | Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning |
| title_short | Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning |
| title_sort | multimodal bearing fault classification under variable conditions a 1d cnn with transfer learning |
| topic | Bearing fault detection 1D CNN Transfer learning Multimodal fusion L2 regularization |
| url | http://www.sciencedirect.com/science/article/pii/S2666827025000659 |
| work_keys_str_mv | AT tasfiqealam multimodalbearingfaultclassificationundervariableconditionsa1dcnnwithtransferlearning AT mdmanjurulahsan multimodalbearingfaultclassificationundervariableconditionsa1dcnnwithtransferlearning AT shivakumarraman multimodalbearingfaultclassificationundervariableconditionsa1dcnnwithtransferlearning |