CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems
Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accu...
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MDPI AG
2025-05-01
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| author | Wei Shao Chengquan Zhou Dawei Sun Chen Li Hongbao Ye |
| author_facet | Wei Shao Chengquan Zhou Dawei Sun Chen Li Hongbao Ye |
| author_sort | Wei Shao |
| collection | DOAJ |
| description | Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accurate diagnosis are imperative. Traditional fault detection methods rely on manual feature extraction, limiting their ability to identify complex faults, and deep learning methods suffer from unstable recognition accuracy. To address these issues, a three-class fault detection method for water pumps based on a convolutional neural network, transformer, and bidirectional gated recurrent unit (CNN-transformer-BiGRU) is proposed here. Methods: It first uses the continuous wavelet transform to convert one-dimensional vibration signals into time–frequency images for input into a CNN to extract the time-domain and frequency-domain features. Next, the transformer enhances the model’s hierarchical learning ability. Finally, the BiGRU captures the forward/backward feature information in the signal sequence. Results: The experimental results show that this method’s accuracy in fault detection is 91.43%, significantly outperforming traditional machine learning models. Using it improved the accuracy, precision, and recall by 1.86%, 1.97%, and 1.86%, respectively, relative to the convolutional neural network and long short-term memory (CNN-LSTM) model. Conclusions: Hence, the proposed model has superior performance indicators. Applying it to aquaculture systems can effectively ensure their stable operation. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-c4d0cd05e7f44637a2b69caf11472ebb2025-08-20T03:46:52ZengMDPI AGApplied Sciences2076-34172025-05-011511611410.3390/app15116114CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture SystemsWei Shao0Chengquan Zhou1Dawei Sun2Chen Li3Hongbao Ye4College of Mathematics and Computer Science, Zhejiang A&F University, 666 Wusu Street, Hangzhou 311300, ChinaAgricultural Equipment Research Institute, Zhejiang Academy of Agricultural Sciences, 298 Desheng Middle Road, Hangzhou 310021, ChinaAgricultural Equipment Research Institute, Zhejiang Academy of Agricultural Sciences, 298 Desheng Middle Road, Hangzhou 310021, ChinaAgricultural Equipment Research Institute, Zhejiang Academy of Agricultural Sciences, 298 Desheng Middle Road, Hangzhou 310021, ChinaAgricultural Equipment Research Institute, Zhejiang Academy of Agricultural Sciences, 298 Desheng Middle Road, Hangzhou 310021, ChinaBackground: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accurate diagnosis are imperative. Traditional fault detection methods rely on manual feature extraction, limiting their ability to identify complex faults, and deep learning methods suffer from unstable recognition accuracy. To address these issues, a three-class fault detection method for water pumps based on a convolutional neural network, transformer, and bidirectional gated recurrent unit (CNN-transformer-BiGRU) is proposed here. Methods: It first uses the continuous wavelet transform to convert one-dimensional vibration signals into time–frequency images for input into a CNN to extract the time-domain and frequency-domain features. Next, the transformer enhances the model’s hierarchical learning ability. Finally, the BiGRU captures the forward/backward feature information in the signal sequence. Results: The experimental results show that this method’s accuracy in fault detection is 91.43%, significantly outperforming traditional machine learning models. Using it improved the accuracy, precision, and recall by 1.86%, 1.97%, and 1.86%, respectively, relative to the convolutional neural network and long short-term memory (CNN-LSTM) model. Conclusions: Hence, the proposed model has superior performance indicators. Applying it to aquaculture systems can effectively ensure their stable operation.https://www.mdpi.com/2076-3417/15/11/6114aquacultureBiGRUconvolutional neural network (CNN)fault detectiontransformer |
| spellingShingle | Wei Shao Chengquan Zhou Dawei Sun Chen Li Hongbao Ye CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems Applied Sciences aquaculture BiGRU convolutional neural network (CNN) fault detection transformer |
| title | CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems |
| title_full | CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems |
| title_fullStr | CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems |
| title_full_unstemmed | CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems |
| title_short | CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems |
| title_sort | cnn transformer bigru a pump fault detection model for industrialized recirculating aquaculture systems |
| topic | aquaculture BiGRU convolutional neural network (CNN) fault detection transformer |
| url | https://www.mdpi.com/2076-3417/15/11/6114 |
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