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CNN-Transformer-BiGRU: A Pump Fault Detection Model for Industrialized Recirculating Aquaculture Systems

文献类型： 外文期刊

作者： Shao, Wei 1 ; Zhou, Chengquan 2 ; Sun, Dawei 2 ; Li, Chen 2 ; Ye, Hongbao 2 ;

作者机构： 1.Zhejiang A&F Univ, Coll Math & Comp Sci, 666 Wusu St, Hangzhou 311300, Peoples R China

2.Zhejiang Acad Agr Sci, Agr Equipment Res Inst, 298 Desheng Middle Rd, Hangzhou 310021, Peoples R China

3.Minist Agr & Rural Affairs, Minist Prov Joint Construct, Key Lab Agr Equipment Southeast Hilly & Mountainou, 298 Desheng Middle Rd, Hangzhou 310021, Peoples R China

关键词： aquaculture; BiGRU; convolutional neural network (CNN); fault detection; transformer

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2025 年 15 卷 11 期

页码：

收录情况： SCI

摘要： Featured Application The model is mainly used for fault detection of pumps in industrial recirculating aquaculture.Abstract 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.

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