###### 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. a 202515611  
living environment for farmed organisms. How to efficiently detect equipment failure and safeguard this pump's long-term stable operation is arguably very important. Common faults of water pumps include impeller damage, blockage, motor burnout due to phase loss, and bearing wear and corrosion. In IRASs, however, impeller wear rarely occurs because there is no sediment in the water and most of its debris, such as feces and residual feed, are adequately filtered by the feces collector, micro-filter, and biological filter barrel. Likewise, since the control cabinet is equipped with phase-loss protection, the motor burnout phenomenon (due to phase loss) is also relatively rare. The most common fault is the bearing failure of the water pump, which can take several forms (Table 1). As seen from Table 1, the most direct manifestation of bearing faults is an altered vibration signal, which is mainly expressed in two ways:  
1. Increase in vibration amplitude: Under normal circumstances, the water pump bearing operates stably, with its vibration amplitude remaining at a relatively low and stable level. When the bearing incurs wear and corrosion, its clearance will increase, loosening the tight fit between the raceway and the balls or rollers. During the operation process, additional impact forces and vibrations are generated, substantially increasing the vibration amplitude. 2. Change in vibration frequency: When there is local damage to the bearing, such as one or more cracks on the surface of its balls or pits on the raceway, specific frequency components related to the bearing's own structure and faults are generated. If the fit between the bearing and the shaft or the bearing seat becomes loose, this will decrease the vibration frequency. In recent years, much research has emerged on equipment fault detection. [PERSON] et al. [5] proposed a detection method using the variational mode decomposition (VMD) algorithm for early rolling bearing faults. In order to better extract effective features, [PERSON] et al. [6] introduced a method of constructing features from the time-domain energy entropy values of modal components, then input those into a support vector machine (SVM) for the identification of rolling bearing faults. In another work, [PERSON] et al.