###### Abstract
[7] took advantage of the multi-view manifold component feature extraction (MvMCFE) technique's superiority in nonlinear feature extraction, to propose a fault detection method that combined it with an optimized SVM. [PERSON] et al. [8] used the particle swarm optimization (PSO) algorithm to optimize the SVM for the detection of motor bearing faults, while [PERSON] et al. [9] used the random forest algorithm to detect faults in charging pile equipment. Using the K-nearest neighbors (KNN) algorithm as a basis, [PERSON] et al. [10] proposed a fault diagnosis algorithm and monitoring system for the bearings of hydraulic turbine units. Finally, [PERSON] et al. [11] combined the KNN algorithm and the Naive Bayes algorithm to effectively diagnose the faults of rolling bearings. Most of the studies cited above relied on traditional machine learning methods, which can perform well at fault detection. Still, the fault features must be manually extracted, and they are easily affected by subjective human judgments. [PERSON] et al. [12] used Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the data, and then fused the long short-term memory (LSTM) and gated \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & **Phenomenon** & **Main Reason (s)** & **Characterization Signal** \\ \hline \multirow{2}{*}{Abrasion Crack} & The surface size changes, and the ball is no longer smooth & \multirow{2}{*}{
\begin{tabular}{c} Inhibition \\ lubrication \\ Overload \\ Chemical corrosion, \\ electric corrosion \\ \end{tabular} } & Vibration signal \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main forms of water pump bearing failure in industrialized recirculating aquaculture systems. recurrent unit (GRU) neural networks to achieve accurate gear fault identification. Likewise, [PERSON] et al. [13] suggested an equipment fault detection method based on knowledge graphs and task learning, while [PERSON] et al. [14] used the attention mechanism-based gated recurrent unit (AM-GRU) network to detect the faults of intelligent substation equipment. Lastly, [PERSON] et al. [15] presented a multi-sensor method combining one-dimensional convolution with LSTM for the successful diagnosis of equipment faults. [PERSON] et al. [16] employed the Empirical Mode Decomposition (EMD) method to preprocess the raw signals of the Magnetic Flux Leakage (MFL) and designed a wire rope defect diagnosis network based on the CNN-transformer. [PERSON] et al. [17] integrated the core advantages of the LSTM and transformer architectures for real-time prediction and fault detection in engineering system tasks. The above deep learning algorithms can automatically extract fault features and demonstrate high recognition accuracy. Yet, most of them are based on one-dimensional signals and lack features integrating the time domain with the frequency domain. To that end, [PERSON] et al. [18] adopted a multi-receptive field graph convolutional network: when extracting features, it integrates the information of adjacent nodes and fuses its own parameters, thereby improving the network's overall performance. [PERSON] et al. [19] developed a bearing fault diagnosis method that combines a frequency-domain feature extraction with a dual-stream convolutional neural network (CNN). [PERSON] et al. [20] proposed a lightweight CNN network that fuses one- and two-dimensional features for equipment fault diagnosis; by researching the feature fusion of the time-frequency domain, those authors improved the model's performance. To sum up, progress is being made at improving the method of equipment fault detection, bringing it closer maturing. However, its application is still mostly restricted to standard industries, and its potential applications in the field of IRASs is unexplored and virtually unknown.