# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 1 Introduction  
Essential service water pumps (ESWPs) play a crucial role in seaside nuclear power plants (SNPPs), which are responsible for transmitting waste heat through seawater. If an ESWP malfunctions without preparation, it is likely to cause nuclear safety issues due to the inability to discharge waste heat promptly. Its condition monitoring is essential for the predictive maintenance of SNPPs [1] and the production of clean and efficient nuclear energy [2]. On the one hand, its condition monitoring ensures the regular transfer of waste heat and maintains the safe and stable operation of the entire SNPP. On the other hand, it is essential to prepare spare parts like sealing rings and bearings in time according to condition monitoring to prevent shutdowns due to the failure of spare parts to arrive in time, affecting production. Present intelligent condition monitoring methods using equipment's historical data align more with predictive maintenance requirements [3], mainly including anomaly detection (AD) and fault diagnosis (FD). There are many unsupervised AD (UAD) methods [4], such as support vector data description (SVDD) [5], k-nearest neighbor clustering [6], dynamic time warping [7], and anomaly detection based on kernel principal componentanalysis (AKPCA) [8]. Although some AD models based on deep learning have been studied [9; 10], most lack further investigation, except for their network design. Relying on these networks will not only fail to solve current problems but may also lead to new problems, such as the explainability and computational complexity of deep learning models. FD aims to obtain status information about equipment based on learning data. Previous research has been conducted to identify pumps' faults, such as misalignment [9], lack of balance [10], bearing defects [11], leakage [12], etc. Focused on feature extraction, a complex feature extraction method composed of continuous wavelet transform, a multilayer feedforward perceptron neural network, and a genetic algorithm has been used by many researchers to prepare valuable features for support vector machines (SVMs) classifying both mechanical and hydraulic faults of centrifugal pumps [10]. Focused on deep learning networks, some researchers converted non-image signals into image signals [13; 14] for neural networks and extracted features using deep networks instead of traditional feature extraction methods [15]. The above research mainly focused on ordinary pumps. Intelligent condition monitoring research on SNPPs has received increasing attention in recent years. For instance, a deep convolutional conditional generative adversarial network was designed to process imbalanced sample problems of motors and bearings [16]. A deep residual neural network was combined with a transferred vibration image to utilize multi-sensor vibration signals to diagnose the motor and bearing [17]. A novel Artificial Disturbance Method (ADM)-based domain discrepancy generalization promotion framework was proposed, significantly enhancing the generalization ability of convolutional neural networks (CNNs) in nuclear power plant fault diagnosis [18]. A hybrid methodology that integrated knowledge-based methods, quantitative mathematical models, and data-driven methodologies was used to meet the complex need for different parts of SNPPs [19]. A Q network calibrated ensemble method was used to realize the fault diagnosis of a nuclear Circulating Water Pump Gearbox, which focused on the problem of imbalanced samples and shaft distribution [20]. Although these studies obtained some satisfying results, it is still hard to apply deep learning models [21]. Most existing UAD algorithms can find anomalies lacking details, and most existing intelligent FD algorithms can only intelligently diagnose faults that have been learned by the model and cause the misdiagnosis of faults that have not been learned. Predictive maintenance needs to find out whether the equipment is in abnormal operation and specific information about the abnormal operation to guide maintenance.