Physical Information-Assisted Attention Convolution Neural Network (PACNN) and Its Application to Plunger Pump Fault Diagnosis

Plunger pumps serve as the core of the fluid delivery system and their fault diagnosis plays a critical role in ensuring system reliability and operational safety. As a prominent deep learning technique, convolutional neural networks (CNNs) have demonstrated significant success in the field of machinery fault diagnosis. However, conventional CNNs typically employ an end-to-end structure that directly processes raw measured signals as model input, often overlooking in-depth exploration of the underlying physical information and thus resulting in limited diagnostic accuracy under complex working conditions. To overcome this challenge, this paper presents a Physical information-assisted Attention Convolutional Neural Network (PACNN) for more robust and accurate plunger pump fault diagnosis. Unlike the traditional CNNs, which rely solely on original measured signals, PACNN enriches the network inputs by incorporating time-domain and frequency-domain features extracted from the measured signals. These features provide richer physical insights and are beneficial to enhancing the data representation. Furthermore, for handling the information fusion of the augmented network inputs, an improved region-focused multi-head attention mechanism is designed by accounting for the scale difference between sensor data and physical features. Additionally, the PACNN model incorporates a learnable convolutional kernel structure based on B-spline functions, thereby boosting the model’s capability to interpret complex fault patterns. Finally, an industrial plunger pump dataset is utilized to validate the method performance and the results show that the proposed PACNN achieves an average diagnostic accuracy of 99.50%, significantly outperforming traditional methods.