Vibration signal analysis and condition monitoring of centrifugal pumps based on time-delay phase space reconstruction and attention mechanism

Centrifugal pumps are the core driving equipment in fluid transport systems, and their operating conditions directly affect the stability and efficiency of the system. Although monitoring changes in physical quantities such as vibration signals can realize the condition monitoring of pump operation, the accuracy of using traditional signal analysis methods to monitor the pump’s working status is relatively low. To address this, this paper proposes a classification method using a convolutional neural network with an attention mechanism on time-delay phase space reconstructed images to improve the accuracy of condition monitoring. The method transforms one-dimensional time series signals into high-dimensional spatial trajectories through time-delay phase space reconstruction, which can provide more detailed feature information for the convolutional neural network with relatively low computational cost, and analyzes the stability of centrifugal pump operation through high-dimensional spatial trajectory analysis. This method combines channel attention mechanisms and spatial attention mechanisms to further enhance the feature extraction capability, enabling the neural network to better capture key features in the pump vibration signals, thereby realizing the recognition and classification of vibration signals under different conditions, and improving the accuracy and robustness of condition monitoring. The results show that in experiments identifying five different types of signals, this method ultimately achieved a test set accuracy of 99.2%, representing an improvement of approximately 7.25% compared to one-dimensional convolution, and a reduction of about 28% in training epochs compared to two-dimensional convolution without attention modules under the same accuracy level.