Intelligent anomaly detection method of pump group based on convolve-gated self-attention multi-source data fusion

Aiming at the problems that it is difficult for pump sets to use multi-dimensional abnormal signals for diagnosis and the relationship between multi-dimensional signals cannot be fully extracted under extreme operating conditions, an intelligent anomaly detection method for pump sets using deep learning network and multi-source sensor data is proposed. This method uses convolutional neural network to fuse multi-source sensor data, and can effectively analyze the relationship between multi-dimensional signals. The self-attention mechanism is used to extract the fusion features of input signals with attention weights, so that the constructed anomaly detection model has the ability to adapt to different types of input signals independently, which ensures the accuracy of the proposed method in the abnormal state detection of pump sets in the scenario of multi-source sensor big data. The reliability and accuracy of the proposed method were verified by setting up a pump group fault simulation test rig. The results show that the proposed method can effectively integrate the information characteristics of multi-source sensors, and on this basis can fully complete the fault diagnosis task of the pump group under extreme operating conditions, and has high diagnostic accuracy.