Fault Diagnosis of Main Pump in Converter Station Based on Deep Neural Network

Abstract

As the core component of the valve cooling system in a converter station, the main pump plays a major role in ensuring the stable operation of the valve. Thus, accurate and efficient fault diagnosis of the main pump according to vibration signals is of positive significance for the detection of failure equipment and reducing the maintenance cost. This paper proposed a new neural network based on the vibration signals of the main pump to classify four faults and one normal state of the main pump, which consisted of a convolutional neural network (CNN) and long short-term memory (LSTM). Multi-scale features were extracted by two CNNs with different kernel sizes, and temporal features were extracted by LSTM. Moreover, random sampling was used in data processing for imbalanced data, which is meaningful for data symmetry. Experimental results indicated that the accuracy of the network was 0.987 obtained from the test set, and the average values of F1-score, recall, and precision were 0.987, 0.987, and 0.988, respectively. It was found that the proposed network performed well in a multi-label fault diagnosis of the main pump and was superior to other methods.