Anomaly Detection of Hydropower Bearing Based on Convolutional Neural Network Autoencoder

Hydropower has become an important energy supply method due to its advantages of pollution-free, renewable, and abundant resources. With the development of detection technology, methods that can detect anomaly without stopping the hydropower unit have gradually gained attention. An autoencoder based on one-dimensional convolutional neural network is proposed for anomaly detection of bearing condition monitoring system of the hydropower unit. The input sensor signals contain 7 features, and the anomaly is judged by the residual error between the reconstructed data and the original data. The proposed method is compared with one-class support vector machine method and autoencoder method. The results show that the accuracy, precision and recall of the proposed method are significantly better than other methods. F1 score, AUC and confusion matrix also show that the proposed method is optimal. The proposed method achieves a detection effect superior to the existing methods with the combination of autoencoder and convolutional neural network.