Application of deep learning in the hydraulic equipment fault diagnosis

摘要

In this paper, combined with the advantages of deep learning and the characteristics of big data equipment to put forward the intelligent fault diagnosis of Diamond Model. In this model, firstly, the data is extracted by the Sparse Autoencoder network and completed the feature extraction; and then the extracted feature vector combined with Softmax classifier is used to classify the equipment fault condition; finally the whole model uses the back propagation algorithm to fine tune the whole network parameters to obtain the optimal parameters that minimize the value of the loss function; ultimately, realized the model of adaptive extraction of fault features and fault conditions of intelligent diagnosis. And through simulation analysis, the accuracy rate of the classification of the DM model is verified to reach 81%, which is significantly higher than the shallow classifier and has a good self-learning ability and the level of knowledge learning ability.