Design of Fault Prediction System for Electromechanical Sensor Equipment Based on Deep Learning

With the increasing complexity, scale, and intelligentization of modern equipment, the maintenance cost of equipment is increasing day by day. Moreover, once an unexpected major failure occurs, it will cause loss and damage to production, economy, and safety. Based on the considerations of system reliability and safety, fault prediction has gradually become a hot topic in the field of reliability. As a new branch of machine learning, deep learning realizes deep abstract feature extraction and expression of complex nonlinear relations by stacking deep neural networks and makes its methods solve bad problems in many traditional machine learning fields. The improvement and excellent results have been achieved. This article first introduces the model structure and working principle of the classic deep learning model noise reduction autoencoder and combines the feature extraction results of the experimental data of electromechanical sensor equipment and the model characteristics to analyze that this type of model can be trained using only normal samples. Under the restriction, the reason for abnormal features can also be correctly filtered. Then, in order to suppress the overfitting of training, the dropout layer is used in this article. The dropout layer will make the hidden layer nodes to be dropped with probability p according to the set probability. Because the lost nodes are random, each training is equivalent to training a new model. It achieves an effect similar to independently training the model and then superimposing it. Experiments prove that the dropout layer is very effective in solving overfitting. Finally, this paper conducts experimental verification on the improved algorithm. The results show that the improved model has a certain improvement in accuracy under the limited training algebra. Under the same training parameters, the accuracy is increased by approximately 2.44%, and the improved model has a better training effect and can be used for electromechanical effective prediction of sensor equipment failure.