A Deep Learning Model with Adaptive Learning Rate for Fault Diagnosis

With the increasing amount of data in the field of equipment fault diagnosis, deep learning is playing an increasingly important role in the process of fault diagnosis, during which the timeliness requirement is high and the fault diagnosis results need to be obtained accurately and timely. However, with the increase of network layers, the training time of deep learning model becomes longer. Learning rate in the deep learning model plays an important role in the process of model training, and a well-designed learning rate adjustment strategy can effectively reduce the training time and satisfy the requirements of fault diagnosis. At present, some deep learning models usually adopt a globally uniform learning rate strategy, which is unreasonable for different parameters. This paper has designed an adaptive learning rate strategy for the parameters of weight and bias respectively in deep learning model. Specifically, the strategy contains a learning rate strategy based on stochastic gradient descent method for weight, and a power exponential learning rate strategy for bias. Experiments are carried out to validate the effectiveness of proposed learning rate strategy. Results suggest that the strategy can reduce the training time and reconstruction error rate of deep learning model, and improve the classification accuracy of fault diagnosis.