An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function

Abstract

Due to the complex underground environment, pumping machines are prone to producing numerous failures. The indicator diagrams of faults are similar to a certain degree, which produces indistinguishable samples. As the samples increase, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. To accurately and quickly judge the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the Rectifying linear unit activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared to the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity and can improve the generalization performance of the deep learning model and the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well-embedded into other neural networks.