Zhang, F.; Li, Y.; Shan, D.; Liu, Y.; Ma, F. An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function. Processes2023, 11, 2540.
Zhang, F.; Li, Y.; Shan, D.; Liu, Y.; Ma, F. An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function. Processes 2023, 11, 2540.
Zhang, F.; Li, Y.; Shan, D.; Liu, Y.; Ma, F. An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function. Processes2023, 11, 2540.
Zhang, F.; Li, Y.; Shan, D.; Liu, Y.; Ma, F. An Improved Fault Diagnosis Approach for Pumps Based on Neural Networks with Improved Adaptive Activation Function. Processes 2023, 11, 2540.
Abstract
Due to the complex underground environment, pumping machines are prone to produce numerous failures. The indicator diagrams of faults are similar in a certain degree, which produces indistinguishable samples. As the samples increases, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. For accurately and quickly judging 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 ReLU 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 with 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, can improve the generalization performance of deep learning model, the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well embedded in other neural networks.
Keywords
Deep learning; fault diagnosis; adaptive activation function; pumping unit
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
