摘要
As the growing requirements for the stability and safety of process industries,the fault detection and diagnosis of pneumatic control valves have crucial practical significance.Many of the approaches were presented in the literature to diagnose faults through the comparison of residual sequences with thresholds.In this study,a novel hybrid neural network model has been developed to address the issue of pneumatic control valve fault diagnosis.First,the feature extractor automatically extracts in-depth features of the signals through multi-scale convolutional neural networks with different kernel sizes,which not only adequately explores the local distinguishable features,but also takes into account the global features.The extracted features are then fused by the feature fusion layer to reduce redundant features.Finally,the long short-term memory for fault identification and the dense layer for fault classification.Experimental results demonstrate that the average test accuracy is above 94%and 16 out of the 19 conditions can be successfully detected in the simulated actual industrial environment.The effectiveness and practicability of the proposed method have been verified through a comparative analysis with existing intelligent fault diagnosis methods,and the results suggest that the developed model has better robustness.
基金
funded by the“Ningxia Key Research and Development Project”,grant number“2022BEE02002”.
作者简介
HAO Hongtao received the M.E.degree in vehicle engineering and the Ph.D.degree in mechanical engineering from Shanghai Jiao Tong University,Shanghai,China,in 2006 and 2015,respectively.He is currently an Associate Professor with the College of Mechanical Engineering,Ningxia University.He has authored or coauthored more than 20 journal articles.His main research interests include deep learning,fault diagnosis,vehicle advanced transmission,etc.E-mail:haoht_03@126.com;WANG Kai received the B.E.degree in mechanical engineering from the Hefei University of Technology,Hefei,China,in 2021.He is currently a M.Sc.candidate with Ningxia University,Yinchuan,China.His main research interests include deep learning and fault diagnosis.E-mail:kaik_01@126.com
