基于注意力卷积胶囊网络的电液比例伺服阀故障诊断
收稿日期: 2024-03-16
修回日期: 2024-04-09
录用日期: 2024-05-11
网络出版日期: 2024-06-03
基金资助
国家重点研发计划(2021YFB2011300);国家自然科学基金(52205062);江苏省自然科学基金(BK20220950);中央高校基本科研业务费专项资金(30922010701);中国博士后科学基金(2022M711624);流体动力与机电系统国家重点实验室开放基金(GZKF-202218);机械传动国家重点实验室开放基金(SKLMT-MSKFKT-202220);智控实验室开放基金(ICL-2023-0305)
Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks
Received date: 2024-03-16
Revised date: 2024-04-09
Accepted date: 2024-05-11
Online published: 2024-06-03
Supported by
National Key R&D Program of China(2021YFB2011300);National Natural Science Foundation of China(52205062);Natural Science Foundation of Jiangsu Province(BK20220950);Fundamental Research Funds for the Central Universities(30922010701);China Postdoctoral Science Foundation(2022M711624);Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(GZKF-202218);the State Key Laboratory of Mechanical Transmissions(SKLMT-MSKFKT-202220);the Open Fund of Intelligent Control Laboratory(ICL-2023-0305)
胡渊豪 , 宋艺博 , 刘家辉 , 赵孝礼 , 邓文翔 , 胡健 , 姚建勇 . 基于注意力卷积胶囊网络的电液比例伺服阀故障诊断[J]. 航空学报, 2024 , 45(15) : 630407 -630407 . DOI: 10.7527/S1000-6893.2024.30407
To address the issues of unknown parameters and difficult modeling in current model-based proportional servo valve fault diagnosis methods， we propose the Attention Convolution Capsule Network （ACCN） algorithm for the first time and design a new proportional servo valve fault diagnosis method based on this network. This algorithm first uses convolutional neural networks and an efficient channel attention mechanism to extract and fuse features from the original multi-channel one-dimensional time-domain signals. The fused results are then input into the subsequent capsule network for further feature extraction and fault classification， yielding the corresponding diagnosis results. The effectiveness of the proposed algorithm and diagnostic method was validated on a proportional servo valve fault simulation test bench. The proposed method achieved 100% accuracy on the test set， and the inclusion of the channel attention mechanism and capsule network enhanced the diagnostic performance of the model to varying degrees.
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