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On-line Access: 2022-05-10

Received: 2021-08-17

Revision Accepted: 2021-12-22

Crosschecked: 2022-05-11

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yan REN

https://orcid.org/0000-0003-4257-2923

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Journal of Zhejiang University SCIENCE A 2022 Vol.23 No.4 P.257-271

http://doi.org/10.1631/jzus.A2100394


Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor


Author(s):  Jin-chuan SHI, Yan REN, He-sheng TANG, Jia-wei XIANG

Affiliation(s):  College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China

Corresponding email(s):   rentingting211@wzu.edu.cn

Key Words:  Hydraulic directional valve, Internal fault diagnosis, Weighted multi-dimensional features, Multi-sensor information fusion


Jin-chuan SHI, Yan REN, He-sheng TANG, Jia-wei XIANG. Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor[J]. Journal of Zhejiang University Science A, 2022, 23(4): 257-271.

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author="Jin-chuan SHI, Yan REN, He-sheng TANG, Jia-wei XIANG",
journal="Journal of Zhejiang University Science A",
volume="23",
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pages="257-271",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2100394"
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%T Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor
%A Jin-chuan SHI
%A Yan REN
%A He-sheng TANG
%A Jia-wei XIANG
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2100394

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T1 - Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor
A1 - Jin-chuan SHI
A1 - Yan REN
A1 - He-sheng TANG
A1 - Jia-wei XIANG
J0 - Journal of Zhejiang University Science A
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DOI - 10.1631/jzus.A2100394


Abstract: 
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise, the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it. Therefore, a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments. Firstly, the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network (CNN) to form a complete and stable multi-dimensional feature set. Secondly, to obtain a weighted multi-dimensional feature set, the multi-dimensional feature sets of similar sensors are combined, and the entropy weight method is used to weight these features to reduce the interference of insensitive features. Finally, the attention mechanism is introduced to improve the dual-channel CNN, which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors, to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis. Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy. It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods. This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.

基于多传感器多维特征加权自适应融合的液压换向阀故障诊断

作者:施锦川,任燕,汤何胜,向家伟
机构:温州大学,机电工程学院,中国温州,325035
目的:通过研究提出一种自适应融合多传感器信息的故障诊断方法,以解决故障信息不足和冗余问题。
创新点:1.异构传感器信息融合方法增强了特征集的故障表达能力,从而可以表征多种故障类型(电磁故障和机械故障;2.个性化加权方法(熵权法和注意力机制的使用)增强了有效信号,削弱了干扰源;3.提出了一种多样化的特征提取方法,获取的多维特征集具有健壮完整的健康信息。
方法:1.通过多类型传感器信息融合,获取丰富的故障信息;2.基于熵权法、注意力机制自适应选择故障敏感故障特征。
结论:1.所提方法为液压换向阀内部故障诊断提供了技术支持,其最高平均准确率可以达到99.82%;2.采用该方法在液压换向阀多传感器信息融合过程中具有自适应性,既能充分利用故障类别的敏感信息,又有助于减少冗余信息的干扰。

关键词:液压换向阀;内部故障诊断;加权多维特征;多传感器信息融合

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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