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


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",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%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
%V 23
%N 4
%P 257-271
%@ 1673-565X
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2100394

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
VL - 23
IS - 4
SP - 257
EP - 271
%@ 1673-565X
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2100394

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.




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