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

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第4期23卷 257-271页

作者： Jin-chuan SHI Yan REN He-sheng TANG Jia-wei XIANG College of Mechanical and Electrical Engineering Wenzhou UniversityWenzhou325035China

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 ***,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 ***,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 ***,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 ***,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 *** results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information *** can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional *** proposed method can achieve high fault-diagnosis accuracy under severe working conditions.

关键词： Hydraulic directional valve internal fault diagnosis Weighted multi-dimensional features Multi-sensor information fusion

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