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

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.