Research on Displacement Fault Diagnosis of Belt Conveyor Based on Improved DDNet
In response to the limitations and long time consumption for the belt displacement fault of belt conveyors in coal mines,this paper investigates the multi-source heterogeneous processing of fault data.Based on the data processing,a multi-source heteroge-neous data fault detection model is constructed by combining edge detection algorithm and improved deep detail network.Firstly,the edge detection algorithm is used to extract edge features from conveyor images,then combines the multi-source heterogeneous data,and recognizes the fault by the improved deep detail network,and then constructs the fault detection model.The results show that the average detection accuracy of the detection model in the processing of belt edge image data is 95.27％,which is 5.34％and 10.21％higher than the accuracy of the object detection algorithm and K-nearest neighbor classification algorithm.Meanwhile,the average image data recall rate of the simultaneous detection model is 93.46％,which is 4.09％and 7.18％higher than the recall rates of the object detection algorithm and K-nearest neighbor classification algorithm.The results show that the multi-source heterogeneous data fault detection model can significantly improve the reliability and robustness of belt displacement detection,and has important research value and practical application prospects.
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