Fault diagnosis on cutting unit of mine roadheader based on PSO- BP neural network

In order to improve an efficiency and accuracy of the fault diagnosis for the cutting unit of the mine roadheader, based on the vibration acceleration sign al of the cutting unit on the mine roadheader as the study object, the vibration acceleration data of the cutting unit on the mine roadheader were collected from an under ground mine and the feature vectors to represent the operation status of the cutting unit on the mine roadheader were analyzed and picked up. The BP neural network was applied as the fault diagnosis method. The rapid convergence and overall discovery capacity of the PSO algorithm was applied directly to the optimization on the W eight threshold value of the BP neural network and the slow convergence speed of the BP neural network and the easy falling in the local minimum problem were solve d.With the training and test conducted on the data samples, a PSO-BP neural network was established to diagnose the cutting unit whether or not in fault and the diagn osis was conducted on the fault whether or not occurred in the cutting unit of the EBZ-1 60 roadheader. The test results showed that in comparison with the optimized B P neural network ( FBP neural network) of the rapid BP method, the PSO-BP neural network would have higher diagnosis accuracy and the training steps would be les s. The method could accurately and effectively diagnose the fault of the cutting unit on the roadheader and could provide a new method and new idea to the study on th e fault diagnosis of the cutting unit on the roadheader.