Researchon Rocker Fault Diagnosis System Based On Virtual Prototype Of Shearer

Based on the virtual prototyping experiment of coal mining machine,wavelet packet transform and neural network algorithm are used to decompose the signal and classify the corresponding model respectively.A multi-parameter complex mechanical fault diagnosis system is designed to make the equipment have a set of self before Fault diagnosis system.In this paper,the failure type parts of the shearer rocker gears,gear crack and bearing parts are fault cases,four idler shafts are used as signal acquisition points,and virtual prototypes are established in Adams model.The flexible part of the faulty part and the test axis is established,and the rigid and flexible virtual prototyping experiment is carried out in Adams.The axle and radial force data of each idler shaft are extracted as a sample to establish the fault diagnosis system.The wavelet coefficients of the data are decomposed by Coif4 wavelet,and the neural network input vector is constructed.The neural network model of load classification and fault classification is established by using Elman neural network to form the neural network model Case classification system.The virtual prototype is applied to the cutting part of the shearer.The fault diagnosis model of the neural network is established by using the force data of each idler shaft,and the verification test is carried out to verify the system reliability.