On-line Fault Diagnosis Model Construction and Real-time Analysis of Rotary Crusher in Metal Open-pit Mine
Ore crushing is an important link in mining production process of open-pit mines. As the large-scale device for the crushing of open-pit mines,in general,gyratory crusher′s failure diagnosis is relatively complicated and it′s hard to make accurate and rapid diagnosis based on subjective experience. Aiming at the failure diagnosis problem of gyratory crusher,real-time data collection is carried out through sensor and the failure diagnosis model is built up based on the improved BP neural network. Taking eccentric bushing failure,bearing wear,oil storage of parallel axis and abnormal oil temperature of parallel axis as the failure types and taking reflux oil temperature,lubricating oil pressure,bearing vibration frequency and revolving speed of bearing as the failure feature parameters,the training and optimization for the failure diagnosis model based on BP neural network are made by utilizing the known failure types and failure sample data. Finally,the optimized failure diagnosis model of gyratory crusher was verified according to the test data. The result indicated that the failure diagnosis model based on BP neural network could effectively judge the real-time failure state of gyratory crusher and the prevention-oriented failure diagnosis model could be realized,which satisfied the failure diagnosis demand of the large-scale gyratory crusher for open-pit mines.
Open pit mineRotary crusherFault diagnosisBP neural network
NETL
NSTL
万方数据
维普
