Fault diagnosis of spraying workshop based on BP neural network

Abstract In this paper, for the failure of spraying workshop door switch, exhaust fan and other faults in workshop, a fault diagnosis algorithm based on BP (Back Propagation) neural network is proposed to solve problem workshop. First, sensor was built. Multi-source sensors were used realize dynamic perception on-site environment equipment status. Through preprocessing multi-source heterogeneous data, unified data template interface established. Secondly, simulate diagnose possible process. Finally, MATLAB test simulation proves its high accuracy diagnosis.