To enhance the whole process safety of fan operations and ensure accurate fault diagnosis and long-term production income of thermal power plants

predicting these risk issues is crucial to enhance the safety of the unit.In this paper

we proposed a fan fault diagnosis model of big data platform that integrates multilayer perceptron and polynomial fitting. The fan early warning model was established by multilayer perceptron and polynomial fitting modeling technology

and integrated into the big data platform to find abnormalities which were difficult to find manually during the operation of the fan. By combining data mining with mechanism analysis and feature value knowledge base

the parameters boundary information of fan stall could be excavated

the stall boundary conditions of the fan were accurately configured under various working conditions

and a stall boundary condition diagram was created. By combining those informations with normal operating conditions

the early stall zone can be obtained. Finally

a fault diagnosis model that covers the entire working condition of the fan can be established.Utilizing the comprehensive big data platform that covers

circulates

and maintains fan operation data

a system of intelligent fan patrol model was constructed. The intelligent patrol disk model which replaces the operator was then used to monitor and diagnose the fan running state regularly

which can achieve accurate and safe diagnosis of fan faults

minimize the fault incidence and maximize the personnel reuse rate.