Fault diagnosis analysis and health management of thermal performance of multi-source data fusion equipment based on fog computing model

Abstract

Waste heat boiler will be restricted by the exhaust parameters of gas turbine, at the same time, it will affect the thermal characteristics of steam side, and its flue gas resistance will directly affect the power and efficiency of gas turbine cycle, which will have an important impact on the efficiency of combined cycle system. Therefore, it is necessary to monitor the running status of the equipment in time, identify the early signs of faults, and make accurate judgments on fault location, fault degree and development trend, so as to improve the reliability and availability of the unit. The thermal system is the main part of thermal power plant production, so the fault diagnosis of this part is particularly important. In this paper, a method of thermal performance fault diagnosis and health management for multi-source data fusion equipment based on fog computing model is proposed. Using the theory of multi-source data fusion analysis, the qualitative values of the parameters of the fog computing model are marked, and the causes of the failure of the failure variables are obtained. Complete the fault subspace identification, and comprehensively evaluate the equipment status according to multi-attribute decision. This method is conducive to the accurate identification of early faults and the accurate judgment of fault degree and fault trend