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

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