Abstract:Due to the characteristics of high efficiency, cleanliness and flexibility, gas turbines have been rapidly developed in the fields of electric power, aerospace and urban thermal heating. However, the health degree of gas turbine equipment will deteriorate because of many influencing factors such as complex structure, frequent start and stop, and variable working conditions. Therefore, the research on the health assessment technology of gas turbine equipment has become increasingly important. With the progress of sensor technology and the new generation of information technology, the data-driven health assessment technology has been greatly developed. However, the current predictive diagnosis model has some problems, such as high complexity and large data storage demand, which increase the burden of system operation. Therefore, on the basis of in-depth analysis of relevant theories, this paper developed a real-time evaluation system of gas turbine health status based on historical data through the operation of relevant model functions and the establishment of evaluation processes, so as to reduce the operating pressure of the intelligent prediction and diagnosis system and improve the real-time monitoring capability. Finally, the stability and feasibility of the developed system are verified by an example, which provides a new development idea for gas turbine equipment health assessment technology.