Research On The Application Information Fusion Technology To Aero-engine Intelligent Monitoring

Aero-engine seems as the heart of the aircraft, whose work reliability plays a direct importance role in the entire aircraft flight safety. So it's very important to monitor its operating status. Because of the complex structure of the aero-engine, poor working environment, the large quantity and high chance of the failure of the aero-engine, we must grasp the real-time operational status of aero-engine parts, positioning of parts of engine failure timely and accurately and identifying the failure quickly, which reduce aircraft downtime and improve the aircraft's attendance. So that ensures the quality of aircraft operation. Currently, the aero-engine diagnostic techniques develop towards real time, intelligent, systematic, early, network, that a single diagnostic technique can not meet the requirements of the development of aviation industry. Therefore, researchers keep innovation the technical of the aero-engine condition monitoring and fault diagnostic, make full use of collected information and combine a variety of diagnostic techniques effectively. The result is comprehensive and timely monitoring of aircraft engines and efficient real-time diagnostic, which provide technical support to the development of the aviation industry.Based on the wear particles recognition and energy spectrum analysis technologies, this paper research on the aero-engine condition monitoring and fault diagnostic, using the intelligent methods such as BP neural network, grey theory, fuzzy theory. Firstly, the wear particles recognition through gray correlation, neural network analysis on the two-dimensional particle size. At the same time, it receives the metal element concentration by energy spectrum analysis, and draws up the Metal concentration limits value table, and fault recognition using neural network. Secondly, using fuzzy-experts theory, the integration of both analysis technologies build intelligent diagnostic model. The accuracy of the model was demonstrated. Engine failure was predicted by gray theoretical. Finally, the software of aero-engine intelligent monitoring system based on information fusion technology is developed preliminarily.