Abstract
In this paper, the fault diagnosis method of Integrated Neural Network based on oil and Vibration information fusion is studied and applied to Aero-engine Fault Diagnosis. Then, taking an CFM56-3 Aero-engine as an example, the application of Integrated Neural Network Fault Diagnosis method in bearing fault diagnosis of Aero-engine is studied by using the idea of Vibration information and oil information Fusion Diagnosis, and the diagnosis method is validated with specific data. The diagnosis results show that compared with the traditional single information source Fault Diagnosis method, the integrated neural network Fault Diagnosis method is more efficient, can detect more fault modes, and has lower misdiagnosis rate.
Highlights
Aero-engine, is a highly complex and sophisticated thermal machine, can provide the power required for flight engines for aircraft
For a certain type of Aero-engine in the field of civil aviation, the main fault diagnosis methods are based on the lubricating oil information and vibration information [1]
This paper studies the application of this method to Aero-engine fault diagnosis
Summary
Aero-engine, is a highly complex and sophisticated thermal machine, can provide the power required for flight engines for aircraft. As the heart of the aircraft, it is known as the “flower of industry”, and directly affects the performance, reliability and economy of the aircraft. It is an important manifestation of the strength of a country’s science, technology, industry and national defense. Because of the complex structure and various parts of Aero-engine, the fault diagnosis of Aero-engine has always been a difficult problem in the field of aero-intelligent maintenance. For a certain type of Aero-engine in the field of civil aviation, the main fault diagnosis methods are based on the lubricating oil information and vibration information [1]. In order to solve these problems effectively, the integrated neural network diagnosis technology emerged as the times require. This paper studies the application of this method to Aero-engine fault diagnosis
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