Architecture Design of Intelligent Diagnosis System for Airborne Electronic Products Based on Big Data

Intelligent diagnosis methods based on big data has been widely used in the field of machinery, which plays an important role. However, the application process of intelligent diagnostic methods in airborne electronic products is relatively slow. Aiming the requirements of fault diagnosis and the typical problems in engineering practice, a diagnosis system architecture of multi-intelligence model is defined, which is realized by constructing support vector machine (SVM), deep belief network (DBN), long short-term memory neural network (LSTM), unscented particle filter (UPF) and random process (RP) models with big data. The architecture can support condition based maintenance and meet the needs of online status analysis and remaining life prediction. According to the case of airborne electronic products, software development was implemented to prove the application effect of this framework in the field of airborne electronic product maintenance.