Aerospace Intelligent Equipment System Based on Embedded Heterogeneous Design for PHM

This paper proposes research on the prediction and health management system (PHM) of aircraft conditional maintenance based on massive data mining. This paper reviews the development history and development status of PHM technology in foreign aircraft engines, fixed-wing aircraft, helicopters, aerospace vehicles, ships, vehicles and rail transit equipment, and then according to the status quo, from the perspectives of system structure, service environment, data sources and storage methods, etc. The application requirements of new generation aircraft for massive data mining are analyzed. Thirdly, based on the data mining work of prediction and health management, this paper proposes a device life prediction method based on Gaussian Hidden Markov Model. The method is divided into an offline stage and an online stage. The offline stage extracts sensor data features and trains model parameters. The second stage then utilizes the model to assess the current state of device health. Finally, this paper proposes an embedded heterogeneous design aircraft PHM massive data mining platform. And designed the overall framework and hardware and software structure of the platform. Finally, this paper takes the aircraft bearing as an example to verify the effectiveness of the algorithm and data platform proposed in this paper. The experimental simulation shows that the data mining efficiency of the system is high, and the accuracy of the equipment life prediction method is as high as 95%. The platform has great military application significance for promoting the integration and engineering realization of aircraft PHM.