KEYWORDS: Data modeling, Machine learning, Instrument modeling, Education and training, Algorithm development, Performance modeling, Process modeling, Engineering, Data processing
In order to predict the key health parameters of complex equipment, this paper constructs an ensemble learning model based on LightGBM with a data-driven idea. Taking the hydraulic system as a typical research object, we have realized the prediction of its flow parameters. Under multiple working conditions, the R2 reached 0.9988, and the prediction accuracy and time cost were excellent. Finally, using the relevant tool chain, we deploy the prediction model in the embedded environment to verify the effectiveness of the method.
At present, the structure of electronic system is more and more complex, and the function is more and more important. However, the degradation state of electronic products has no obvious external performance, and there are sudden and random faults. The development of fault diagnosis technology of electronic products is relatively slow. In view of the above problems, this paper proposes a complex electronic system health management method, which defines the modules such as state management, execution management, NVRAM manager and watchdog manager to define the functions and cross-linking relationships of each component, which realizes the health management of complex electronic system under the joint supervision of software application and hardware electronic equipment.
With the development of big data, artificial intelligence and other technologies, data-driven aviation equipment fault diagnosis and prediction technology has gradually become a research hotspot in the aviation field. Many typical intelligent algorithm models have been applied to this field. However, limited by the airborne embedded computing environment, there are still some problems in the deployment of intelligent prediction models represented by deep neural networks on aircraft. This paper summarizes and analyzes the research and application of typical deep neural networks such as convolutional neural networks in the field of aircraft fault diagnosis and prediction. Facing the airborne embedded environment, the current difficulties in deploying the deep neural network algorithm model in the airborne environment are analyzed. The development direction of the application of fault prediction and diagnosis algorithms represented by neural networks in the future is discussed.
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