The quality status of aircraft use involves multiple indicators and has nonlinear correlations. The commonly used traditional evaluation methods often rely heavily on expert knowledge and cannot depict the nonlinear relationship of indicators, making it difficult to accurately and effectively support mission aircraft usage decisions. This article constructs one mission aircraft usage quality status indicator system that includes annual usage plan execution rate, matching degree of aircraft lifespan usage, variability of the remaining lifespan echelon difference, rate of satisfaction with aviation materials supply, and aircraft failure free rate. Wavelet Neural Network and BP Neural Network models are respectively used to predict and evaluate the quality status. Case validation shows that Wavelet Neural Network have faster data learning speed and higher prediction accuracy compared to BP Neural Network, which are basically consistent with actual results and can provide more accurate quantitative basis for mission aircraft usage decision-making.
There are many health status parameters for aeroengine, leading to partly information overlap. The accuracy of commonly used evaluation methods is seriously restricted, due to the reliance on human subjective experience. The PCA-Kmeans combination algorithm for aeroengine health status evaluation is constructed, the specific steps are proposed, and the result is evaluated and verified by RBF neural network. Taking NASA public dataset as an example, the experimental results suggest that the PCA-Kmeans combined algorithm is well suited to health status clustering based on PCA dimension reduction, and is basically consistent with the evaluation results of RBF neural network. It provides a reference for large scale objective evaluation of aeroengine health status, comprehensively mastering the overall performance degradation of engine and scientifically making maintenance decisions.
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