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摘要: 为有效利用监控大数据准确识别民机发动机性能的状态并预测其性能退化过程，针对民机发动机故障数据偏少且其性能退化过程呈现多阶段退化特性，提出了一种考虑民机发动机性能参数阶段不确定退化特性的可靠性评估模型；通过动态自适应窗宽改进了基于贝叶斯信息准则(BIC)的变点检测模型，利用改进自适应窗宽的BIC识别了自适应变点；根据识别出的自适应变点分阶段建立了不确定Liu过程的分布函数模型，结合民机发动机性能退化过程首达阈值的数学性质进行了可靠性评估，并通过对比民机发动机性能退化样本数据验证了模型的准确性和优越性。分析结果表明：利用改进自适应窗宽的BIC变点检测模型识别民机发动机性能退化过程的变点后，采用变点描述分阶段退化过程的均方误差为5.8×10-28，在自适应窗宽具有不同变化规律的条件下，识别出的变点无明显变化，说明模型能够准确识别民机发动机性能退化过程的自适应变点，且具有较强的稳健性；利用阶段不确定Liu过程分析民机发动机性能参数的动态退化规律时，其平均评估误差比原Liu过程模型降低了约23.69%，得到的可靠性评估结果更加准确，且改进模型由于具有Lipschitz连续性，能够准确预测未来某段时间内民机发动机性能退化过程的可靠性水平。由此可见，建立的改进可靠性评估模型能够为实际工程中民机发动机的性能状态监控与健康管理应用提供理论方法。Abstract: To effectively utilize the monitoring big data to accurately identify the performance status and predict the performance degradation process of civil aircraft engines, a reliability evaluation model considering the uncertain degradation characteristics of civil aircraft engine performance parameters in different stages was proposed, so as to address the lack of fault data and the multi-stage degradation characteristics of civil aircraft engines during the performance degradation process. The change point detection model based on the Bayesian information criterion (BIC) was improved by the dynamic adaptive window width, and the adaptive change point was identified by the improved BIC with adaptive window width. Based on the identified adaptive change points, a distribution function model of the uncertain Liu process was established in stages. The reliability evaluation was conducted according to the mathematical properties of the first threshold of the performance degradation process of civil aircraft engines. In addition, the accuracy and superiority of the model were verified by comparing the sample data of performance degradation of civil aircraft engines. Analysis results indicate that the mean square error of degradation process in stages described by the change points is 5.8×10-28 after identifying the change points of performance degradation process of civil aircraft engines by using the BIC change point detection model with the improved adaptive window width. Under the conditions that the adaptive window width has different changing rules, the identified change points have no obvious change. Therefore, the model can accurately identify the adaptive change points of performance degradation process of civil aircraft engines and has strong robustness. When the uncertain Liu process in stages is used to analyze the dynamic degradation law of performance parameters of civil aircraft engines, the average evaluation error reduces by about 23.69% compared with the original Liu process model, and the reliability evaluation results are more accurate. Due to the Lipschitz continuity, the improved model can accurately predict the reliability level of performance degradation process of civil aircraft engines in a certain period of time in the future. It can be seen that the established improved reliability evaluation model can provide theoretical methods for the performance status monitoring and health management application of civil aircraft engines in practical engineering.
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图 5 EGTM分布函数方差变化曲线
Figure 5. Change curves of variances of EGTM distribution functions
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