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首页> 外文期刊>Journal of Engineering for Gas Turbines and Power >A Hybrid Prognostic Model Formulation and Health Estimation of Auxiliary Power Units
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A Hybrid Prognostic Model Formulation and Health Estimation of Auxiliary Power Units

机译：辅助动力装置的混合预测模型制定和健康估计

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摘要

Prognostic health monitoring is an important element of condition-based maintenance and logistics support. The accuracy of prediction and the associated confidence in prediction greatly influence overall performance and subsequent actions either for maintenance or logistics support. Accuracy of prognosis is directly dependent on how closely one can capture the system and component interactions. Traditionally, such models assume a constant and univariate prognostic formulation-that is, components degrade at a constant rate and are independent of each other. Our objective in this paper is to model the degrading system as a collection of prognostic states (health vectors) that evolve continuously over time. The proposed model includes an age dependent deterioration distribution, component interactions, as well as effects of discrete events arising from line maintenance actions and/or abrupt faults. Mathematically, the proposed model can be summarized as a continuously evolving dynamic model, driven by non-Gaussian input and switches according to the discrete events in the system. We develop this model for aircraft auxiliary power units, but it can be generalized to other progressive deteriorating systems. The system identification and recursive state estimation scheme for the developed non-Gaussian model under a partially specified distribution framework has been deduced. The diagnostic/prognostic capabilities of our model and algorithms have been demonstrated using simulated and field data.
机译：预后健康监测是基于状况的维护和后勤支持的重要组成部分。预测的准确性和相关的预测信心会极大地影响整体性能以及后续维护或后勤支持措施。预后的准确性直接取决于人们能否捕捉到系统和组件之间的相互作用。传统上，此类模型采用恒定且单变量的预测公式-也就是说，组件以恒定的速率退化并且彼此独立。我们在本文中的目标是将退化系统建模为随时间连续发展的预后状态（健康向量）的集合。所提出的模型包括与年龄有关的劣化分布，组件相互作用以及线路维护操作和/或突然故障引起的离散事件的影响。从数学上讲，所提出的模型可以概括为一个不断发展的动态模型，该模型由非高斯输入和开关根据系统中的离散事件驱动。我们为飞机辅助动力装置开发了该模型，但是它可以推广到其他渐进式恶化系统。推导了在部分指定的分配框架下，所开发的非高斯模型的系统识别和递归状态估计方案。我们的模型和算法的诊断/预后能力已使用模拟和现场数据进行了证明。

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