Maintenance request prediction for airplanes based on multivariate damage model

Dao Zhong, Jing Feng, Quan Sun, Zhengqiang Pan, N. Yang
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Abstract

Field data is often used as the basis for the prediction of an airplanes maintenance request. In the traditional methods, maintenance request predictions are mainly obtained immediately using data. However, uncertainty analysis during the failure detection is ignored, which makes maintenance request inaccurate. To overcome the above problems, a novel approach is proposed in this paper: a multivariate damage model is established to obtain the degree of airplane damage, which is used as an indicator for maintenance request predictions. On the basis of the degree of damage, uncertainty analysis can be effectively described using a stochastic process and the Markov process. The transition probability and transition time corresponding to the potential detection rate and date of maintenance, which are used to determine the distribution of maintenance requests. Experiments are implemented based on field data of a certain type of airplane. Results confirm that the proposed method performs well in the predictions of maintenance requests.
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基于多变量损伤模型的飞机维修需求预测
现场数据经常被用作飞机维修需求预测的基础。在传统的方法中,维修需求预测主要是利用数据进行即时预测。然而,在故障检测过程中忽略了不确定性分析,导致维修要求不准确。为了克服上述问题,本文提出了一种新的方法:建立多变量损伤模型来获得飞机的损伤程度,并将其作为维修需求预测的指标。在损伤程度的基础上,利用随机过程和马尔可夫过程可以有效地描述不确定性分析。潜在检测率和维修日期对应的过渡概率和过渡时间,用于确定维修请求的分布。根据某型飞机的现场数据进行了试验。结果表明,该方法在维修需求预测方面具有较好的效果。
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