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Decision Support Approach to Fleet Maintenance Requirements in the Aviation Industry

机译:航空业机队维护要求的决策支持方法

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This paper discusses a decision support approach in modeling jet engine life and predicting maintenance requirements for engine restoration. The removal characteristics are estimated by collecting field data based on the engine age and operating environment. This analysis includes an example of a jet engine application illustrating how the model predictions compare to actual events. In today's highly competitive market, airlines must find more effective ways to reduce costs because the expense of engine spare parts and restoration are steadily increasing. It is critical for airlines to understand how an engine will age towards its mature time-on-wing to determine budgets, shop capacities, and spare engine and spare parts inventories. In an effort to forecast removal rates of jet engines a model was developed that incorporated statistical information obtained from field data. The probability density function for engine removal classifications was estimated by applying a hazard model with consideration of censored field data. These functions, in conjunction with simulation, were used to forecast jet engine removal patterns. This methodology provided improved performance, based on data fit and forecasting accuracy, compared with traditional techniques such as autoregressive integrated moving average and linear regression.
机译:本文讨论了在建模喷气发动机寿命和预测发动机维修保养要求方面的决策支持方法。通过基于发动机寿命和工作环境收集现场数据来估算去除特性。此分析包括一个喷气发动机应用程序的示例,该示例说明了模型预测与实际事件的比较。在当今竞争激烈的市场中,航空公司必须找到更有效的降低成本的方法,因为发动机备件和维修的费用在稳步增长。对于航空公司来说,了解发动机如何适应其成熟的机翼运行时间至关重要,以确定预算,车间容量以及备用发动机和备件库存。为了预测喷气发动机的清除率,开发了一个模型,该模型结合了从现场数据获得的统计信息。发动机拆卸分类的概率密度函数是通过应用经过审查的现场数据的危险模型来估算的。这些功能与仿真一起用于预测喷气发动机的拆卸模式。与传统技术(例如自回归综合移动平均值和线性回归)相比,该方法基于数​​据拟合和预测准确性提供了改进的性能。

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