ABSTRACT

This paper addresses the problem of Fault Classification and Prediction (FCP) without sufficient data in aircraft maintenance. An innovative approach based on Agent-Based Simulation System (ABSS) and the Fuzzy-Rough Nearest Neighbour (FRNN) method is proposed. To do so, a framework for integrating the FRNN approach into ABSS is firstly provided. The concept and architecture models of FRNN-ABSS are then used to describe the FRNN-ABSS system. Subsequently, random and sequence strategies are designed for the FCP of the engine. An algorithm for integrating the FRNN method into ABSS is also developed to automate FCP. Finally, the experiments analysing the impact of different strategies on maintenance costs and service level have been conducted. The results show that the approach proposed has achieved success for random and sequence strategies: 9.3% and 2.5% of maintenance costs have been saved; 4.17% and 12.5% of delayed flights have been changed into on-time flights.

摘要

本文解决了飞机维修中没有足够数据的故障分类和预测 (FCP) 问题。提出了一种基于Agent-Based Simulation System (ABSS) 和Fuzzy-Rough Nearest Neighbor (FRNN) 方法的创新方法。为此，首先提供了将 FRNN 方法集成到 ABSS 中的框架。然后使用 FRNN-ABSS 的概念和架构模型来描述 FRNN-ABSS 系统。随后，针对发动机的 FCP 设计了随机和序列策略。还开发了一种将 FRNN 方法集成到 ABSS 中的算法，以实现 FCP 的自动化。最后，进行了分析不同策略对维护成本和服务水平影响的实验。结果表明，所提出的方法在随机和序列策略上取得了成功： 9. 节省了3%和2.5%的维护成本；4.17% 和 12.5% 的延误航班已改为准点航班。