GOALI/Collaborative Research: Improved Spare Parts Inventory Management in Aircraft Engines through Hybrid Sensing

合作研究/GOALI：通过混合传感改进飞机发动机备件库存管理该GOALI奖的目的是发展基础科学和必要的工具，将现场大量机器设备的传感器测量转化为对维护该设备所需备件的可靠预测和库存政策。虽然设备健康监测与许多制造商相关，但这项研究的应用背景是商用喷气发动机。这项研究包括四个关键步骤：改进传感方法和信号解释，以诊断发动机状态；开发程序，将这些数据转换为大修时间和资源需求的预测；建立部件预测方法和库存政策，在考虑使用和经济条件的情况下，在现场发动机中汇总这些信息；以及创建一个用于监测和维护大型机队的模拟工具，以验证该方法。这项研究将建立一个对发动机维修过程进行建模和模拟的框架，并将产生可转化为对工业合作伙伴和整个航空航天行业具有实用价值的实用工具的理论结果，从而改进备件的预测和库存管理。虽然目前使用的传感器数据可以提供发动机整体健康状况的指示，但在这项研究中开发的先进传感技术提供了在开始大修之前预测哪些特定部件需要更换的潜力，从而提供了更多时间来确保必要的资源。这项研究将确定这些先进的传感技术带来的额外健康信息和改进决策的经济影响，并可能为它们的普及安装提供理由。虽然这些模型将主要在工业合作伙伴的业务部门内进行验证，但它们将有利于广泛的制造公司，对这些公司来说，售后服务是其业务的关键组成部分。

Abstract for Collaborative Research/GOALI: Improved Spare Parts Inventory Management in Aircraft Engines through Hybrid SensingThe objective of this GOALI award is to develop the basic science and necessary tools to transform sensor measurements from a large set of machine equipment in the field into reliable forecasts and inventory policies for the spare parts required to maintain that equipment. While equipment health monitoring is of relevance to many manufacturers, the application context of this research is commercial jet engines. The research consists of four key steps: advancing sensing methods and the interpretation of signals to diagnose engine condition; developing procedures for transforming this data into predictions of time-to-overhaul and resource-requirements; building part forecasting methods and inventory policies that aggregate this information across engines in the field, with consideration of usage and economic conditions; and creating a simulation tool for the monitoring and maintenance of a large fleet to validate the methodology. This research will establish a framework for modeling and simulating the process of engine maintenance, and will produce theoretical results that can be translated into applicable tools of practical value to the industrial partner and the aerospace industry at large, leading to improved forecasting and inventory management for spare parts. While currently-used sensor data can provide an indication of the overall health of the engine, the advanced sensing technologies developed in this research offer the potential to predict which specific parts will need replacement before an overhaul is initiated, thus providing more time to secure the necessary resources. This research will determine the economic impact of the additional health information and improved decision-making enabled by these advanced sensing technologies, and will potentially make the case for their pervasive installation. Although the models will primarily be validated within the industrial partner's business units, they will be beneficial to a wide array of manufacturing firms for whom after-sales service is a critical component of their business.