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

该奖项的目的是开发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结果转化为可靠的预测和维护设备所需备件的库存政策。虽然设备健康监测与许多制造商有关，但这项研究的应用背景是商用喷气发动机。该研究包括四个关键步骤：先进的传感方法和信号的解释，以诊断发动机状况;开发程序，将这些数据转化为预测的时间，以大修和资源需求;建立部分预测方法和库存政策，汇总这些信息在该领域的发动机，考虑使用和经济条件;并创建一个模拟工具，用于大型车队的监控和维护，以验证该方法。这项研究将建立一个框架，用于建模和模拟发动机维修过程，并将产生理论结果，可以转化为实用价值的工业合作伙伴和航空航天工业的适用工具，从而改善预测和库存管理的备件。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，但本研究中开发的先进传感技术提供了在大修开始之前预测哪些特定部件需要更换的可能性，从而提供更多的时间来确保必要的资源。这项研究将确定这些先进传感技术所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企业，对这些企业来说，售后服务是其业务的关键组成部分。

The objective of this Grant Opportunities for Academic Liaison with Industry (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.