Development of Input Selection Methods for Predictive Modelling in the Health Monitoring of Gas Turbine Engines

本研究项目的目的是开发有效的输入选择方法，用于预测建模应用程序的一部分，工业合作伙伴（TECSIS公司）正在进行的项目，处理的健康监测的量化的燃气涡轮机（GT）发动机使用数据分析工具。TECSIS提供产品开发和研发服务，并在预测和健康管理（PHM）系统开发方面拥有积极的研究组合。展望未来，作为其持续产品进步的一部分，TECSIS需要一种方法来自动选择对排气温度（EGT）和功率等输出有重大影响的最主要输入，这些输出是燃气轮机健康监测的主要指标。拟议的输入选择方法将由滑铁卢大学的研究团队与工业合作伙伴的技术专家和工程师密切合作，利用先进的机器学习技术开发。建议的输入选择方法的好处包括提高预测精度，更快，更具成本效益的predictivemodels，更好地解释所构建的模型，并节省成本的下一轮数据收集dueto涉及较少的输入。这些方法也有显着的影响，开发预测建模，分类和聚类应用程序在其他机械，电气和软件系统，TECSIS工程。将所提出的输入选择方法应用于预测建模和其他模式识别工具中，将有助于TECSIS扩展其应用领域。该项目的成功将使工业合作伙伴能够创造新的创收来源，并接触到新的客户。

The objective of this research project is to develop efficient input selection methods intended for predictivemodelling applications as part of industrial partner's (TECSIS Corporation) ongoing projects that deal with thequantification of the health monitoring of a gas turbine (GT) engine using data analytics tools. TECSISprovides product development and research and development services, and has an active research portfolio inPrognostics and Health Management (PHM) system development. Going forward as part of their continuousproduct advancements, TECSIS needs a methodology to automatically select the most dominant inputs thathave significant influence on outputs like exhaust gas temperature (EGT) and power which are major indicatorsfor health monitoring of gas turbines. The proposed input selection methods will be developed utilizingadvanced machine learning techniques by the research team from the University of Waterloo in closecollaboration with the technical experts and engineers from the industrial partner. The benefits of the proposedinput selection methods include improved prediction accuracy, faster and more cost-effective predictivemodels, better interpretations of constructed models, and cost savings on the next round of data collection dueto fewer inputs involved. These methods also have significant implications for developing predictive modeling,classification, and clustering applications in other mechanical, electrical, and software systems that TECSISworks in. Incorporation of the proposed input selection methods into its predictive modeling and other patternrecognition tools will help TECSIS to expand its applications areas. The success of this project will enable theindustrial partner to create new source of revenue generation and reach out to new clientele.