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

该研究项目的目标是开发高效的输入选择方法，用于预测建模应用程序，作为工业合作伙伴(TECSIS Corporation)正在进行的项目的一部分，这些项目使用数据分析工具对燃气轮机(GT)发动机的健康监测进行量化。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，作为其持续产品改进的一部分，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。滑铁卢大学的研究小组将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拟议的投入选择方法。建议的输入选择方法的好处包括提高预测精度、更快和更具成本效益的预测模型、更好地解释所构建的模型，以及由于涉及的输入更少而节省了下一轮数据收集的成本。这些方法对于在TECSIS工作的其他机械、电气和软件系统中开发预测建模、分类和集群应用程序也具有重要意义。将拟议的输入选择方法纳入其预测建模和其他模式识别工具，将有助于TECSIS扩大其应用领域。该项目的成功将使工业合作伙伴能够创造新的收入来源，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.