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

燃气轮机健康监测预测建模输入选择方法的开发

• 批准号: 513460-2017
• 负责人: Heppler, Glenn
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639999
• 关键词: Development; Input; Selection; Methods; Predictive

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.

该研究项目的目的是开发有效的输入选择方法，旨在用于预测销售应用程序，这是工业合作伙伴（TECSIS Corporation）正在进行的项目，这些项目涉及使用数据分析工具的燃气轮机（GT）发动机的健康监控的量级监测。 TecSisprovides产品开发和研发服务，并具有积极的研究组合Inprognostics and Health Management（PHM）系统开发。作为其连续生产进步的一部分，TECSIS需要一种方法，可以自动选择最主要的投入，这些输入对排气气温（EGT）和功率等产量产生重大影响，这是对燃气轮机健康监测的主要指标。拟议的输入选择方法将使用滑铁卢大学的研究小组与工业合作伙伴的技术专家和工程师进行关注的研究团队开发。提出的信息选择方法的好处包括提高预测准确性，更快，更具成本效益的预测模型，对构造模型的更好解释以及在下一轮数据收集二重奏二重奏中所涉及的较少输入的成本节省。这些方法还可以在其他机械，电气和软件系统中开发预测性建模，分类和聚类应用，这些系统将所提出的输入选择方法纳入其预测建模和其他模式认识工具中，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建新的收入来源并与新客户联系。