Enhancement and Verification of Input Selection Methods for Predictive Modeling in Life Cycle Management

生命周期管理中预测建模输入选择方法的增强和验证

• 批准号: 522090-2018
• 负责人: Heppler, Glenn
• 金额: $ 0.91万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653463
• 关键词: Enhancement; Verification; Input; Selection; Methods

The goal of this research project is to develop efficient input selection methods intended for predictive**modeling applications as part of industrial partner's (TECSIS Corporation) ongoing projects that deal with the**quantification of the health monitoring of a gas turbine (GT) engine using data analytics tools. TECSIS**provides product development and research and development services, and has an active research portfolio in**Life Cycle Management (LCM) system development. Going forward as part of their continuous product**advancements, TECSIS needs an efficient methodology to automatically select the most dominant inputs that**have significant influence on the output like exhaust gas temperature (EGT) and power which are major**indicators for health monitoring of gas turbines. The same approach can also be applied to predictive modeling**with limited sets of large amount of simulated data to reduce the computational time and cost. The proposed**enhancements to input selection methods will be developed utilizing advanced machine learning and**optimization techniques by the research team from the University of Waterloo in close collaboration with the**technical experts and engineers from the industrial partner. The benefits of the proposed input selection**methods include improved prediction accuracy, faster and more cost-effective predictive models with better**interpretations, and cost savings on the next round of data collection due to 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 TECSIS works in. Incorporation of the**proposed enhancements to input selection methods into its predictive modeling and other pattern recognition**tools will help TECSIS to expand its applications areas. The success of this project will enable the industrial**partner to create new source of revenue generation and reach out to new clientele.

该研究项目的目标是开发有效的输入选择方法，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，该项目涉及使用数据分析工具的**量化燃气轮机（GT）发动机的健康监测。 TECSIS **提供产品开发和研发服务，并拥有**生命周期管理（LCM）系统开发方面的积极研究组合。作为其连续产品的一部分，TECSIS需要有效的方法，以自动选择最主要的输入，这些输入**对排气气温（EGT）和功率等产量具有重大影响，这是燃气轮机健康监测的主要**指标。使用有限的大量模拟数据集以减少计算时间和成本，也可以将相同的方法应用于预测性建模**。拟议的**对输入选择方法的增强功​​能将利用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的研究团队的优化技巧开发。提出的输入选择的好处**方法包括提高的预测准确性，更快，更具成本效益的预测模型，具有更好的**解释，并且由于所涉及的输入较少，因此在下一轮数据收集中节省了成本。这些**方法还对TECSIS工作的其他机械，电气和软件系统中的应用程序进行了重大影响。将**拟议的增强功能纳入其预测性建模和其他模式识别**将帮助TECSIS扩展其应用程序领域。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。