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，作为工业合作伙伴（TECSIS公司）正在进行的项目的一部分，该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**生命周期管理（LCM）系统开发方面拥有积极的研究组合。作为其持续产品进步的一部分，TECSIS需要一种有效的方法来自动选择对输出有重大影响的最主要输入，如废气温度（EGT）和功率，这是燃气轮机健康监测的主要指标。同样的方法也可以应用于具有有限的大量模拟数据集的预测建模**，以减少计算时间和成本。滑铁卢大学的研究团队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和优化技术，开发对输入选择方法的改进。所提出的输入选择方法的好处包括提高预测精度，更快，更具成本效益的预测模型，更好的解释，以及由于较少的输入而节省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、分类和聚类应用也具有重要意义。在其预测建模和其他模式识别工具中纳入拟议的输入选择方法增强功能将有助于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收入来源，并接触到新的客户。