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.

本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涡轮机（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）系统开发方面拥有活跃的研究组合。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显著影响的最主要输入，如排气温度（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预测建模 ** 与有限的大量模拟数据集，以减少计算时间和成本。拟议的输入选择方法的 ** 增强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和 ** 优化技术开发。所提出的输入选择 ** 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，具有更好的解释，以及由于所涉及的输入较少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 ** 应用程序也有重要意义。将 ** 建议的输入选择方法的增强功能纳入其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。