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
基本信息
- 批准号:522090-2018
- 负责人:
- 金额:$ 0.91万
- 依托单位:
- 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:
- 关键词:
项目摘要
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**生命周期管理(LCM)系统开发方面拥有活跃的研究组合。展望未来,作为其持续的产品**改进的一部分,TECSIS需要一种高效的方法来自动选择对输出有重大影响的最主要的输入,如废气温度(EGT)和功率,这些都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**,使用有限的大量模拟数据集来减少计算时间和成本。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和**优化技术开发对输入选择方法的**改进。拟议的投入选择**方法的好处包括提高了预测精度、更快和更具成本效益的预测模型和更好的**解释,以及由于涉及的投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**提议的对输入选择方法的改进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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Heppler, Glenn其他文献
Heppler, Glenn的其他文献
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{{ truncateString('Heppler, Glenn', 18)}}的其他基金
Dynamics and Control of Micropolar Material Structures with Embedded Angular Momentum
嵌入角动量的微极性材料结构的动力学与控制
- 批准号:
RGPIN-2017-03866 - 财政年份:2021
- 资助金额:
$ 0.91万 - 项目类别:
Discovery Grants Program - Individual
Dynamics and Control of Micropolar Material Structures with Embedded Angular Momentum
嵌入角动量的微极性材料结构的动力学与控制
- 批准号:
RGPIN-2017-03866 - 财政年份:2020
- 资助金额:
$ 0.91万 - 项目类别:
Discovery Grants Program - Individual
Dynamics and Control of Micropolar Material Structures with Embedded Angular Momentum
嵌入角动量的微极性材料结构的动力学与控制
- 批准号:
RGPIN-2017-03866 - 财政年份:2019
- 资助金额:
$ 0.91万 - 项目类别:
Discovery Grants Program - Individual
Development of Machine Learning Based Predictive Modeling for Forest Biomass Estimation**
基于机器学习的森林生物量估算预测模型的开发**
- 批准号:
537549-2018 - 财政年份:2018
- 资助金额:
$ 0.91万 - 项目类别:
Engage Grants Program
Dynamics and Control of Micropolar Material Structures with Embedded Angular Momentum
嵌入角动量的微极性材料结构的动力学与控制
- 批准号:
RGPIN-2017-03866 - 财政年份:2018
- 资助金额:
$ 0.91万 - 项目类别:
Discovery Grants Program - Individual
Dynamics and Control of Micropolar Material Structures with Embedded Angular Momentum
嵌入角动量的微极性材料结构的动力学与控制
- 批准号:
RGPIN-2017-03866 - 财政年份:2017
- 资助金额:
$ 0.91万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2017
- 资助金额:
$ 0.91万 - 项目类别:
Engage Grants Program
Adaptive interpolation modeling techniques towards reduced numerical CFD grid computation
用于减少数值 CFD 网格计算的自适应插值建模技术
- 批准号:
499382-2016 - 财政年份:2016
- 资助金额:
$ 0.91万 - 项目类别:
Engage Plus Grants Program
Development of intelligent interpolation models based on limited and expensive CFD and FEA simulations for real-time applications
基于有限且昂贵的 CFD 和 FEA 仿真开发智能插值模型,用于实时应用
- 批准号:
478367-2015 - 财政年份:2015
- 资助金额:
$ 0.91万 - 项目类别:
Engage Grants Program
Dynamics and control of micropolar material structures with embedded angular momentum
嵌入角动量的微极性材料结构的动力学与控制
- 批准号:
6208-2011 - 财政年份:2015
- 资助金额:
$ 0.91万 - 项目类别:
Discovery Grants Program - Individual
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