Reference number : GC-2017-Q2-00108, amendment number : , agreement type : G, recipient legal name : Heppler, Glenn (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The objective of this research project is to develop efficient input selection methods intended for predictive_x000D_
modelling applications as part of industrial partner's (TECSIS Corporation) ongoing projects that deal with the_x000D_
quantification of the health monitoring of a gas turbine (GT) engine using data analytics tools. TECSIS_x000D_
provides product development and research and development services, and has an active research portfolio in_x000D_
Prognostics and Health Management (PHM) system development. Going forward as part of their continuous_x000D_
product advancements, TECSIS needs a methodology to automatically select the most dominant inputs that_x000D_
have significant influence on outputs like exhaust gas temperature (EGT) and power which are major indicators_x000D_
for health monitoring of gas turbines. The proposed input selection methods will be developed utilizing_x000D_
advanced machine learning techniques by the research team from the University of Waterloo in close_x000D_
collaboration with the technical experts and engineers from the industrial partner. The benefits of the proposed_x000D_
input selection methods include improved prediction accuracy, faster and more cost-effective predictive_x000D_
models, better interpretations of constructed models, and cost savings on the next round of data collection due_x000D_
to fewer inputs involved. These methods also have significant implications for developing predictive modeling,_x000D_
classification, and clustering applications in other mechanical, electrical, and software systems that TECSIS_x000D_
works in. Incorporation of the proposed input selection methods into its predictive modeling and other pattern_x000D_
recognition tools will help TECSIS to expand its applications areas. The success of this project will enable the_x000D_
industrial partner to create new source of revenue generation and reach out to new clientele., program purpose french : The objective of this research project is to develop efficient input selection methods intended for predictive_x000D_
modelling applications as part of industrial partner's (TECSIS Corporation) ongoing projects that deal with the_x000D_
quantification of the health monitoring of a gas turbine (GT) engine using data analytics tools. TECSIS_x000D_
provides product development and research and development services, and has an active research portfolio in_x000D_
Prognostics and Health Management (PHM) system development. Going forward as part of their continuous_x000D_
product advancements, TECSIS needs a methodology to automatically select the most dominant inputs that_x000D_
have significant influence on outputs like exhaust gas temperature (EGT) and power which are major indicators_x000D_
for health monitoring of gas turbines. The proposed input selection methods will be developed utilizing_x000D_
advanced machine learning techniques by the research team from the University of Waterloo in close_x000D_
collaboration with the technical experts and engineers from the industrial partner. The benefits of the proposed_x000D_
input selection methods include improved prediction accuracy, faster and more cost-effective predictive_x000D_
models, better interpretations of constructed models, and cost savings on the next round of data collection due_x000D_
to fewer inputs involved. These methods also have significant implications for developing predictive modeling,_x000D_
classification, and clustering applications in other mechanical, electrical, and software systems that TECSIS_x000D_
works in. Incorporation of the proposed input selection methods into its predictive modeling and other pattern_x000D_
recognition tools will help TECSIS to expand its applications areas. The success of this project will enable the_x000D_
industrial partner to create new source of revenue generation and reach out to new clientele., agreement title english : Development of Input Selection Methods for Predictive Modelling in the Health Monitoring of Gas Turbine Engines, agreement title french : Development of Input Selection Methods for Predictive Modelling in the Health Monitoring of Gas Turbine Engines, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-07-12, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada