Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

The objective of this research project is to develop efficient input selection methods intended for predictivex000D
modelling applications as part of industrial partner's (TECSIS Corporation) ongoing projects that deal with thex000D
quantification of the health monitoring of a gas turbine (GT) engine using data analytics tools. TECSISx000D
provides product development and research and development services, and has an active research portfolio inx000D
Prognostics and Health Management (PHM) system development. Going forward as part of their continuousx000D
product advancements, TECSIS needs a methodology to automatically select the most dominant inputs thatx000D
have significant influence on outputs like exhaust gas temperature (EGT) and power which are major indicatorsx000D
for health monitoring of gas turbines. The proposed input selection methods will be developed utilizingx000D
advanced machine learning techniques by the research team from the University of Waterloo in closex000D
collaboration with the technical experts and engineers from the industrial partner. The benefits of the proposedx000D
input selection methods include improved prediction accuracy, faster and more cost-effective predictivex000D
models, better interpretations of constructed models, and cost savings on the next round of data collection duex000D
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 TECSISx000D
works in. Incorporation of the proposed input selection methods into its predictive modeling and other patternx000D
recognition tools will help TECSIS to expand its applications areas. The success of this project will enable thex000D
industrial partner to create new source of revenue generation and reach out to new clientele.