Grant: Collaborative Research and Development Grants
Carleton University
$40,000.00
Development of an intelligent gas turbine engine health monitoring and degradation prediction system
EF 2018-19
Résumé
Gas turbine engines (GTE) have been widely used in civilian and defense sectors for generating energy and are critical to the operation of many industrial plants, aircrafts, and heavy vehicles. Any unexpected engine failure could be costly and potentially disastrous. Monitoring and predicting the GTE's health condition is therefore critically important and necessary in industries for gaining comprehensive knowledge of the engine system, improving operation reliability, and reducing maintenance cost. Because of their tremendous importance, extensive research has been conducted over the past decades in GTE health condition monitoring and prediction as a means to provide advance warning of failure, enable forecasted maintenance, and improve system qualification. Although a variety of methods have been proposed in the literature, reliable assessment of GTE current and future health conditions still remains a challenging task in both research and industrial applications due to the inherent complexities in GTE structures and time-varying operating conditions. Partnering with an Ottawa-based company, Life Prediction Technologies (LPTi), the proposed research is aimed at developing an intelligent GTE health monitoring and degradation prediction system to address this challenge. Our ultimate goal is to develop more reliable techniques and tools for GTE health condition monitoring and prediction to help Canadian companies improve asset utilization, reduce the total cost of GTE ownership, and remain competitive in the global market.*
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
508307-2016
Name nom
Liu, Jie
Department département
Mechanical & Aerospace Engineering
Organization id
23
Institution établissement
Carleton University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2016
Award amount
40000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
520
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Production and operations management
Area of application fr
Production et gestion des opérations
Research subject code
2109
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Mechanical systems and instrumentation
Research subject fr
Instrumentation et systèmes mécaniques
Partie
2
Nb partie
2
Application title
Development of an intelligent gas turbine engine health monitoring and degradation prediction system
Keyword
NSERC
Application summary
Gas turbine engines (GTE) have been widely used in civilian and defense sectors for generating energy and are critical to the operation of many industrial plants, aircrafts, and heavy vehicles. Any unexpected engine failure could be costly and potentially disastrous. Monitoring and predicting the GTE's health condition is therefore critically important and necessary in industries for gaining comprehensive knowledge of the engine system, improving operation reliability, and reducing maintenance cost. Because of their tremendous importance, extensive research has been conducted over the past decades in GTE health condition monitoring and prediction as a means to provide advance warning of failure, enable forecasted maintenance, and improve system qualification. Although a variety of methods have been proposed in the literature, reliable assessment of GTE current and future health conditions still remains a challenging task in both research and industrial applications due to the inherent complexities in GTE structures and time-varying operating conditions. Partnering with an Ottawa-based company, Life Prediction Technologies (LPTi), the proposed research is aimed at developing an intelligent GTE health monitoring and degradation prediction system to address this challenge. Our ultimate goal is to develop more reliable techniques and tools for GTE health condition monitoring and prediction to help Canadian companies improve asset utilization, reduce the total cost of GTE ownership, and remain competitive in the global market.*