Grant: Collaborative Research and Development Grants
Carleton University
$62,667.00
Condition monitoring of lubricating oils in diesel engines
FY 2019-20
Summary
Our industrial partner, Gastops Ltd, is a Canadian world leader in condition monitoring of rotating machinery. In collaboration with Carleton University, the company is now studying physical and chemical mechanisms of degradation in lubricants, potentially enabling developments of technologies for real-time condition monitoring using on-line sensors capable of predicting the remaining useful life of a lubricating oil. This research requires two fundamental scientific contributions. The first is a means to calibrate and characterize the signals that an on-line sensor will produce. This entails careful, controlled oil degradation experiments and accurate characterizations of the resulting by-products using well-established lab-based tools. The second requirement is accurate modeling of the oil degradation processes. Once the sensor is calibrated to accurately identify the types and concentrations of key degradation by-products, it can only predict the remaining useful life of lubricating oil if a model exists to extrapolate lubricating oil conditions based on the current state of the oil and the expected operating conditions of the machinery. Our combined theoretical, computational and experimental approach will lead to the development of a chemical kinetics model with this predictive capability.
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
515552-2017
Name nom
Miller, Ronald
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
2019
Competition year année de concours
2017
Award amount
62667
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
516
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Mechanical machinery, heavy equipment (including farm, forestry, and construction equipment)
Area of application fr
Machinerie mécanique, machinerie lourde (y compris machinerie agricole, forestière, de construction)
Research subject code
3707
Research subject group en
Organic chemistry
Research subject group fr
Chimie organique
Research subject en
Kinetics, reactive intermediaries and mechanisms of reactions
Research subject fr
Cinétique, intermédiaires réactifs et mécanismes des réactions
Installment
inst:3 - 3
Partie
3
Nb partie
3
Application title
Condition monitoring of lubricating oils in diesel engines
Keyword
NSERC
Application summary
Our industrial partner, Gastops Ltd, is a Canadian world leader in condition monitoring of rotating machinery. In collaboration with Carleton University, the company is now studying physical and chemical mechanisms of degradation in lubricants, potentially enabling developments of technologies for real-time condition monitoring using on-line sensors capable of predicting the remaining useful life of a lubricating oil. This research requires two fundamental scientific contributions. The first is a means to calibrate and characterize the signals that an on-line sensor will produce. This entails careful, controlled oil degradation experiments and accurate characterizations of the resulting by-products using well-established lab-based tools. The second requirement is accurate modeling of the oil degradation processes. Once the sensor is calibrated to accurately identify the types and concentrations of key degradation by-products, it can only predict the remaining useful life of lubricating oil if a model exists to extrapolate lubricating oil conditions based on the current state of the oil and the expected operating conditions of the machinery. Our combined theoretical, computational and experimental approach will lead to the development of a chemical kinetics model with this predictive capability.