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Grant: Alliance Grants

University of Ontario Institute of Technology

$183,600.00

Creation of LIVE Digital Twin for Prognostics and Predictive Maintenance of Rotating Machineries (LIVE for 2PM)

FY 2022-23

Summary
Due to the pandemic, the regular in-person inspection of the critical Rotating Machineries (RM) in hydropower, wind energy, and transportation were interrupted. Unfortunately, RM typically deteriorate their balance condition dynamically, due to wear, thermal bending, dirt collection, insulation failure, partial discharge, fault currents, thermal stress due to overloading, etc., and gradually develop concerning vibratory unbalance responses affecting RM performance and lifespan. Failure of RM in applications such as turbomachines, electromotors, generators, pumps, jet fans, etc. creates enormous unscheduled repair costs and more importantly may put human life in danger. This project aims to develop methodologies required to employ the concept of LIVE Digital Twin for Prognostics and Predictive Maintenance (LIVE for 2PM) of RM highly critical assets. The term "LIVE" refers to the first letters of the four phases in the methodology, i.e. Learn, Identify, Verify, and Extend. The key feature in LIVE digital Twins is multi-physics simulation solutions developed in alternative levels and modes, which are supported by artificial neural network and deep learning tools. LIVE Digital Twin allows Learning the behavioral patterns of individual RM considering their specifications, environment and customized factors. It Identifies the locations and types of sensors and the sensing procedures to Verify the possibilities for progressively increasing defects. It Extends the life of RM by advising predictive maintenance avoiding its failure. Implementation of LIVE Digital Twin not only reduces the needs for in-person inspection during the pandemic, but also can result in significant improvement in asset management of the critical Canadian infrastructures for energy and transportation sectors with predictive maintenance, reduced cost of repair, reduced frequency in replacing parts, shorter down-time of the corresponding highly critical infrastructures, reduced waste, and increased life-span of critical machines. All of these result in significant economic growth for a chain of industries and also a noticeable welfare of the society.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
570439-2021
Name nom
Barari, Ahmad A
Department département
Mechanical and Manufacturing Engineering
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2021
Award amount
183600
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
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
2107
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Modelling, simulation and finite element methods
Research subject fr
Modélisation, simulation et méthodes des éléments finis
Installment
inst:2
Partie
1
Nb partie
2
Application title
Creation of LIVE Digital Twin for Prognostics and Predictive Maintenance of Rotating Machineries (LIVE for 2PM)
Application summary
Due to the pandemic, the regular in-person inspection of the critical Rotating Machineries (RM) in hydropower, wind energy, and transportation were interrupted. Unfortunately, RM typically deteriorate their balance condition dynamically, due to wear, thermal bending, dirt collection, insulation failure, partial discharge, fault currents, thermal stress due to overloading, etc., and gradually develop concerning vibratory unbalance responses affecting RM performance and lifespan. Failure of RM in applications such as turbomachines, electromotors, generators, pumps, jet fans, etc. creates enormous unscheduled repair costs and more importantly may put human life in danger. This project aims to develop methodologies required to employ the concept of LIVE Digital Twin for Prognostics and Predictive Maintenance (LIVE for 2PM) of RM highly critical assets. The term "LIVE" refers to the first letters of the four phases in the methodology, i.e. Learn, Identify, Verify, and Extend. The key feature in LIVE digital Twins is multi-physics simulation solutions developed in alternative levels and modes, which are supported by artificial neural network and deep learning tools. LIVE Digital Twin allows Learning the behavioral patterns of individual RM considering their specifications, environment and customized factors. It Identifies the locations and types of sensors and the sensing procedures to Verify the possibilities for progressively increasing defects. It Extends the life of RM by advising predictive maintenance avoiding its failure. Implementation of LIVE Digital Twin not only reduces the needs for in-person inspection during the pandemic, but also can result in significant improvement in asset management of the critical Canadian infrastructures for energy and transportation sectors with predictive maintenance, reduced cost of repair, reduced frequency in replacing parts, shorter down-time of the corresponding highly critical infrastructures, reduced waste, and increased life-span of critical machines. All of these result in significant economic growth for a chain of industries and also a noticeable welfare of the society.