Grant: Collaborative Research and Development Grants
Lakehead University
$40,000.00
Online condition monitoring of turbine pump stations
EF 2014-15
Résumé
One of the fundamental problems facing a wide range of industries is how to effectively identify a machinery defect before it reaches critical levels so as to avoid machinery performance degradation, malfunction, and even catastrophic failures. The current strategy in fault diagnosis is to periodically shut down the machine service for manual inspection. Often, no faults are detected during these routine examinations; consequently, the unnecessary downtimes may add significant costs to the operation of machines. The objective of this research project, in collaboration with the Bare Point Water Treatment Plant in Thunder Bay, Ontario, is to develop new technologies and intelligent tools for real-time condition monitoring of turbine pump stations. It aims to improve operation reliability and reduce maintenance cost. To achieve this goal, advanced research and development will be taken in an interdisciplinary field. Specifically, a new signal processing technique will be proposed for fault detection for rolling element bearings; a more accurate classifier will be developed for automatic fault diagnosis in electric motors and shafts; a new training technique will be suggested to improve classifier's adaptive capability to accommodate different machinery conditions; an intelligent platform will be developed for online condition monitoring of turbine pump workstations. This research will have the potential to change the traditional meaning of maintenance. The developed fault detection technologies and intelligent tools can also be applied in other Canadian industries for real-time machinery health condition monitoring and would help to improve production quality and operational safety, and reduce maintenance cost.
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
435043-2012
Name nom
Wang, WilsonQuansheng
Department département
Mechanical Engineering
Organization id
25
Institution établissement
Lakehead University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2014
Competition year année de concours
2012
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
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
2100
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Mechanical engineering
Research subject fr
Génie mécanique
Partie
3
Nb partie
3
Application title
Online condition monitoring of turbine pump stations
Keyword
NSERC
Application summary
One of the fundamental problems facing a wide range of industries is how to effectively identify a machinery defect before it reaches critical levels so as to avoid machinery performance degradation, malfunction, and even catastrophic failures. The current strategy in fault diagnosis is to periodically shut down the machine service for manual inspection. Often, no faults are detected during these routine examinations; consequently, the unnecessary downtimes may add significant costs to the operation of machines. The objective of this research project, in collaboration with the Bare Point Water Treatment Plant in Thunder Bay, Ontario, is to develop new technologies and intelligent tools for real-time condition monitoring of turbine pump stations. It aims to improve operation reliability and reduce maintenance cost. To achieve this goal, advanced research and development will be taken in an interdisciplinary field. Specifically, a new signal processing technique will be proposed for fault detection for rolling element bearings; a more accurate classifier will be developed for automatic fault diagnosis in electric motors and shafts; a new training technique will be suggested to improve classifier's adaptive capability to accommodate different machinery conditions; an intelligent platform will be developed for online condition monitoring of turbine pump workstations. This research will have the potential to change the traditional meaning of maintenance. The developed fault detection technologies and intelligent tools can also be applied in other Canadian industries for real-time machinery health condition monitoring and would help to improve production quality and operational safety, and reduce maintenance cost.