Grant: Alliance Grants
Lakehead University
$44,500.00
An Intelligent Monitoring System for Real-time Fault Diagnosis of Pump Stations
EF 2024-25
Résumé
Water pump stations are widely used in industries such as water supply, power generation, agriculture, and mining. Any pump element defect could result in pump performance degradation or unexpected service shutdowns. The objective of this NSERC Alliance Grants research, in collaboration with the Bare Point Water Treatment Plant in Thunder Bay, Ontario, is to develop new techniques and AI tools for pump station condition monitoring and fault diagnosis. It aims to improve operation reliability and safety, but to reduce maintenance costs by preventing unnecessary machine downtimes. To achieve this goal, advanced research and development will be undertaken in multidisciplinary academic fields. Specifically, a new wireless smart sensor network will be developed for data acquisition in the forms of vibration, electric current and temperature. New signal processing techniques will be proposed for fault detection in pump motors (bearings and rotors) and transmission systems. A new evolving intelligent classifier will be developed to integrate the strength of several fault detection techniques for fault diagnosis of motors and pumps. Appropriate machine learning and deep learning algorithms will be proposed to improve AI system convergence and adaptive capability corresponding to different pump system conditions. A smart monitoring system will be developed for condition monitoring and fault diagnosis of pump stations. The contributions from this multidisciplinary research will enhance Canada's profile in the global academic community in the related fields such as machinery modeling, signal processing, AI, machine learning, and mechatronics. It is also foreseen that the developed technologies and AI tools can benefit a wide array of Canadian industries to improve their production quality and operation safety, to prevent human injury and environment pollution, and to reduce maintenance costs. In addition, the proposed techniques and AI tools can be used in health science for human health diagnostics and prognostics.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
597894-2024
Name nom
Wang, Wilson Quansheng WQ
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
2024
Competition year année de concours
2024
Award amount
44500
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
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
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
An Intelligent Monitoring System for Real-time Fault Diagnosis of Pump Stations
Keywords
artificial intelligence; fault diagnostics; induction motors; machine learning; rolling element bearings; signal processing; smart sensors; system condition monitoring; turbine pump stations; wireless sensor network
Application summary
Water pump stations are widely used in industries such as water supply, power generation, agriculture, and mining. Any pump element defect could result in pump performance degradation or unexpected service shutdowns. The objective of this NSERC Alliance Grants research, in collaboration with the Bare Point Water Treatment Plant in Thunder Bay, Ontario, is to develop new techniques and AI tools for pump station condition monitoring and fault diagnosis. It aims to improve operation reliability and safety, but to reduce maintenance costs by preventing unnecessary machine downtimes. To achieve this goal, advanced research and development will be undertaken in multidisciplinary academic fields. Specifically, a new wireless smart sensor network will be developed for data acquisition in the forms of vibration, electric current and temperature. New signal processing techniques will be proposed for fault detection in pump motors (bearings and rotors) and transmission systems. A new evolving intelligent classifier will be developed to integrate the strength of several fault detection techniques for fault diagnosis of motors and pumps. Appropriate machine learning and deep learning algorithms will be proposed to improve AI system convergence and adaptive capability corresponding to different pump system conditions. A smart monitoring system will be developed for condition monitoring and fault diagnosis of pump stations. The contributions from this multidisciplinary research will enhance Canada's profile in the global academic community in the related fields such as machinery modeling, signal processing, AI, machine learning, and mechatronics. It is also foreseen that the developed technologies and AI tools can benefit a wide array of Canadian industries to improve their production quality and operation safety, to prevent human injury and environment pollution, and to reduce maintenance costs. In addition, the proposed techniques and AI tools can be used in health science for human health diagnostics and prognostics.
