Machine Condition Monitoring Professors in Global

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Morten Kristiansen
Aalborg University Denmark
process monitoring
industrial automation
process automation
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Jie Liu
Carleton University Canada
machinery condition monitoring
structure condition monitoring
machine learning
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Jie Liu
Carleton University Canada
machinery condition monitoring
structure condition monitoring
machine learning
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Louise Klotz
Technical University of Denmark Denmark
climate monitoring
emissions monitoring
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Sonia Grego
Duke University Singapore
monitoring technologies
machine learning applications in environmental monitoring
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Fathy Ismail
University of Waterloo Canada
machinery diagnostics
machining dynamics
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W Mugge
Delft University of Technology Netherlands
human-machine control
motor control systems
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Frank Neitzel
Technical University of Berlin Germany
structural monitoring
machine alignment
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Amy Li
University of Waterloo Canada
indoor pollutant monitoring
radon monitoring
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Michelle Gray
University of New Brunswick Canada
environmental monitoring
long-term monitoring
aquatic environmental monitoring
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Ateev Mehrotra
Brown University United States of America
patient monitoring
quality measurement
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Dieter Claeys
Ghent University Belgium
manufacturing systems performance analysis
maintenance optimization
control of manufacturing systems
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Sebastian Schrøder
Aalborg University Denmark
production automation
automation
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Andrew Goldenberg
University of Toronto Canada
intelligent machines
automation
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Banu Örmeci
Carleton University Canada
environmental surveillance
real-time monitoring technologies
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Banu Örmeci
Carleton University Canada
environmental surveillance
real-time monitoring technologies
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Sinisa Durovic
University of Manchester United Kingdom
condition monitoring electric machines
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Sivaneasan Bala Krishnan
Singapore Institute of Technology Singapore
condition monitoring system
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Gregor Giebel
Technical University of Denmark Denmark
condition monitoring of wind turbines
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Behzad Kordi
University of Manitoba Canada
condition monitoring of high voltage apparatus
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Shirley Dyke
Purdue University United States of America
structural condition monitoring
machine vision
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