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学术报告:Gear Condition Monitoring and Fault Diagnosis Based on Demodulation Analysis

  

    报告题目Gear Condition Monitoring and Fault Diagnosis Based on Demodulation Analysis

    报告时间2017317日(星期五)上午9:00-11:00

    报告地点东院7D-101

 

专家介绍:

Dr Fengshou Gu is currently working as the Principle Research Fellow and the Head of Measurement and Data Analysis Research Group (MDARG) at the Centre for Efficiency and Performance Engineering (CEPE), the University of Huddersfield. He has been worked in the field of machine monitoring and diagnostic for more than 30 years in developing an advanced diagnostic laboratory and numerical simulation methods which have facilities to simulate various faults for majority machines. He is supervising over 40 PhD students and undertaking more than 5 contracts funded from UK and Chinese government and industries.

Jan 1985 – June 1991 – Lecturer in Vibration and Acoustics, Taiyuan University of Technology, China

July 1991 – Oct 1996– Research Engineer at University of Manchester, U.K

Nov 1996 – Sept. 2007 Senior Research Engineer at University of Manchester, U.K

Sept. 2007 - to date Principle Research Fellow, Head of MDARG at University of Huddersfield, U.K.

Fengshou Gu is one of experts in the fields of machinery performance diagnostics/prognostics over 20 years of research experience. To improve operational efficiency and reliability of different machines, he has worked in multiple disciplines including electrical, mechanical and thermal dynamics, vibration, acoustics and tribology for modelling, advanced measurements, signal processing and information optimization. He is the author of over 200 technical and professional publications in machinery diagnosis, signal processing, measurement system and related fields. He has undertaken vibro-acoustic research projects of turbine machines, electric motors, internal combustion engines, reciprocating compressors, centrifugal pumps, hydraulic power systems, gearboxes and bearings. He has experienced in system modeling, various physical parameter measurements, advanced signal processing techniques including time-frequency analysis, wavelet transforms, adaptive signal decompositions, neural network algorithms and statistical analysis. Recent research interests are mainly focusing on power supply data based system diagnostics and performance monitoring, and tribological behavior of engines running with alternative fuels.

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