Hogir Rafiq proposes two approaches, the signal processing based condition monitoring approaches with applications to fault detection in gear systems, and application of deep mathematical and system theoretical methods to fault detection. The author develops the multivariate empirical mode decomposition (MEMD) algorithm to enhance the capability of extracting fault features and theoretical problems in nonlinear frequency analysis methods, respectively. The effectiveness has been demonstrated by an experimental study on a wind turbine gearbox test rig.
About the author Hogir Rafiq received his Ph.D. degree at the Institute of Automatic Control and Complex Systems (AKS), Faculty of Engineering, University of Duisburg-Essen, Germany, in 2023, and his M.Sc. degree in Control and Systems Engineering at the department of Automatic Control and Systems Engineering (ACSE), The University of Sheffield, UK, in 2012. His research interests include condition monitoring, signal processing and data-driven fault diagnosis and nonlinear frequency analysis.

Hogir Rafiq proposes two approaches, the signal processing based condition monitoring approaches with applications to fault detection in gear systems, and application of deep mathematical and system theoretical methods to fault detection. The author develops the multivariate empirical mode decomposition (MEMD) algorithm to enhance the capability of extracting fault features and theoretical problems in nonlinear frequency analysis methods, respectively. The effectiveness has been demonstrated by an experimental study on a wind turbine gearbox test rig.