What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
The scientist’s investigation covers issues in Electronic engineering, Fault detection and isolation, Vibration, Algorithm and Fault.
His Electronic engineering research is multidisciplinary, incorporating elements of Acoustics, Condition monitoring and Signal processing.
The various areas that Ming Liang examines in his Signal processing study include Energy, Artificial intelligence and Pattern recognition.
The Vibration study combines topics in areas such as Demodulation and Signal.
In his research, Signal-to-noise ratio and Smoothness is intimately related to Wavelet, which falls under the overarching field of Algorithm.
As a member of one scientific family, Ming Liang mostly works in the field of Fault, focusing on Time–frequency analysis and, on occasion, Control engineering.
His most cited work include:
- Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples (475 citations)
- Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications (243 citations)
- Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation (201 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Vibration, Fault, Signal, Control theory and Algorithm.
His Vibration research incorporates elements of Electronic engineering, Frequency modulation, Background noise and Fault detection and isolation.
His work in Electronic engineering covers topics such as Artificial intelligence which are related to areas like Boredom.
Ming Liang has included themes like Demodulation, Time–frequency analysis, Rotational speed, Sideband and Bearing in his Fault study.
In his research on the topic of Signal, Structural engineering is strongly related with Wavelet transform.
The concepts of his Algorithm study are interwoven with issues in Mathematical optimization and Wavelet.
He most often published in these fields:
- Vibration (30.26%)
- Fault (26.32%)
- Signal (25.00%)
What were the highlights of his more recent work (between 2015-2021)?
- Fault (26.32%)
- Vibration (30.26%)
- Control theory (21.05%)
In recent papers he was focusing on the following fields of study:
Ming Liang mostly deals with Fault, Vibration, Control theory, Signal and Bearing.
His research in Fault intersects with topics in Demodulation, Data mining, Time–frequency representation, Representation and Rotational speed.
His biological study deals with issues like Electronic engineering, which deal with fields such as Fault detection and isolation.
His Vibration research is multidisciplinary, relying on both Sideband, Structural engineering, Frequency modulation and Condition monitoring.
The study incorporates disciplines such as Instantaneous phase, Order tracking and Fourier transform in addition to Control theory.
His work carried out in the field of Signal brings together such families of science as Algorithm, Wavelet transform and Artificial intelligence.
Between 2015 and 2021, his most popular works were:
- Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications (243 citations)
- Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions (81 citations)
- Generalized stepwise demodulation transform and synchrosqueezing for time–frequency analysis and bearing fault diagnosis (75 citations)
In his most recent research, the most cited papers focused on:
- Mechanical engineering
- Artificial intelligence
- Electrical engineering
His primary scientific interests are in Fault, Vibration, Algorithm, Bearing and Signal.
Ming Liang combines subjects such as Filter and Control theory with his study of Fault.
In Vibration, he works on issues like Structural engineering, which are connected to Rotation and Tooth surface.
Ming Liang has researched Algorithm in several fields, including Time–frequency representation and Feature extraction.
His Signal study integrates concerns from other disciplines, such as Electronic engineering and Wavelet, Wavelet transform.
His studies examine the connections between Instantaneous phase and genetics, as well as such issues in Demodulation, with regards to Signal processing.
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