M
Ming Liang
Researcher at University of Ottawa
Publications - 147
Citations - 7144
Ming Liang is an academic researcher from University of Ottawa. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 43, co-authored 142 publications receiving 5866 citations.
Papers
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Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples
TL;DR: A systematic review of over 20 major time-frequency analysis methods reported in more than 100 representative articles published since 1990 can be found in this article, where their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined.
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Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications
TL;DR: In this article, the spectral kurtosis (SK) technique is extended to that of a function of frequency that indicates how the impulsiveness of a signal can be detected and analyzed.
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Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation
TL;DR: Considering the spectral complexity of planetary gearbox vibration signals as well as their amplitude modulation and frequency modulation (AMFM) nature, a simple yet effective method was proposed in this paper based on amplitude and frequency demodulations.
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Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform
Chuan Li,Chuan Li,Ming Liang +2 more
TL;DR: In this paper, a generalized synchrosqueezing transform (GST)-based time-frequency (TF) signal analysis is proposed to detect gearbox faults under varying shaft speed.
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A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection
I. Soltani Bozchalooi,Ming Liang +1 more
TL;DR: In this paper, the smoothness index is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli of the vibration signal, and it has been successfully used to de-noise both simulated and experimental signals.