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Author

Kesheng Wang

Other affiliations: University of Pretoria
Bio: Kesheng Wang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Order tracking & Fault (power engineering). The author has an hindex of 13, co-authored 42 publications receiving 492 citations. Previous affiliations of Kesheng Wang include University of Pretoria.

Papers
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Journal ArticleDOI
TL;DR: A new statistically robust band selection tool which can capture cyclostationarity separately from non-Gaussianity is proposed, based on the strength of target cyclic frequency components in the spectrum of the log envelope (LES), and so potential fault frequencies must be known in advance.

95 citations

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TL;DR: In this article, the authors presented a study on rotating machine vibration signals by using computed order tracking, Vold-Kalman filtering and intrinsic mode functions from the empirical mode decomposition method.

77 citations

Journal ArticleDOI
TL;DR: In this paper, the amplitudes of characteristic frequencies based on a phenomenological model were explored for a planetary gearbox and a modified sideband energy ratio (SER) was proposed to deal with the problem of rotating speed fluctuation.

65 citations

Journal ArticleDOI
TL;DR: In this paper, phase angle data extracted from measured planetary gearbox vibrations is used for fault detection under non-stationary operational conditions, together with sample entropy, and the proposed scheme proves to be effective and features advantages on fault diagnosis of planetary gearboxes under nonstationary operating conditions.

57 citations

Journal ArticleDOI
TL;DR: A signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques is proposed and proves to be effective for planetary gearbox fault diagnosis.
Abstract: The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold–Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis.

48 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.

1,410 citations

Journal ArticleDOI
TL;DR: A systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection is provided.

312 citations

Journal ArticleDOI
TL;DR: In this paper, an EMD-based rolling bearing diagnosing method was proposed for bearing damage detection at a much earlier stage of damage development, by using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions ( IMF s) and then, a new method of IMF s aggregation into three Combined Mode Function (CMF s) was applied and finally the vibration signal was divided into three parts of signal.

175 citations

Journal ArticleDOI
Ming Zhao1, Jing Lin1, Xiufeng Wang1, Yaguo Lei1, Junyi Cao1 
TL;DR: In this article, a tacho-less order tracking method is established for any speed variations including large speed variation such as run-up or run-down process of machinery, where a Chirplet-based approach is proposed to estimate the instantaneous frequency of a certain harmonic of rotating frequency.

129 citations