Author
Xianfeng Fan
Other affiliations: Shanghai Jiao Tong University, University of Electronic Science and Technology of China
Bio: Xianfeng Fan is an academic researcher from University of Alberta. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 5, co-authored 7 publications receiving 605 citations. Previous affiliations of Xianfeng Fan include Shanghai Jiao Tong University & University of Electronic Science and Technology of China.
Papers
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TL;DR: In this article, the authors proposed a new fault detection method that combines Hilbert transform and wavelet packet transform for gearbox demodulation, which can extract modulating signal and help to detect the early gear fault.
308 citations
TL;DR: In this paper, conventional D-S evidence theory is improved through the introduction of fuzzy membership function, importance index, and conflict factor in order to address the issues of evidence sufficiency, evidence importance, and conflicting evidences in the practical application of D- S evidence theory.
168 citations
TL;DR: Wavelet transform is proposed to be used to pre-process the data collected from a single sensor and then use the coefficients of the wavelet transforms at different scales as input to ICA and PCA to overcome the requirement for signals from at least two separate sensors.
89 citations
TL;DR: A new decision method is proposed that can deal with issues, combine multi-evidence information from different methods, and provide more accurate diagnostic results and can enhance diagnostic accuracy and autonomy.
74 citations
TL;DR: In this article, a fault detection method called Hilbert and TT-transform (HTT-transform) was proposed to analyze the modulating signal created by a faulty gear using a time-time representation.
Abstract: Machine vibration signal has been used in fault detection and diagnosis. Modulation and non-stationarity existing in the signal generated by a faulty gearbox present challenges to effective fault detection. Hilbert transform has the ability to address the modulation issue. This paper outlines a novel fault detection method called Hilbert & TT-transform (HTT-transform) which combines Hilbert transform and TT-transform obtained from the inverse Fourier transform of the S-transform. The principle of the proposed method is to analyze the modulating signal created by a faulty gear using a time-time representation. The method has the advantage of providing a new way of localizing the time features of the modulating signal around a particular point on the time axis through scaled windows. It is verified with simulated signals and real gearbox vibration signals. The results obtained by CWT, S-transform, TT- transform, and HTT-transform are compared. They show that utilizing the proposed method can improve the effectiveness of gearbox fault detection.
6 citations
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TL;DR: Current applications of wavelets in rotary machine fault diagnosis are summarized and some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, newWavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosed are discussed.
1,087 citations
TL;DR: In this article, a tutorial on Hilbert transform applications to mechanical vibration is presented, with a large number of examples devoted to illustrating key concepts on actual mechanical signals and demonstrating how the Hilbert transform can be taken advantage of in machine diagnostics, identification of mechanical systems and decomposition of signal components.
553 citations
TL;DR: This paper aims to review and summarize publications on condition monitoring and fault diagnosis of planetary gearboxes and provide comprehensive references for researchers interested in this topic.
551 citations
TL;DR: A novel method for multi-sensor data fusion based on a new belief divergence measure of evidences and the belief entropy was proposed, which outperforms other related methods where the basic belief assignment of the true target is 89.73%.
447 citations
TL;DR: In this paper, a new deconvolution method is presented for the detection of gear and bearing faults from vibration data, which takes advantage of the periodic nature of the faults as well as the impulse-like vibration behaviour associated with most types of faults.
444 citations