J
Jiawei Xiang
Researcher at Wenzhou University
Publications - 159
Citations - 4811
Jiawei Xiang is an academic researcher from Wenzhou University. The author has contributed to research in topics: Finite element method & Wavelet. The author has an hindex of 29, co-authored 133 publications receiving 3033 citations. Previous affiliations of Jiawei Xiang include Xi'an Jiaotong University & University of Ottawa.
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Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system
TL;DR: VMD is a newly developed technique for adaptive signal decomposition, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions and shows that the multiple features can be better extracted with the VMD, simultaneously.
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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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Convolutional neural network-based hidden Markov models for rolling element bearing fault identification
TL;DR: In this article, a convolutional neural network-based hidden Markov models (CNN HMMs) are presented to classify multi-fault in mechanical systems, and the average classification accuracy ratios are 98.125% and 98% for two data series with agreeable error rate reductions.
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A data indicator-based deep belief networks to detect multiple faults in axial piston pumps
TL;DR: A method using deep belief networks (DBNs) is proposed to detect multiple faults in axial piston pumps using DBNs, which confirms the feasibility and effectiveness of multiple faults detection in axials piston pumps.
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Latest developments in gear defect diagnosis and prognosis: A review
TL;DR: An insight into various defects that generally occur in gears is provided and a state-of-the-art review is provided on the latest and most widely used diagnosis methods for gearbox condition monitoring.