D
Dong Wang
Researcher at Shanghai Jiao Tong University
Publications - 169
Citations - 6587
Dong Wang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Bearing (mechanical). The author has an hindex of 35, co-authored 144 publications receiving 3945 citations. Previous affiliations of Dong Wang include Sichuan University & City University of Hong Kong.
Papers
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Journal ArticleDOI
EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings
TL;DR: Experimental results showed that the proposed condition monitoring and fault diagnosis scheme of railway axle bearings is effective in identifying different bearing health conditions.
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An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture:
Dong Wang,Wei Guo,Peter W. Tse +2 more
TL;DR: An enhanced EEMD for the purpose of blind component separation and a fusion rule based on locations of local minima of the revised spectral coherence is proposed to automatically fuse successive IMFs with similar characteristics into a new IMF, called an enhanced IMF (EIMF).
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Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing
TL;DR: Oscillation based PE calculation based on tunable Q factor wavelet transform (TQWT) is implemented to quantify bearing health and it is confirmed that OBPE provides a much better result than direct application of PE in dynamic bearing health monitoring.
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A new blind fault component separation algorithm for a single-channel mechanical signal mixture
Dong Wang,Peter W. Tse +1 more
TL;DR: In this article, a blind component separation (BCS) method is proposed to extract different mechanical fault features from a single-channel mixed signal, which can be decomposed into two parts: the periodic and transient subsets.
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Fisher’s discriminant ratio based health indicator for locating informative frequency bands for machine performance degradation assessment
TL;DR: A Fisher’s discriminant ratio-based health indicator is proposed to fully consider the contributions of all spectrum amplitudes in the frequency domain to machine PDA and can directly realize data-level fusion, namely fusion of frequency amplitudes.