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.
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Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators
TL;DR: A thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning is presented and provides a basis for predicting the remaining useful life of bearings and gears.
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An enhanced Kurtogram method for fault diagnosis of rolling element bearings
TL;DR: In this paper, the authors proposed an enhanced Kurtogram based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths, which measured the protrusion of the sparse representation.
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Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model
TL;DR: In this article, a battery capacity prognostic method is developed to estimate the remaining useful life of lithium-ion batteries, which consists of a relevance vector machine and a conditional three-parameter capacity degradation model.
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Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier
TL;DR: A new intelligent fault diagnosis scheme based on the extraction of statistical parameters from the paving of a wavelet packet transform (WPT), a distance evaluation technique (DET) and a support vector regression (SVR)-based generic multi-class solver is proposed.
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The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2”
Peter W. Tse,Dong Wang +1 more
TL;DR: In this paper, the sparsogram is constructed using the sparsity measurements of the power spectra from the envelopes of wavelet packet coefficients at different wavelet decomposition depths.