J
Jian-Qiao Sun
Researcher at University of California, Merced
Publications - 247
Citations - 5973
Jian-Qiao Sun is an academic researcher from University of California, Merced. The author has contributed to research in topics: Nonlinear system & Multi-objective optimization. The author has an hindex of 33, co-authored 244 publications receiving 4619 citations. Previous affiliations of Jian-Qiao Sun include Aalborg University & Binghamton University.
Papers
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Journal ArticleDOI
Remaining useful life estimation in prognostics using deep convolution neural networks
Xiang Li,Qian Ding,Jian-Qiao Sun +2 more
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
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Passive, Adaptive and Active Tuned Vibration Absorbers—A Survey
TL;DR: An overview of the recent development of tuned vibration absorbers (TVAs) for vibration and noise suppression is presented in this paper, which summarizes some popular theory for analysis and optimal tuning of these devices, discusses various design configurations, and presents some contemporary applications of passive TVAs.
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Multi-Layer domain adaptation method for rolling bearing fault diagnosis
TL;DR: The proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis and can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved.
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Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation
TL;DR: The proposed intelligent fault diagnosis method offers a new and promising approach to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset.
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The Generalized Cell Mapping Method in Nonlinear Random Vibration Based Upon Short-Time Gaussian Approximation
Jian-Qiao Sun,C. S. Hsu +1 more
TL;DR: In this article, the transition probability matrix of the generalized cell mapping (GCM) method in nonlinear random vibration has been computed in a very efficient and accurate way for computing the one-step transition probability matrices.