A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics
TLDR
Wang et al. as discussed by the authors proposed a health-adaptive time-scale representation (HTSR) embedded CNN, which is designed to exploit the concept of TSR, informed by the physics of the time and frequency characteristics induced by the faultrelated signals.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2022-03-15 and is currently open access. It has received 16 citations till now. The article focuses on the topics: Convolutional neural network & Computer science.read more
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A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Adam Thelen,Xiaoye Zhang,Olga Fink,Yan Lu,Sayan Ghosh,Byeng D. Youn,Michael D. Todd,Sankaran Mahadevan,Chao Hu,Zhenxiu Hu +9 more
TL;DR: In this paper , the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins are examined, and a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.
Journal ArticleDOI
Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations
TL;DR: In this paper , a semi-supervised learning approach is proposed to detect and diagnose unseen and unknown faults in gear systems using a deep convolutional generative adversarial network (DCGAN).
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Bayesian deep-learning for RUL prediction: An active learning perspective
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Variable three-term conjugate gradient method for training artificial neural networks
TL;DR: In this article , a variable three-term conjugate gradient (VTTCG) method was proposed to enhance search direction and uses a variable step size to achieve improved convergence stability, and the experimental results show that the performance of the VTTCG method is superior to that of four conventional methods including SGD, Adam, AMSGrad, and AdaBelief.
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Physics-informed ensemble learning for online joint strength prediction in ultrasonic metal welding
Yuquan Meng,Chenhui Shao +1 more
TL;DR: In this article , a hierarchical physics-informed ensemble learning (PIEL) framework was developed for accurate online prediction of UMW joint strength using both physical knowledge and online sensing data.
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