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Hua Wang

Bio: Hua Wang is an academic researcher from Nanjing Tech University. The author has contributed to research in topics: Slewing bearing & Nonlinear system. The author has an hindex of 9, co-authored 30 publications receiving 265 citations.

Papers
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Journal ArticleDOI
Ran Gu1, Jie Chen1, Rongjing Hong1, Hua Wang1, Weiwei Wu2 
TL;DR: The proposed adaptive variational mode decomposition and Teager energy operator method (AVMD-TEO) can effectively reduce signal noise and extract incipient fault feature of rolling bearings.

100 citations

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TL;DR: Experiments comparing with several algorithms show that the proposed methods can effectively evaluate the health condition of the slewing bearing.

47 citations

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TL;DR: This paper presents a method of testing the fatigue life using a small sample test and three fatigue life calculation methods have distinct advantages and can be mutually referenced to improve the accuracy of bearing life calculations.

40 citations

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TL;DR: In this article, the effect of environmental medium on corrosion fatigue life has been investigated and it was shown that the presence of corrosive medium will accelerate both crack initiation and propagation rates and reduce the failure life for the expansion joints.

33 citations


Cited by
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Journal ArticleDOI
27 Sep 2017
TL;DR: In this paper, an empirical study of feature extraction methods for the application of low-speed bearing condition monitoring is presented, where the selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
Abstract: This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.

301 citations

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TL;DR: A review for application of robotics in onshore oil and gas industry and semi-autonomous robots, where actions are performed by robots but cognitive decisions are still taken by skilled operator are presented.

269 citations

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TL;DR: This article provides a survey of recent research on fault prognosis and reports on some of the significant application domains where prognosis techniques are employed.
Abstract: Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

194 citations

Journal ArticleDOI
TL;DR: A novel method by integrating the Convolutional Neural Networks with the Variational Mode Decomposition (VMD) algorithms to achieve an effective and efficient fault diagnosis of rolling bearings under different environments and states is developed.

103 citations

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TL;DR: A new deep learning framework – Temporal convolutional network with residual self-attention mechanism (TCN-RSA), which can learn both time-frequency and temporal information of signals and outperforms the other state-of-the-art methods in RUL prediction and system prognosis with respect to better accuracy and computation efficiency.

103 citations