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Niu, Gang,Jiang, Junjie,Youn, Byeng D.,Pecht, Michael Elsevier 2018 ISA transactions Vol.72 No.-
<P><B>Abstract</B></P> <P>Autonomous vehicles are playing an increasingly importance in support of a wide variety of critical events. This paper presents a novel autonomous health management scheme on rail vehicles driven by permanent magnet synchronous motors (PMSMs). Firstly, the PMSMs are modeled based on first principle to deduce the initial profile of pneumatic braking (p-braking) force, then which is utilized for real-time demagnetization monitoring and degradation prognosis through similarity-based theory and generate prognosis-enhanced p-braking force strategy for final optimal control. A case study is conducted to demonstrate the feasibility and benefit of using the real-time prognostics and health management (PHM) information in vehicle ‘drive-brake’ control automatically. The results show that accurate demagnetization monitoring, degradation prognosis, and real-time capability for control optimization can be obtained, which can effectively relieve brake shoe wear.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel autonomous health management scheme for PMSM Rail Vehicles. </LI> <LI> Accurate demagnetization monitoring and degradation prognosis. </LI> <LI> Real-time prognosis-enhanced optimal control capability. </LI> <LI> Integrated control characteristic for ‘drive-brake’ systems. </LI> <LI> Advanced prognosis control enabled maintenance optimization. </LI> </UL> </P>
Decision fusion system for fault diagnosis of elevator traction machine
Gang Niu,Sun-Soon Lee,Bo-Suk Yang,Soo-Jong Lee 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.1
Fault detection and diagnosis is critical for healthy operation of an elevator system. In order to realize a real-time and convenient diagnosis and satisfy the requirement of advanced maintenance of an elevator system, this paper proposes an intelligent fault diagnosis approach of induction motor for elevator traction machine using a developed decision fusion system. First, the basic knowledge of fusion techniques is briefly introduced which consists of classifier selection and decision fusion. Then a developed decision fusion system is presented. Next, four fusion algorithms?majority voting, Bayesian belief, multi-agent and modified Borda count?are employed for comparison in a real-world diagnosis experiment of a faulty elevator motor system. Based on the satisfactory results shown in the experiment, a big potential in real-world application is presented that is effective and cost saving only by analyzing stator current signals using proposed decision fusion system.
DEMPSTER-SHAFER REGRESSION FOR TIME-SERIES MONITORING AND PREDICTION
Gang Niu,Bo-Suk Yang 대한기계학회 2007 대한기계학회 춘추학술대회 Vol.2007 No.10
Multi-step-ahead time series prediction involves the task of predicting a sequence of future values using only the values observed in the past, which can be regarded as stand-stone of data-driven machinery prognosis. This paper introduces a whole approach for multi-step-ahead time-series prediction utilizing Dempster-Shafer regression theory. First, original time-series signal is reconstructed based on the theorem of Takens. Next, Dempster-Shafer regression technique is employed to take on the task of time-series prediction. Moreover, we employ iterated strategy to realize multi-step-ahead prediction. The proposed approach is evaluated by a condition monitoring and prediction experiment of methane compressor. The errors of multi-step-ahead time-series prediction are evaluated. Experiment results show that the proposed approach can be competent for the task of data-driven machinery prognosis.
Elevator Motor Fault Diagnosis using Bayesian Belief Fusion and Stator Current Signals
Gang Niu,Bo-Suk Yang(양보석) 한국동력기계공학회 2006 한국동력기계공학회 학술대회 논문집 Vol.- No.-
Fault detection and diagnosis are critical for health operation of elevator system. Aim to realize a real-time and convenient diagnosis for satisfying the requirement of advanced maintenance of elevator system. This paper develops an intelligent fault diagnosis system of elevator motor using a new approach, decision fusion. First, the basic knowledge of fusion techniques are briefly introduced which consist of classifier selection and multi-classifier fusion. Then a new decision fusion system is presented. Next Bayesian belief fusion algorithms are employed in a real-world diagnosis experiment of a faulty elevator motor system. Based on the satisfied results shown in the experiment, a big potential in the real-world application is presented that are effective and cost saving only by analyzing stator current signals using proposed decision fusion system.
Niu, Feng,Zhao, Song,Xu, Chang-Yan,Chen, Lin,Ye, Long,Bi, Gui-Bin,Tian, Gang,Gong, Ping,Nie, Tian-Hong Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.24
Background: To explore the molecular mechanisms of metastatic osteosarcoma (OS) by using the microarray expression profiles of metastatic and non-metastatic OS samples. Materials and Methods: The gene expression profile GSE37552 was downloaded from Gene Expression Omnibus database, including 2 human metastatic OS cell line models and 2 two non-metastatic OS cell line models. The differentially expressed genes (DEGs) were identified by Multtest package in R language. In addition, functional enrichment analysis of the DEGs was performed by WebGestalt, and the protein-protein interaction (PPI) networks were constructed by Hitpredict, then the signal pathways of the genes involved in the networks were performed by Kyoto Encyclopaedia of Genes and Genomes (KEGG) automatic annotation server (KAAS). Results: A total of 237 genes were classified as DEGs in metastatic OS. The most significant up- and down-regulated genes were A2M (alpha-2-macroglobulin) and BCAN (brevican). The DEGs were significantly related to the response to hormone stimulus, and the PPI network of A2M contained IL1B (interleukin), LRP1 (low-density lipoprotein receptor-related protein 1) and PDGF (platelet-derived growth factor). Furthermore, the MAPK signaling pathway and focal adhesion were significantly enriched. Conclusions: A2M and its interactive proteins, such as IL1B, LRP1 and PDGF may be candidate target molecules to monitor, diagnose and treat metastatic OS. The response to hormone stimulus, MAPK signaling pathway and focal adhesion may play important roles in metastatic OS.
An Investigation into the Prevalence of Voice Strain in Chinese University Teachers
( Gang Zhou ),( Xiao Chun Niu ) 범태평양 응용언어학회 2015 Journal of Pan-Pacific Association of Applied Ling Vol.19 No.1
Vocal disorders are very common occupation-related disease in teachers, though it has never been given enough attention in China. As a result, the occupational health care of professional voice users is surprisingly undeveloped compared to the attention given to occupational hearing disorders or many other occupational symptoms. The aim of the present study was to assess the prevalence of voice problems in the general population of Chinese university teachers, and explore whether their voice problems affected their daily life, their social life and their work. A voice strain and voice handicap index questionnaire was administered to university instructors of English (N = 156) in six Chinese universities. Results indicated that voice strain is prevalent among Chinese university instructors. The respondents`` self-perceptions revealed that voice strain was significantly correlated with their job and their daily activities.