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- 저자 : Hongtian Chen (검색결과 4 건)
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A Multi-mode Incipient Sensor Fault Detection and Diagnosis Method for Electrical Traction Systems
Hongtian Chen,Bin Jiang,Ningyun Lu 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.4
This paper proposes a data-driven sensor fault detection and diagnosis (FDD) method for electrical traction systems. Considering their switched characteristics, electrical traction systems can be regarded as switched systems. A mixture non-Gaussian data set will be formed, which can be firstly divided into six different operation modes, and principal component analysis (PCA) is then used for feature extraction in each mode. For two fault indicators in principal and residual subspaces, their probability density functions (PDFs) are estimated and used to determine reasonable thresholds for FDD. The proposed methodology extends the application of multivariate statistical technology to electrical traction systems. It can be applied easily and effectively without requirements on system parameters, and can deal with incipient sensor faults in traction system. Experiments with several different types of incipient sensor faults are conducted, which can demonstrate the effectiveness of the proposed method.
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Qing Luo,Jia Zhang,Hongtian Wang,Fenghong Chen,Xi Luo,Beiping Miao,Xingmei Wu,Renqiang Ma,Xiangqian Luo,Geng Xu,Jianbo Shi,Huabin Li 대한천식알레르기학회 2015 Allergy, Asthma & Immunology Research Vol.7 No.5
Purpose: Chronic rhinosinusitis (CRS) is characterized by the excessive production of mucus. However, the molecular mechanism underlying mucin overproduction in CRS with or without nasal polyps (CRSwNP and CRSsNP, respectively) is poorly understood. This study was conducted to assess the importance of the transcription factor FoxA2 in mucin production and to investigate the targeting of FoxA2 as a potential therapeutic strategy for mucus hypersecretion in CRS patients. Methods: We enrolled 15 CRSwNP patients, 15 CRSsNP patients, and 10 normal controls in this study. The expression levels of FoxA2, MUC5AC, and MUC5B in inflamed and healthy nasal tissues were examined via immunohistochemistry and quantitative reverse transcription-polymerase chain reaction, and the levels of several proinflammatory cytokines in nasal secretions were measured via FlowCytomix analysis. In addition, the expression of MUC5AC and FoxA2 was determined in polyp-derived epithelial cells and NCI-H292 cells after in vitro stimulation. Results: FoxA2 was significantly down-regulated, and MUC5AC and MUC5B were significantly up-regulated in both the CRSwNP and CRSsNP patients compared to the controls (P<0.05), and the protein level of FoxA2 was negatively associated with the IL-6 level in the CRS patients (P<0.05). IL-6 significantly increased MUC5AC expression but inhibited FoxA2 expression in vitro (P<0.05). Transfection with a FoxA2 expression plasmid significantly decreased MUC5AC promoter activity (P<0.05) and inhibited IL-6-induced MUC5AC production (P<0.05). In addition, clarithromycin significantly alleviated IL-6-induced FoxA2 suppression and decreased MUC5AC expression in vitro (P<0.05). Conclusions: Our findings suggest that FoxA2 may be considered a therapeutic target for the modulation of mucus hypersecretion in CRS patients.
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Just-in-time Learning-aided Nonlinear Fault Detection for Traction Systems of High-speed Trains
Chao Cheng,Xiuyuan Sun,Junjie Shao,Hongtian Chen,Chao Shang 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.9
Traction systems in high-speed trains exhibit significant dynamic characteristics, which mainly arise from operation-point changes. Most existing fault detection methods provide static data models for global structures, especially for traditional multivariate statistical analysis methods constrained by constant operating points. The symptoms of incipient faults are slight and easily hidden. Despite the moderate effect of incipient faults, they will compromise the overall performance and remaining life of traction systems in the long run. Therefore, a just-in-time slow feature analysis method is proposed in this study. The salient advantages of the proposed method are: 1) It can be applied to dynamic non-linear systems; 2) It can detect incipient faults subject to environments containing noise and unknown disturbances; 3) It mitigates false alarms caused by parameter mutation during mode-switching. A series of experiments are carried out on a traction system platform to verify the effectiveness and superiority of the proposed method.
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Chao Cheng,Xin Wang,Shuiqing Xu,Ke Feng,Hongtian Chen 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.6
The running gear system provides the safety guarantee for the normal operation of high-speed trains. The massive historical data in the system can be used for fault detection and diagnosis. This data inevitably exists redundancy, which makes the valuable data not fully utilized in the process of extracting latent variables. In this paper, to make full and effective use of historical data, a dynamic weighted slow feature analysis (DWSFA) method is proposed, which can detect slow-change faults in the running gear system of high-speed trains. The proposed method based on basis functions can reduce the amount of time lags required for the process of extracting latent variables, and it obtains the better fault detection (FD) performance. The effectiveness of the proposed method is verified via a running gear system of high-speed train.
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