Journal of Shanghai Jiaotong University ›› 2015, Vol. 49 ›› Issue (06): 775-779.
• Automation Technique, Computer Technology • Previous Articles Next Articles
GUO Tianxu,CHEN Maoyin,ZHOU Donghua
Received:
2015-03-26
Online:
2015-06-29
Published:
2015-06-29
GUO Tianxu,CHEN Maoyin,ZHOU Donghua. A Fault Detection Method of Non-Gaussian Processes and Small Shift[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 775-779.
[1]Kandula V K. Fault detection in process control plants using principal component analysis [D]. India: VIT University, 2011.[2]GarciaAlvarez D. Fault detection using principal component analysis (PCA) in a wastewater treatment plant (WWTP) [C]∥Proceedings of the International Student’s Scientific Conference. St Petersburg, Russia: [s.n.], 2009.[3]Qin S J. Statistical process monitoring: Basics and beyond [J]. Journal of Chemometrics, 2003, 17(89): 480502.[4]Qin S J. Survey on datadriven industrial process monitoring and diagnosis [J]. Annual Reviews in Control, 2012, 36(2): 220234.[5]Zhang Y, Zhang Y. Fault detection of nonGaussian processes based on modified independent component analysis [J]. Chemical Engineering Science, 2010, 65(16): 46304639.[6]Ning C, Chen M, Zhou D. Hidden Markov modelbased statistics pattern analysis for multimode process monitoring: An indexswitching scheme[J]. Industrial and Engineering Chemistry Research, 2014, 53(27): 1108411095. [7]葛志强, 宋执环, 杨春节. 基于 MCUSUMICAPCA 的微小故障检测[J]. 浙江大学学报: 工学版, 2008, 42(3): 373377.GE Zhiqiang, SONG Zhihuan, YANG Chunjie. Small shift detection based on MCUSUMICAPCA[J]. Journal o f Zhejiang University: Engineering Science, 2008, 42(3): 373377.[8]Zeng J, Kruger U, Geluk J, et al. Detecting abnormal situations using the KullbackLeibler divergence[J]. Automatica, 2014, 50(11): 27772786.[9]Harmouche J, Delpha C, Diallo D. Incipient fault detection and diagnosis based on KullbackLeibler divergence using principal component analysis: Part I[J]. Signal Processing, 2014, 94: 278287.[10]Harmouche J, Delpha C, Diallo D. Incipient fault detection and diagnosis based on KullbackLeibler divergence using principal component analysis: Part II[J]. Signal Processing, 2015, 109: 334344.[11]葛志强, 杨春节, 宋执环. 基于 MEWMAPCA 的微小故障检测方法研究及其应用[J]. 信息与控制, 2007, 36(5): 650656.GE Zhiqiang, YANG Chunjie, SONG Zhihuan. Research and application of small shifts detection method based on MEWMAPCA[J]. Information and Control, 2007, 36(5): 650656.[12]Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210227.[13]Wright J, Ma Y, Mairal J, et al. Sparse representation for computer vision and pattern recognition [J]. Proceedings of the IEEE, 2010, 98(6): 10311044.[14]Ren L, Lü W. Fault detection via sparse representation for semiconductor manufacturing processes [J]. IEEE Transactions on Semiconductor Manufacturing,2014,27(2): 252259. |
[1] | LI Yuan, YAO Zongyu. Principal Polynomial Nonlinear Process Fault Detection Based on Neighborhood Preserving Embedding [J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 1001-1008. |
[2] | HE Xiawei, CAI Yunze, YAN Lingling. A Combined Residual Detection Method of Reaction Wheel for Fault Detection [J]. Journal of Shanghai Jiao Tong University, 2021, 55(6): 716-728. |
[3] | LIU Ziwen (刘子文), XIAO Lei (肖雷), BAO Jinsong (鲍劲松), TAO Qingbao (陶清宝) . Bearing Incipient Fault Detection Method Based on Stochastic Resonance with Triple-Well Potential System [J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 482-487. |
[4] | XU Qiaoning,AI Qinglin,DU Xuewen,LIU Yi. An Integrated Model-Based and Data-Driven Method for Early Fault Detection of a Ship Rudder Electro-Hydraulic Servo System [J]. Journal of Shanghai Jiaotong University, 2020, 54(5): 451-464. |
[5] | LIU Mingguang, LIAO Yaxuan, LI Xiangshun . Data-Driven Fault Detection of Three-Tank System Applying MWAT-ICA [J]. J Shanghai Jiaotong Univ Sci, 2020, 25(5): 659-664. |
[6] | ZHAO Ting, WANG Shentao, NIU Lin, XI Peili, CAI Yunze. Detection Algorithm of Ship Wake in SAR Images [J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1259-1268. |
[7] | HUANG Jian, YANG Xu. Online Weighted Slow Feature Analysis Based Fault Detection Algorithm [J]. Journal of Shanghai Jiao Tong University, 2020, 54(11): 1142-1150. |
[8] | PENG Rui (彭锐), MA Xiaoyang *(马晓洋), ZHAI Qingqing (翟庆庆), GAO Kaiye (高凯烨). Software Reliability Growth Model Considering First-Step and Second-Step Fault Dependency [J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(4): 477-479. |
[9] | XU Xiaoling (徐晓玲), LIU Yiling (刘沂玲), LIU Qiegen (刘且根),LU Hongyang (卢红阳), ZHANG Minghui (张明辉). Gradient-Based Low Rank Method for Highly Undersampled Magnetic Resonance Imaging Reconstruction [J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(3): 384-. |
[10] |
WANG Xing,ZHOU Yipeng,TIAN Yuanrong,CHEN You,ZHOU Dongqing,HE Jiyuan.
Sparse Decomposition for Frequency Modulation Radar Signal Based on Advanced Genetic Algorithm and SinChirplet Atom [J]. Journal of Shanghai Jiaotong University, 2017, 51(9): 1124-1130. |
[11] | L ¨U Wentao1* (吕文涛), WANG Junfeng2 (王军锋), YU Wenxian2 (郁文贤), BAO Xiaomin1 (包晓敏). Range Profile Target Recognition Using Sparse Representation Based on Feature Space [J]. Journal of shanghai Jiaotong University (Science), 2017, 22(5): 615-623. |
[12] | BAI Caijuan1,LIU Jing1,JIANG Xiaoyu2,ZHANG Guoxian1,HUANG Kaiyu1. The Reconstruction of Digital Holography Based on Iterative De-Noising Shrinkage-Thresholding Algorithm [J]. Journal of Shanghai Jiaotong University, 2017, 51(12): 1435-1442. |
[13] | Ming-hui ZHANG, Xiao-yang HE, Shen-yuan DU, Qie-gen* LIU. A Generalized Two-Level Bregman Method with Dictionary Updating for Non-Convex Magnetic Resonance Imaging Reconstruction [J]. Journal of Shanghai Jiao Tong University(Science), 2015, 20(6): 660-669. |
[14] | NING Chao,CHEN Maoyin,ZHOU Donghua. Fault Reconstruction for Multiple Failure Modes Based on Threshold Fault Subspace Extraction Algorithm [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 780-785. |
[15] | SAHNG Jun,CHEN Maoyin,ZHOU Donghua. Incipient Fault Detection Using Transformed Component Statistical Analysis [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 799-805. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||