Incipient Fault Detection Using Transformed Component Statistical Analysis

Abstract

Abstract:

Abstract： Incipient fault detection is of significant importance for preventing the occurrence of accidents. A multivariate analysis method named transformed component statistical analysis (TCSA) was proposed to solve the incipient fault detection problem. The algorithm processes the data in the sliding time window to extract transformed components. Statistics (mean, variance, skewness, kurtosis) of transformed components were monitored to realize the detection of incipient faults. The transformed components extracted by the approach are linear combinations of the normalized data. Statistics of transformed components can reflect some invariants under normal condition. Some incipient faults break the balance and therefore can be detected. Numerical simulation and Tennessee Eastman process (TEP) simulation indicate that TCSA is able to detect both incipient sensor faults and process faults effectively.

Key words: incipient fault, transformed component statistical analysis, fault detection

CLC Number:

TP 277

SAHNG Jun,CHEN Maoyin,ZHOU Donghua. Incipient Fault Detection Using Transformed Component Statistical Analysis[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 799-805.

[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]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	LIU Yang,HE Xiao,ZHOU Donghua. Fault Detection for a Class of Closed-Loop Systems with Distributed Measurements [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 757-761.
[9]	CONG Ya,GE Zhiqiang,SONG Zhihuan. Multi-Rate Principle Component Analysis for Process Monitoring [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 762-767.
[10]	GUO Tianxu，CHEN Maoyin，ZHOU Donghua. A Fault Detection Method of Non-Gaussian Processes and Small Shift [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 775-779.
[11]	NING Chao,CHEN Maoyin,ZHOU Donghua. Fault Reconstruction for Multiple Failure Modes Based on Threshold Fault Subspace Extraction Algorithm [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 780-785.
[12]	LIU Xiaodong,ZHONG Maiying,LIU Hai. EKF-Based Fault Detection of Unmanned Aerial Vehicle Flight Control System [J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 884-888.
[13]	SU Xian-li1,2 (苏先礼), ZHAN Xing-qun1* (战兴群), NIU Man-cang1 (牛满仓), ZHANG Yan-hua1 (张炎华). Receiver Autonomous Integrity Monitoring Availability and Fault Detection Capability Comparison Between BeiDou and GPS [J]. Journal of shanghai Jiaotong University (Science), 2014, 19(3): 313-324.
[14]	CAI Lianfang,TIAN Xuemin,ZHANG Ni. A Nonlinear Dynamic Process Fault Detection Method Based on Kernel State Space Independent Component Analysis [J]. Journal of Shanghai Jiaotong University, 2014, 48(07): 971-976.
[15]	FANG Ting,DONG Chong,HU Xingliu,WANG Yan. Contour Extraction and Fault Detection of Insulator Strings in Aerial Images [J]. Journal of Shanghai Jiaotong University, 2013, 47(12): 1818-1822.

Incipient Fault Detection Using Transformed Component Statistical Analysis

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments