Incipient fault detection with smoothing techniques in statistical process monitoring

被引:136
作者
Ji, Hongquan [1 ,2 ]
He, Xiao [1 ,2 ]
Shang, Jun [1 ,2 ]
Zhou, Donghua [1 ,2 ,3 ]
机构
[1] Tsinghua Univ, TNList, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[3] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China
基金
中国国家自然科学基金;
关键词
Incipient fault detection; Fault detectability; Quadratic form; Smoothing technique; Multivariate statistical process monitoring; DIAGNOSIS; IDENTIFICATION; DISTURBANCE; PROJECTION;
D O I
10.1016/j.conengprac.2017.03.001
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In modern industry, detecting incipient faults timely is of vital importance to prevent serious system performance deterioration and ensure optimal process operation. Recently, multivariate statistical process monitoring (MSPM) techniques have been extensively studied and widely applied to modern industrial systems. However, conventional fault detection indices utilized in statistical process monitoring are not sensitive to incipient faults with small magnitude. In this paper, by introducing two representative smoothing techniques, novel incipient fault detection strategies based on a generic fault detection index in MSPM are proposed. Fault detectability for each proposed strategy is analyzed. In addition, the effects of the smoothing parameters on fault detection, including advantages and disadvantages, are also investigated. Finally, case studies on a numerical example and two practical industrial processes are carried out to demonstrate the effectiveness of the proposed incipient fault detection strategies.
引用
收藏
页码:11 / 21
页数:11
相关论文
共 38 条
[1]  
[Anonymous], 2015, THESIS
[2]   Data-driven Fault Detection and Diagnosis for HVAC water chillers [J].
Beghi, A. ;
Brignoli, R. ;
Cecchinato, L. ;
Menegazzo, G. ;
Rampazzo, M. ;
Simmini, F. .
CONTROL ENGINEERING PRACTICE, 2016, 53 :79-91
[3]   SOME THEOREMS ON QUADRATIC FORMS APPLIED IN THE STUDY OF ANALYSIS OF VARIANCE PROBLEMS .1. EFFECT OF INEQUALITY OF VARIANCE IN THE ONE-WAY CLASSIFICATION [J].
BOX, GEP .
ANNALS OF MATHEMATICAL STATISTICS, 1954, 25 (02) :290-302
[4]   Principle component analysis based control charts with memory effect for process monitoring [J].
Chen, JH ;
Liao, CM ;
Lin, FRJ ;
Lu, MJ .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2001, 40 (06) :1516-1527
[5]   Probabilistic contribution analysis for statistical process monitoring: A missing variable approach [J].
Chen, Tao ;
Sun, Yue .
CONTROL ENGINEERING PRACTICE, 2009, 17 (04) :469-477
[6]   Canonical correlation analysis-based fault detection methods with application to alumina evaporation process [J].
Chen, Zhiwen ;
Ding, Steven X. ;
Zhang, Kai ;
Li, Zhebin ;
Hu, Zhikun .
CONTROL ENGINEERING PRACTICE, 2016, 46 :51-58
[7]   Fault diagnosis based on Fisher discriminant analysis and support vector machines [J].
Chiang, LH ;
Kotanchek, ME ;
Kordon, AK .
COMPUTERS & CHEMICAL ENGINEERING, 2004, 28 (08) :1389-1401
[8]   Subspace approach to multidimensional fault identification and reconstruction [J].
Dunia, R ;
Qin, SJ .
AICHE JOURNAL, 1998, 44 (08) :1813-1831
[9]   Review of Recent Research on Data-Based Process Monitoring [J].
Ge, Zhiqiang ;
Song, Zhihuan ;
Gao, Furong .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2013, 52 (10) :3543-3562
[10]   A Novel Statistical-Based Monitoring Approach for Complex Multivariate Processes [J].
Ge, Zhiqiang ;
Xie, Lei ;
Song, Zhihuan .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2009, 48 (10) :4892-4898