Research on PCA Based Sensor Fault Detection and its Application

Li Cheng; Zhi Gang Yao

doi:10.4028/www.scientific.net/AMM.404.508

Paper Titles

The State Recognition for Rotary Machines Based on Fractal Theory and Neural Network
p.485

Super-Resolution Imaging in a Close-Packed Synthetic Aperture Digital Holographic Microscopy
p.490

Study on Hidden Information of Microelectronic Packaging Image Series with PCA
p.495

Measurement of Static Performance of Inductive Proximity Switch for a Mobile Robot
p.502

Research on PCA Based Sensor Fault Detection and its Application
p.508

Key-Frame Extraction Method from Single-Lens Undersea Video Sequences
p.514

The Nonparametric Confidence Interval for the Process Capability Index C*_pmk
p.520

An Extrema Extension Method Based on Support Vector Regression for Restraining the End Effects in Empirical Mode Decomposition
p.526

An Improve Firefly Algorithm and its Application
p.533

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 404Research on PCA Based Sensor Fault Detection and...

Research on PCA Based Sensor Fault Detection and its Application

Abstract:

This paper deals with problem related to fault detection in complex process with various sorts of sensors. Not model based but data-driven multivariate statistical process monitoring approach PCA is proposed, fault detection indices statistic and square prediction error are discussed and their complementary relationship is also presented. Availability and reliability of the method proposed in this paper is verified by experimental example.

You might also be interested in these eBooks

Engineering Decisions for Manufacturing Systems

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 404)

Pages:

508-513

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.404.508

Citation:

Cite this paper

Online since:

September 2013

Authors:

Li Cheng, Zhi Gang Yao

Keywords:

Fault Detection, Principal Component Analysis (PCA), Process Monitoring, Q Statistic, T² Statistic

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Z.G. Yao, L. Cheng and Q.L. Wang. Sensor fault detection, diagnosis and validation-a survey. Applied Mechanics and Materials. Vol. 229-231: 1265-1271, (2012).

DOI: 10.4028/www.scientific.net/amm.229-231.1265

Google Scholar

[2] S.X. Ding, P. Zhang, A. Naik, E.L. Ding and B. Huang. Subspace method aided data-driven design of fault detection and isolation systems. Journal of process control, 19(2009): 1496-1510.

DOI: 10.1016/j.jprocont.2009.07.005

Google Scholar

[3] Y. L Wang, G.F. Ma, S.X. Ding and C.J. Li. Subspace aided data-driven design of robust fault detection and isolation systems. Automatica, 47(2011): 2474-2480.

DOI: 10.1016/j.automatica.2011.05.028

Google Scholar

[4] R.J. Patton, P.M. Frank, and R.N. Clark. Issues of fault diagnosis for dynamic systems. Berlin: Springer-Verlag, (2000).

Google Scholar

[5] J. Chen and R.J. Patton. Robust model-based fault diagnosis for dynamic systems. Boston: Kluwer academic publishers, (1999).

Google Scholar

[6] S.X. Ding. Model-based fault diagnosis techniques design schemes algorithms and tools. Berlin: Springer-Verlag, (2008).

Google Scholar

[7] S.J. Qin. Statistical process monitoring: basics and beyond. J. Chemometrics 2003, 17: 480-502.

DOI: 10.1002/cem.800

Google Scholar

[8] J.E. Jackson. A user's guide to principal components. Wiley Interscience, New York, USA, (1991).

Google Scholar

[9] I.T. Jolliffe. Principal component analysis. Spring Verlag, Berlin, (1986).

Google Scholar

[10] S. Valle, W. Li and S.J. Qin. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Industrial and Engineering Chemistry Research, 38(11): 4389-4401, (1999).

DOI: 10.1021/ie990110i

Google Scholar