- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 937196, 17 pages
A Fault Prognosis Strategy Based on Time-Delayed Digraph Model and Principal Component Analysis
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Received 30 August 2012; Accepted 15 November 2012
Academic Editor: Huaguang Zhang
Copyright © 2012 Ningyun Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess, and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley & Sons, 2006.
- B. Jiang and F. N. Chowdhury, “Fault estimation and accommodation for linear MIMO discrete-time systems,” IEEE Transactions on Control Systems Technology, vol. 13, no. 3, pp. 493–499, 2005.
- B. Jiang, M. Staroswiecki, and V. Cocquempot, “Fault accommodation for nonlinear dynamic systems,” IEEE Transactions on Automatic Control, vol. 51, no. 9, pp. 1578–1583, 2006.
- M. A. Schwabacher, “A survey of data-driven prognostics,” in Proceedings of InfoTech at Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration, pp. 887–891, September 2005.
- M. Schwabacher and K. Goebel, “A survey of artificial intelligence for prognostics,” in Proceedings of AAAI Fall Symposium on Artificial Intelligence for Prognostics, pp. 107–114, Arlington, Va, USA, November 2007.
- J. Arnhold, P. Grassberger, K. Lehnertz, and C. E. Elger, “A robust method for detecting interdependences: application to intracranially recorded EEG,” Physica D, vol. 134, no. 4, pp. 419–430, 1999.
- B. S. Caldwell, “Knowledge sharing and expertise coordination of event response in organizations,” Applied Ergonomics, vol. 39, no. 4, pp. 427–438, 2008.
- S. J. Schiff, P. So, T. Chang, R. E. Burke, and T. Sauer, “Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble,” Physical Review E, vol. 54, no. 6, pp. 6708–6724, 1996.
- J. Y. Wang, Z. J. Shao, P. Ji, K. T. Yao, and Z. Q. Chen, “Online diagnosis of abnormal conditions of air separation process by dynamic PCA,” Computers and Applied Chemistry, vol. 27, no. 1, pp. 1–5, 2010.
- J. Pearl, Causality: Models, Reasoning and Inference, Cambridge University Press, New York, NY, USA, 2000.
- C. W. J. Granger, “Investigating causal relations by econometric and cross-spectral methods,” Econometrica, vol. 37, pp. 424–438, 1969.
- C. W. J. Granger, “Testing for causality: a personal viewpoint,” Journal of Economic Dynamics & Control, vol. 2, no. 4, pp. 329–352, 1980.
- K. Hlaváčková-Schindler, M. Paluš, M. Vejmelka, and J. Bhattacharya, “Causality detection based on information-theoretic approaches in time series analysis,” Physics Reports, vol. 441, no. 1, pp. 1–46, 2007.
- T. Schreiber, “Measuring information transfer,” Physical Review Letters, vol. 85, no. 2, pp. 461–464, 2000.
- M. Paluš, V. Komárek, Z. Hrnčíř, and K. Štěrbová, “Synchronization as adjustment of information rates: detection from bivariate time series,” Physical Review E, vol. 63, no. 4, pp. 462111–462116, 2001.
- J. Jeong, J. C. Gore, and B. S. Peterson, “Mutual information analysis of the EEG in patients with Alzheimer's disease,” Clinical Neurophysiology, vol. 112, no. 5, pp. 827–835, 2001.
- S. H. Na, S. H. Jin, S. Y. Kim, and B. J. Ham, “EEG in schizophrenic patients: mutual information analysis,” Clinical Neurophysiology, vol. 113, no. 12, pp. 1954–1960, 2002.
- M. Bauer and N. F. Thornhill, “A practical method for identifying the propagation path of plant-wide disturbances,” Journal of Process Control, vol. 18, no. 7-8, pp. 707–719, 2008.
- V. T. Tran, B. S. Yang, and A. C. C. Tan, “Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems,” Expert Systems with Applications, vol. 36, no. 5, pp. 9378–9387, 2009.
- A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information,” Physical Review A, vol. 33, no. 2, pp. 1134–1140, 1986.
- S. H. Jin, P. Lin, and M. Hallett, “Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction,” Clinical Neurophysiology, vol. 121, no. 3, pp. 392–401, 2010.
- M. Bauer, J. W. Cox, M. H. Caveness, J. J. Downs, and N. F. Thornhill, “Finding the direction of disturbance propagation in a chemical process using transfer entropy,” IEEE Transactions on Control Systems Technology, vol. 15, no. 1, pp. 12–21, 2007.
- N. F. Thornhill, J. W. Cox, and M. A. Paulonis, “Diagnosis of plant-wide oscillation through data-driven analysis and process understanding,” Control Engineering Practice, vol. 11, no. 12, pp. 1481–1490, 2003.
- S. L. Ho and M. Xie, “The use of ARIMA models for reliability forecasting and analysis,” Computers and Industrial Engineering, vol. 35, no. 1–4, pp. 213–216, 1998.
- L. Datong, P. Yu, and P. Xiyuan, “Fault prediction based on time series with online combined kernel SVR methods,” in Proceedings of IEEE Intrumentation and Measurement Technology Conference (I2MTC '09), pp. 1163–1166, Singapore, May 2009.
- S. K. Yang, “An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor,” Reliability Engineering and System Safety, vol. 75, no. 1, pp. 103–111, 2002.
- E. A. Rietman and M. Beachy, “A study on failure prediction in a plasma reactor,” IEEE Transactions on Semiconductor Manufacturing, vol. 11, no. 4, pp. 670–680, 1998.
- R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society B, vol. 58, no. 1, pp. 267–288, 1996.
- S. Ben Taieb, G. Bontempi, A. F. Atiya, and A. Sorjamaa, “A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition,” Expert Systems with Applications, vol. 39, no. 8, pp. 7067–7083, 2012.
- N. Lu, F. Gao, Y. Yang, and F. Wang, “PCA-based modeling and on-line monitoring strategy for uneven-length batch processes,” Industrial and Engineering Chemistry Research, vol. 43, no. 13, pp. 3343–3352, 2004.
- L. Zhu, Z. Chen, X. Chen, Z. Shao, and J. Qian, “Simulation and optimization of cryogenic air separation units using a homotopy-based backtracking method,” Separation and Purification Technology, vol. 67, no. 3, pp. 262–270, 2009.
- Z. Xu, J. Zhao, X. Chen et al., “Automatic load change system of cryogenic air separation process,” Separation and Purification Technology, vol. 81, no. 3, pp. 451–465, 2011.