Continuous health assessment using a single Hidden Markov model

11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010

Volumn , Issue , 2010, Pages 1347-1352

  Geramifard, O ^a   Xu, J X ^a   Zhou, J H ^b   Li, X ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

Author keywords

ARMAX; Health condition monitoring; Hidden Markov model; Singular value decomposition; Variance inflation factor

Indexed keywords

ARMAX; ARMAX MODEL; AUTO-REGRESSIVE MOVING AVERAGE MODEL; DATA SETS; EXOGENOUS INPUT; FEATURE SPACE; FORCE SIGNAL; HEALTH ASSESSMENTS; REAL TIME; SINGLE HIDDEN MARKOV MODEL; SINGULAR VALUES; STATISTICAL APPROACH; TEMPORAL MODELS; VARIANCE INFLATION FACTOR;

COMPUTER VISION; CONDITION MONITORING; FEATURE EXTRACTION; HEALTH; MILLING (MACHINING); MILLING MACHINES; ROBOTICS; SINGULAR VALUE DECOMPOSITION;

HIDDEN MARKOV MODELS;

EID: 79952433552     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICARCV.2010.5707866     Document Type: Conference Paper

References (14)

1. S. Jie, G. S. Hong, M. Rahman, and Y. S. Wong, "Feature Extraction and Selection in Tool Condition Monitoring System," vol. 2557, pp. 487-497, 2002.
2. J.H. Zhou, C. K. Pang, F. L. Lewis, and Z.W. Zhong, "Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification, " IEEE transactions on Industrial Informatics, 2009.
3. J. A. Rice, Mathematical Statistics and Data Analysis. Duxbury Press, April 2001.
4. R. M. O'brien, "A Caution Regarding Rules of Thumb for Variance Inflation Factors," Quality and Quantity, vol. 41, no. 5, pp. 673-690, 2007.
5. V. Klema and A. Laub, "The singular value decomposition: Its computation and some applications," Automatic Control, IEEE Transactions on, vol. 25, no. 2, pp. 164-176, 1980.
6. K. Konstantinides and K. Yao, "Statistical analysis of effective singular values in matrix rank determination," IEEE Trans. Acoust., Speech Signal Process., vol. 36, no. 5, pp. 757-763, May 1988.
7. G. C. Reinsel, Elements of Multivariate Time Series Analysis. Springer, 1997.
8. L. Ljung, System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1987.
9. K. P. Murphy, "Dynamic Bayesian Networks: Representation, Inference and Learning," PhD, UNIVERSITY OF CALIFORNIA, BERKELEY, USA, 2002.
10. Z. Ghahramani, "Learning Dynamic Bayesian Networks," LECTURE NOTES IN COMPUTER SCIENCE, no. 1387, pp. 168-197, 1998.
11. R. E. Neapolitan, Ed., Learning Bayesian Networks. Prentice Hall, 2004, pp. 674.
12. P. Smyth, "hidden markov models for fault detection in dynamic systems," pattern recognition, vol. 27, no. 1, pp. 149-164, 1994.
13. A. K. S. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483-1510, 2006.
14. A. Heng, S. Zhanga, A. C. C. Tana, and J. Mathew, "Rotating machinery prognostics: State of the art, challenges and opportunities," Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 724-739, 2009.