Automated Process Quality Monitoring with Prediction of Fault Condition Using Measurement Data
Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
[1] Y. S. Nga, R. Srinivasana, "An adjoined multi-model approach for
monitoring batch and transient operations," Computers and Chemical
Engineering, vol. 33, pp. 887-902, 2009.
[2] V. Vapnik, "The Nature of Statistical Learning Theory," Springer-Verlag,
1995, New York, NY.
[3] B. Schölkopf, A. J. Smola, and K. M├╝ller, "Nonlinear component analysis
as a kernel eigenvalue problem," Neural Computation, vol. 10, pp.
1299-1319, 1998.
[4] R. Rosipal, and L. J. Trejo, "Kernel partial least squares regression in
reproducing Kernel Hilbert space," Journal of Machine Learning
Research, vol. 2, pp. 97-123, 2001.
[5] G. Baudat, and F. Anouar, "Generalized discriminant analysis using a
kernel approach," Neural Computation, vol. 12, pp. 2385-2404, 2000.
[6] J. Trygg, and S. Wold, "Orthogonal projections to latent structures
(O-PLS)," Journal of Chemometrics, vol. 16, pp. 19-128, 2002.
[7] S. Yu, K. Yu, V. Tresp, H. Kriegel, andM.Wu, "Supervised probabilistic
principal component analysis. In: Proceedings of the 12th international
conference on knowledge discovery and data mining (SIGKDD), pp
464-473, 2006.
[8] K. Kourti, "Application of latent variable methods to process control and
multivariate statistical process control in industry," International Journal
of Adaptive Control and Signal Processing, vol. 19, pp. 213-246, 2005.
[9] R. Leardi, and A. L. Gonzalez, "Genetic algorithms applied to feature
selection in PLS regression: how and when to use them," Chemometrics
Intelligent Laboratory Systems, vol. 41, pp. 195-207, 1998.
[10] A. Durand, O. Devos, C. Ruckebusch, and J. P. Huvenne, "Genetic
algorithm optimisation combined with partial least squares regression
and mutual information variable selection procedures in near-infrared
quantitative analysis of cotton-viscose textiles," Analytica Chimica Acta,
vol. 595, pp. 72-79, 2007.
[11] C. S.Soh, P. Raveendran, and R. Mukundan, "Mathematical models for
prediction of active substance content in pharmaceutical tablets and
moisture in wheat," Chemometrics and Intelligent Laboratory Systems,
vol. 93, pp. 63-69, 2008.
[12] Y. Shao, and Y. He, "Nondestructive measurement of the internal quality
of bayberry juice using Vis/NIR spectroscopy," Journal of Food
Engineering, vol. 79, pp. 1015-1019, 2007.
[1] Y. S. Nga, R. Srinivasana, "An adjoined multi-model approach for
monitoring batch and transient operations," Computers and Chemical
Engineering, vol. 33, pp. 887-902, 2009.
[2] V. Vapnik, "The Nature of Statistical Learning Theory," Springer-Verlag,
1995, New York, NY.
[3] B. Schölkopf, A. J. Smola, and K. M├╝ller, "Nonlinear component analysis
as a kernel eigenvalue problem," Neural Computation, vol. 10, pp.
1299-1319, 1998.
[4] R. Rosipal, and L. J. Trejo, "Kernel partial least squares regression in
reproducing Kernel Hilbert space," Journal of Machine Learning
Research, vol. 2, pp. 97-123, 2001.
[5] G. Baudat, and F. Anouar, "Generalized discriminant analysis using a
kernel approach," Neural Computation, vol. 12, pp. 2385-2404, 2000.
[6] J. Trygg, and S. Wold, "Orthogonal projections to latent structures
(O-PLS)," Journal of Chemometrics, vol. 16, pp. 19-128, 2002.
[7] S. Yu, K. Yu, V. Tresp, H. Kriegel, andM.Wu, "Supervised probabilistic
principal component analysis. In: Proceedings of the 12th international
conference on knowledge discovery and data mining (SIGKDD), pp
464-473, 2006.
[8] K. Kourti, "Application of latent variable methods to process control and
multivariate statistical process control in industry," International Journal
of Adaptive Control and Signal Processing, vol. 19, pp. 213-246, 2005.
[9] R. Leardi, and A. L. Gonzalez, "Genetic algorithms applied to feature
selection in PLS regression: how and when to use them," Chemometrics
Intelligent Laboratory Systems, vol. 41, pp. 195-207, 1998.
[10] A. Durand, O. Devos, C. Ruckebusch, and J. P. Huvenne, "Genetic
algorithm optimisation combined with partial least squares regression
and mutual information variable selection procedures in near-infrared
quantitative analysis of cotton-viscose textiles," Analytica Chimica Acta,
vol. 595, pp. 72-79, 2007.
[11] C. S.Soh, P. Raveendran, and R. Mukundan, "Mathematical models for
prediction of active substance content in pharmaceutical tablets and
moisture in wheat," Chemometrics and Intelligent Laboratory Systems,
vol. 93, pp. 63-69, 2008.
[12] Y. Shao, and Y. He, "Nondestructive measurement of the internal quality
of bayberry juice using Vis/NIR spectroscopy," Journal of Food
Engineering, vol. 79, pp. 1015-1019, 2007.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52144", author = "Hyun-Woo Cho", title = "Automated Process Quality Monitoring with Prediction of Fault Condition Using Measurement Data", abstract = "Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.", keywords = "Prediction, operation monitoring, on-line data,nonlinear statistical methods, empirical model.", volume = "7", number = "5", pages = "806-5", }