Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30063
Automated Process Quality Monitoring with Prediction of Fault Condition Using Measurement Data

Authors: Hyun-Woo Cho

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 The calibration model is used to predict two faulty conditions from historical reference data.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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058343

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF

References:


[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.