Using Curve-Registration Information for Profile Monitoring

References

• Altman, N. S. and Villarreal, J. C. (2004). “Self-Modelling Regression for Longitudinal Data with Time-Invariant Covariates”. The Canadian Journal of Statistics 32, pp. 251–268.
   Web of Science ®Google Scholar
• Annoni, M.; Cristaldi, L.; and Lazzaroni, M. (2008). “Measurements, Analysis and Interpretation of The Signals from a High-Pressure Waterjet Pump”. IEEE Transactions on Instrumentation and Measurement 57 (1), pp. 34–47.
   Web of Science ®Google Scholar
• Bowman, A. W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis. New York, NY: Oxford University Press.
   Google Scholar
• Chang, S. I. and Yadama, S. (2010). “Statistical Process Control for Monitoring Non-Linear Profiles Using Wavelet Filtering and B-Spline Approximation”. International Journal of Production Research 48 (4), pp. 1049–1068.
   Web of Science ®Google Scholar
• Chen, C. S. and Hung, Y. P. (1999). “RANSAC-Based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images”. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, pp. 1229–1234.
   Web of Science ®Google Scholar
• Chou, Y.-M.; Mason, R. L.; and Young J. C. (2001). “The Control Chart for Individual Observations from a Multivariate Non-Normal Distribution”. Communications in Statistics, Simulation and Computation 30 (8–9), pp. 1937–1949.
   Google Scholar
• Colosimo, B. M. and Pacella, M. (2007). “On the Use of Principal Component Analysis to Identify Systematic Patterns in Roundness Profiles”. Quality and Reliability Engineering International 23, pp. 925–941.
   Web of Science ®Google Scholar
• Colosimo, B. M. and Pacella, M. (2010). “A Comparison Study of Control Charts for Statistical Monitoring of Functional Data”. International Journal of Production Research 23, pp. 707–725.
   Google Scholar
• Del Castillo, E. and Colosimo, B. M. (2011). “Statistical Shape Analysis of Experiments for Manufacturing Processes”. Technometrics 53 (1), pp. 1–15.
   Web of Science ®Google Scholar
• Ding, Y.; Zeng, L.; and Zhou, S. (2006). “Phase I Analysis for Monitoring Nonlinear Profiles in Manufacturing Processes”. Journal of Quality Technology 38 (3), pp. 199–216.
   Web of Science ®Google Scholar
• Dryden, I. L. and Mardia, K. V. (1998). Statistical Shape Analysis, vol. 4. New York, NY: John Wiley & Sons.
   Google Scholar
• Eilers, P. H. (2004). “Parametric Time Warping”. Analytical Chemistry 76 (2), pp. 404–411.
   PubMed Web of Science ®Google Scholar
• Gao, X. (2012). “On-line Monitoring of Batch Process with Multiway PCA/ICA”. In Principal Component Analysis–Multidisciplinary Application, chapter 13. Intech Ed.
   Google Scholar
• Gasser, T.; Kneip, A.; Ziegler, P.; Largo, R.; and Prader, A. (1990). “A Method for Determining the Dynamics and Intensity of Average Growth”. Annals of Human Biology 17, pp. 459–474.
   PubMed Web of Science ®Google Scholar
• Gervini, D. and Gasser, T. (2004). “Self-Modelling Warping Functions”. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66 (4), pp. 959–971.
   Web of Science ®Google Scholar
• Gervini, D. (2014). “Dynamic Retrospective Regression for Functional Data”. Technometrics DOI: 10.1080/00401706. 2013.879076.
   Google Scholar
• Goldenthal, R. and Bercovier, M. (2004). “Spline Curve Approximation and Design by Optimal Control Over the Knots”. Computing 72 (1–2), pp. 53–64.
   Web of Science ®Google Scholar
• Grasso, M.; Goletti, M.; Annoni, M.; and Colosimo, B. M. (2013). “A New Approach for Online Health Assessment of Abrasive Waterjet Cutting Systems”. International Journal of Abrasive Technology 6 (2), pp. 158–181.
   Google Scholar
• Grasso, M.; Pennacchi, P.; and Colosimo, B. M. (2014a). “Empirical Mode Decomposition of Pressure Signal for Health Condition Monitoring in Waterjet Cutting”. The International Journal of Advanced Manufacturing Technology 72 (1–4), pp. 347–364.
   Web of Science ®Google Scholar
• Grasso, M.; Colosimo, B. M.; and Pacella, M. (2014b). “Profile Monitoring via Sensor Fusion: The Use of PCA Methods for Multi-Channel Data”. International Journal of Production Research 52 (20), pp. 6110–6135.
   Web of Science ®Google Scholar
• Guo, Y.; Gu, Y.; and Zhang, Y. (2011). “Invariant Feature Point Based ICP with the RANSAC for 3D Registration”. Information Technology Journal 10, pp. 276–284.
   Google Scholar
• Horváth, L. and Kokoszka, P. (2012). Inference for Functional Data with Applications, vol. 200. London: Springer.
   Google Scholar
• James, G. M. (2007). “Curve Alignment by Moments”. The Annals of Applied Statistics 1, pp. 480–501.
   Web of Science ®Google Scholar
• Jin J. and Shi, J. (1999). “Feature-Preserving Data Compression of Stamping Tonnage Information Using Wavelets”. Technometrics 41 (4), 327–339.
   Web of Science ®Google Scholar
• Jin, J. and Shi, J. (2001). “Automatic Feature Extraction of Waveform Signals for In-Process Diagnostic Performance Improvement”. Journal of Intelligent Manufacturing 12, pp. 257–268.
   Web of Science ®Google Scholar
• Jolliffe I. T. (2002). Principal Component Analysis, 2nd edition, in Springer Series in Statistics. New York: Springer.
   Google Scholar
• Kang, L. and Albin, S. L. (2000). “On-Line Monitoring When the Process Yields a Linear Profile”. Journal of Quality Technology 32 (4), pp. 418–426.
   Web of Science ®Google Scholar
• Kaziska, D. and Srivastava, A. (2007). “Gait-Based Human Recognition by Classification of Cyclostationary Processes on Nonlinear Shape Manifolds”. Journal of the American Statistical Association 102, 1114–1128.
   Web of Science ®Google Scholar
• Ke, C. and Wang, Y. (2001). “Semiparametric Nonlinear Mixed-Effects Models and Their Applications (with Comments and a Rejoinder by the Authors)”. Journal of the American Statistical Association 96, pp. 1272–1298
   Web of Science ®Google Scholar
• Kim, J.; Huang, Q.; Shi, J.; and Chang, T. S. (2006). “Online Multichannel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve”. Journal of Manufacturing Science and Engineering 128 (4), pp. 944–950.
   Web of Science ®Google Scholar
• Kneip, A. and Gasser, T. (1992). “Statistical Tools to Analyze Data Representing a Sample of Curves”. Annals of Statistics 20, pp. 1266–1305.
   Web of Science ®Google Scholar
• Kovacevic, R.; Hashish, M.; Mohan, R.; Ramulu, M.; Kim, T. J.; and Geskin, E. S. (1997). “State of the Art of Research and Development in Abrasive Waterjet Machining”. Journal of Manufacturing Science and Engineering 119 (4), 776–785.
   Web of Science ®Google Scholar
• Lindstrom, M. J. and Bates, D. M. (1990). “Nonlinear Mixed Effects Models for Repeated Measures Data”. Biometrics 46, pp. 673–687.
   PubMed Web of Science ®Google Scholar
• Molinari, N.; Durand, J. F.; and Sabatier, R. (2004). “Bounded Optimal Knots for Regression Splines”. Computational Statistics & Data Analysis 45 (2), pp. 159–178.
   Web of Science ®Google Scholar
• Montgomery, D. C. (2008). Introduction to Statistical Quality Control, 6th edition. New York: John Wiley & Sons.
   Google Scholar
• Mosesova, S. A.; Chipman, H. A.; MacKay, R. J.; and Steiner, S. H. (2007). “Profile Monitoring Using Mixed-Effects Models”. BISRG Report RR-06-06, http://www.bisrg.uwaterloo.ca.
   Google Scholar
• Noorossana, R.; Saghaei, A.; and Amiri, A. (2012). Statistical Analysis of Profile Monitoring. New York: John Wiley & Sons.
   Google Scholar
• Okello, N. and Ristic, B. (2003). “Maximum Likelihood Registration for Multiple Dissimilar Sensors”. IEEE Transactions on Aerospace and Electronic Systems 39 (3), pp. 1074–1083.
   Web of Science ®Google Scholar
• Paynabar, K.; Jin, J.; and Pacella, M. (2013). “Analysis of Multichannel Nonlinear Profiles Using Uncorrelated Multilinear Principal Component Analysis with Applications in Fault Detection and Diagnosis”. IEE Transactions 45 (11), pp. 1235–1247.
   Web of Science ®Google Scholar
• Ramsay, J. O.; Wickham, H.; Graves, S.; and Hooker, G. (2012). fda: Functional Data Analysis. R package version 2.4.0, http://CRAN.R-project.org/package=fda.
   Google Scholar
• Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis, 2nd edition. New York, NY: Springer.
   Google Scholar
• Ramsay, J. O. and Li, X. (1998). “Curve Registration”. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60 (2), pp. 351–363.
   Web of Science ®Google Scholar
• Sangalli, L. M.; Secchi, P.; Vantini, S.; and Veneziani, A. (2009a). “A Case Study in Exploratory Functional Data Analysis: Geometrical Features of the Internal Carotid Artery”. Journal of the American Statistical Association 104 (485), 37–48.
   Web of Science ®Google Scholar
• Sangalli, L. M.; Secchi, P.; Vantini, S.; and Veneziani, A. (2009b). “Efficient Estimation of Three-Dimensional Curves and Their Derivatives by Free-Knot Regression Splines, Applied to the Analysis of Inner Carotid Artery Centrelines”. Journal of the Royal Statistical Society, Series C (Applied Statistics) 58 (3), pp. 285–306.
   Web of Science ®Google Scholar
• Sangalli, L. M.; Secchi, P.; Vantini, S.; and Vitelli, V. (2010). “K-Mean Alignment for Curve Clustering”. Computational Statistics & Data Analysis 54 (5), pp. 1219–1233.
   Web of Science ®Google Scholar
• Sangalli, L. M.; Secchi, P.; and Vantini, S. (2013). “Analysis of AneuRisk65 Data: K-Mean Alignment”. MOX-Report No. 45/2013, http://mox.polimi.it/it/progetti/pubblicazioni/quaderni/45–2013.pdf.
   Google Scholar
• Senin, N.; Colosimo, B. M.; and Pacella, M. (2013). “Point Set Augmentation Through Fitting For Enhanced ICP Registration of Point Clouds in Multisensor Coordinate Metrology”. Robotics and Computer-Integrated Manufacturing 29 (1), pp. 39–52.
   Web of Science ®Google Scholar
• Silverman, B. W. (1995). “Incorporating Parametric Effects into Functional Principal Components Analysis”. Journal of the Royal Statistical Society, Series B 57, pp. 673–689.
   Google Scholar
• Srivastava, A.; Wu, W.; Kurtek, S.; Klassen, E.; and Marron, J. S. (2011). “Registration of Functional Data Using Fisher–Rao Metric”. arXiv preprint arXiv:1103.3817.
   Google Scholar
• Tang, R. and Müller, H. G. (2008). “Pairwise Curve Synchronization for Functional Data”. Biometrika 95 (4), pp. 875–889.
   Web of Science ®Google Scholar
• Tucker, J. D.; Wu, W.; and Srivastava, A. (2013). “Generative Models for Function Data Using Phase and Amplitude Separation”. Computational Statistics and Data Analysis 61, pp. 50–66.
   Web of Science ®Google Scholar
• Vantini, S. (2012). “On the Definition of Phase and Amplitude Variability in Functional Data Analysis”. TEST 21 (4), pp. 676–696.
   Web of Science ®Google Scholar
• Varmuza, K. and Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemometrics. Boca Raton, FL: CRC Press.
   Google Scholar
• Woodall, W. H.; Spitzner, D. J.; Montgomery, D. C.; and Gupta, S. (2004). “Using Control Charts to Monitor Process and Product Quality Profiles”. Journal of Quality Technology 36 (3), pp. 309–320.
   Web of Science ®Google Scholar
• Zhou, S.; Jin, N.; and Jin, J. (2005). “Cycle-Based Signal Monitoring Using a Directionally Variant Multivariate Control Chart System”. IEE Transactions 37, pp. 971–982.
   Web of Science ®Google Scholar
• Zhou, S. and Shen, X. (2001). “Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes”. Journal of the American Statistical Association 96 (453), pp. 247–259.
   Web of Science ®Google Scholar
• Zhou, R. R.; Serban, N.; Gebraeel, N., and Muller, H.-G. (2014). “A Functional Time Warping Approach to Modeling and Monitoring Truncated Degradation Signals”. Technometrics 56 (1), pp. 67–77.
   Web of Science ®Google Scholar