References

Abbott, T.S. and Person, L.H. (1991) Method and system for monitoring and displaying engine performance parameters. United States Patent, patent number: 5050081.

Akaike, H. (1974) A new look at statistical model identification. IEEE Transactions on Automatic Control, 19(6):716–723.

Akbari, H., Levinson, R., and Rainer, L. (2005) Monitoring the energy-use effects of cool roofs on California commercial buildings. Energy and Buildings, 37(10):1007–1016.

Aldrich, J. (1997) R.A. Fisher and the making of maximum likelihood 1912–1922. Statistical Science, 12(3):162–176.

Al-Ghazzawi, A. and Lennox, B. (2008) Monitoring a complex refining process using multivariate statistics. Control Engineering Practice, 16(3):294–307.

Allen, D.M. (1974) The relationship between variable selection and data augmentation and a method for prediction. Technometrics, 16(1):125–127.

Anderson, T.W. (1951) Estimating linear restrictions on regression coefficients for multivariate normal distributions. The Annals of Mathematical Statistics, 22(3):327–351.

Anderson, T.W. (1963) Asymptotic theory for principal component analysis. The Annals of Mathematical Statistics, 34(1):122–148.

Anderson, T.W. (2003) An Introduction into Multivariate Statistical Analysis. John Wiley & Sons, New York, USA, 3rd. edition.

Aparisi, F. (1997–1998) Sampling plans for the multivariate T2 control chart. Quality Engineering, 10(1):141–147.

Aravena, J.L. (1990) Recursive moving window DFT algorithm. IEEE Transactions on Computers, 39(1):145–148.

Arteaga, F. and Ferrer, A. (2002) Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10):408–418.

Bardou, O. and Sidahmed, M. (1994) Early detection of leakages in the exhaust and discharge systems of reciprocating machines by vibration analysis. Mechanical Systems & Signal Processing, 8(5):551–570.

Bartelmus, W. and Zimroz, R. (2009) Vibration condition monitoring of planetary gearbox under varying external load. Mechanical Systems & Signal Processing, 23(1):246–257.

Basseville, M. (1988) Detecting changes in signals and systems—a survey. Automatica, 24(3):309–326.

Baydar, N. and Ball, A.D. (2001) A comparative study of acoustics and vibration signals in detection of gear failures using Wigner-Ville distribution. Mechanical Systems & Signal Processing, 15(6):1091–1107.

Baydar, N., Ball, A.D., and Kruger, U. (1999) Detection of incipient tooth defect in helical gears using principal components. In 1st International Conference on the Integrating of Dynamics, Monitoring and Control, pp.93–100, Manchester, UK.

Baydar, N., Chen, Q., Ball, A.D., and Kruger, U. (2001) Detection of incipient tooth defect in helical gears using multivariate statistics. Mechanical Systems & Signal Processing, 15(2):303–321.

Bellman, R. (1957) Dynamic Programming. Princeton University Press, Priceton, NJ, USA.

Billingsley, P. (1995) Probability and Measures. John Wiley & Sons, New York, 3rd edition.

Bissessur, Y., Martin, E.B., and Morris, A.J. (1999) Monitoring the performance of the paper making process. Control Engineering Practice, 7(11):1357–1368.

Bissessur, Y., Martin, E.B., Morris, A.J., and Kitson, P. (2000) Fault detection in hot steel rolling using neural networks and multivariate statistics. IEE Proceedings, Part D—on Control Theory and Applications, 147(6):633–640.

Björck, Å (1996) Numerical Methods for Least Squares Problems. SIAM Publishing, Philadelphia, PA, USA.

Boller, C. (2000) Next generation structural health monitoring and its integration into aircraft design. International Journal of Systems Science, 31(11):1333–1349.

Box, G.E.P. (1954) Some theorems on quadratic forms applied in teh study of analysis of variance problems: Effect of inequality of variance in one-way classification. Annals of Mathematical Statistics, 25(2):290–302.

Box, G.E.P., Hunter, W.G., MacGregor, J.F., and Erjavec, J. (1973) Some problems associated with the analysis of multiresponse data. Technometrics, 15(1):33–51.

Bradley, R.C. (2007) Introduction to Strong Mixing Conditions, Volume 1–3. Kendrick Press, Heber City, Utah, USA.

Brussee, W. (2004) Statistics for Six Sigma Made Easy. McGraw-Hill, New York, USA.

Buckley, P.S., Luyben, W.L., and Shunta, J.P. (1985) Design of distillation column control systems. Edward Arnold, London.

Bunch, J.R., Nielsen, C.P., and Sorensen, D.C. (1978) Rank-one modifcation of the symmetric eigenproblem. Numerische Mathematik, 31:31–48.

Burr, J.T. (2005) Elementary Statistical Quality Control. Marcel Dekker, New York, USA.

Byrd, R.H., Lu, P., Nocedal, J., and Zhu, C. (1995) A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5):1190–1208.

Cattell, R.B. (1966) The scree test for the number of factors. Multivariate Behavioral Research, 1(2):245–276.

Cattell, R.B. and Vogelmann, S. (1977) A comprehensive trial of the scree and KG criteria for determining the number of factors. Multivariate Behavioral Research, 12(3):289–325.

Champagne, B. (1994) Adaptive eigendecomposition of data covariance matrices based on first order perturbations. IEEE Transactions on Signal Processing, 42(10):2758–2770.

Chatterjee, C., Kang, Z., and Roychowdhury, V.P. (2000) Algorithms for accelerated convergence of adaptive pca. IEEE Transactions on Neural Networks, 11(2):338–355.

Chen, J. and Liu, K.C. (2004) On-line batch process monitoring using dynamic pca and dynamic PLS models. Chemical Engineering Science, 57(1):63–75.

Chen, Q. and Kruger, U. (2006) Analysis of extended partial least squares for monitoring large-scale processes. IEEE Transactions on Control Systems Technology, 15(5):807–813.

Chen, Y.D., Du, R., and Qu, L.S. (1995) Fault features of large rotating machinery and diagnosis using sensor fusion. Journal of Sound & Vibration, 188(2):227–242.

Chiang, L.H., Russel, E.L., and Braatz, R.D. (2001) Fault Detection and Diagnosis in Industrial Systems. Springer-Verlag, London.

Coello, C.A.C., Pulido, G.T., and Lechuga, M.S. (2004) Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3):256–279.

Cramér, H. (1946) Mathematical Methods in Statistics. Princeton University Press, Princeton, NJ, USA.

Crosby, P.B. (1979) Quality is Free. McGraw Hill, New York.

Cullum, J.K. and Willoughby, R.A. (2002) Lanczos Algorithms for Large Symmetric Eigenvalue Computations: Theory. SIAM Publishing, Philadelphia, PA.

Daszykowski, M. (2007) From projection pursuit to other unsupervised chemometric techniques. Journal of Chemometrics, 21(7-9):270–279.

Davies, P.L. (1992) The asymptitics of Rousseeuw's minimum volume ellipsoid estimator. The Annals of Statistics, 20(4):1828–1843.

Dayal, B.S. and MacGregor, J.F. (1996) Identification of finite impulse response models: Methods and robustness issues. Industrial & Engineering Chemistry Research, 35(11):4078–4090.

aDayal, B.S. and MacGregor, J.F. (1997a) Improved PLS algorithms. Journal of Chemometrics, 11(1):73–85.

bDayal, B.S. and MacGregor, J.F. (1997b) Multi-output process identification. Journal of Process Control, 7(4):269–282.

cDayal, B.S. and MacGregor, J.F. (1997c) Recursive exponentially weighted PLS and its applications to adaptive control and prediction. Journal of Process Control, 7(3):169–179.

de Jong, S. (1993) Simpls, an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18:251–263.

de Jong, S., Wise, B.M., and Ricker, N.L. (2001) Canonical partial least squares and continuum power regression. Journal of Chemometrics, 15(2):85–100.

Deming, W.E. and Birge, R.T. (1934) On the statistical theory of errors. Review of Modern Physics, 6(3):119–161.

Ding, S.X. (2008) Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. Springer, Berlin.

Dionisio, R.M.A. and Mendes, D.A. (2006) Entropy-based independence test. Nonlinear Dynamics, 44:351–357.

Doebling, S.W., Farrar, C.R., Prime, M.B., and Shevitz, D.W. (1996) Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review. Technical report, Los Alamos National Laboratory, USA.

Donoho, D.L. (1982) Breakdown properties of multivariate location estimators. Qualifying Paper. Harvard University, Boston, MA, USA.

Doukopoulos, X.G. and Moustakides, G.V. (2008) Fast and stable subspace tracking. IEEE Transactions on Signal Processing, 56(4):1452–1465.

Duchesne, C., Kourti, T., and MacGregor, J.F. (2002) Multivariate SPC for startups and grade transitions. AIChE Journal, 48(12):2890–2901.

Duchesne, C. and MacGregor, J.F. (2001) Jackknife and bootstrap methods in the identification of dynamic models. Journal of Process Control, 11(5):553–564.

Duda, R.O. and Hart, P.E. (1973) Pattern classification and scene analysis. John Wiley & Sons, New York.

Dunia, R. and Qin, S.J. (1998) A unified geometric approach to process and sensor fault identification and reconstruction: the unidimensional fault case. Computers & Chemical Engineering, 22(7-8):927–943.

Dunia, R., Qin, S.J., Edgar, T.F., and McAvoy, T.J. (1996) Identification of faulty sensors using principal component analysis. AIChE Journal, 42(10):2797–2812.

Dunteman, G.H. (1989) Principal Components Analysis. Quantitative Applications in the Social Sciences. Sage, London.

Durrett, R. (1996) Probability: Theory and Examples. Duxbury Press, Wadsworth Publishing Company, Belmont, CA, USA, 2nd edition.

Eastment, H.T. and Krzanowski, W.J. (1982) Cross-validatory choice of the number of components from a principal component analysis. Technometrics, 24(1):73–77.

Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York.

Farmer, S.A. (1971) An investigation into the results of principal component analysis of data derived from random numbers. The Statistician, 20(4):63–72.

Feigenbaum, D. (1951) Quality Control: Principles, Practice, and Administration. McGraw-Hill, New York.

Feital, T., Kruger, U., Xie, L., Schubert, U., Lima, E.L., and Pinto, J.C. (2010) A unified statistical framework for monitoring multivariate systems with unknown source and error signals. Chemometrics & Intelligent Laboratory Systems, 104(2):223–232.

Feller, W. (1936) Über den zentralen grenzwertsatz der wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, 40(1):521–559.

Feller, W. (1937) Über den zentralen grenzwertsatz der wahrscheinlichkeitsrechnung. ii. Mathematische Zeitschrift, 42(1):301–312.

Fisher, H. (2011) A History of the Central Limit Theorem: From Classical to Modern Probability Theory. Springer Verlag, New York.

Fortier, J.J. (1966) Simultaneous linear prediction. Psychometrika, 31(3):369–381.

Fortuna, L., Graziani, S.G. Xibilia, M., and Barbalace, N. (2005) Fuzzy activated neural models for product quality monitoring in refineries. In Zítek, P., editor, Proceedings of the 16th IFAC World Congress, volume 16. Elsevier.

Frank, P.M., Ding, S.X., and Marcu, T. (2000) Model-based fault diagnosis in technical processes. Transactions of the Institute of Measurement and Control, 22(1):57–101.

Fuchs, E. and Donner, K. (1997) Fast least-squares polynomial approximation in moving time windows. In Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP, pages 1965–1968, Munich, Germany.

Fugate, M.L., Sohn, H., and Farrar, C.R. (2001) Vibration-based damage detection using statistical process control. Mechanical System & Signal Processing, 15(4):707–721.

Fuller, W.A. (1987) Measurement Error Models. John Wiley & Sons, New York, USA.

Gallagher, N.B., Wise, B.M., Butler, S.W., White, D.D., and Barna, G.G. (1997) Development of benchmarking of multivariate statistical process control tools for a semiconductor etch process: Improving robustness through model updating. In Proceedings of ADCHEM 97, pages 78–83, Banff, Canada. International Federation of Automatic Control.

Ge, Z., Kruger, U., Lamont, L., Xie, L., and Song, Z. (2010) Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches. Mechanical Systems & Signal Processing, 24(8):2972–2984.

Ge, Z., Xie, L., Kruger, U., and Song, Z. (2011) Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions. AIChE Journal, in print.

Geisser, S. (1974) A predictive approach to the random effect model. Biometrika, 61(1):101–107.

Geladi, P. and Kowalski, B.R. (1986) Partial least squares regression: A tutorial. Analytica Chimica Acta, 185:231–246.

Gérard, Y., Holdsworth, R.J., and Martin, P.A. (2007) Multispecies in situ monitoring of a static internal combustion engine by near-infrared diode laser sensors. Applied Optics, 46(19):3937–3945.

Gnanadesikan, R. and Kettenring, J.R. (1972) Robust estimates, residuals, and outliers detection with multiresponse data. Biometrics, 28:81–124.

Golub, G.H. (1973) Some modifed matrix eigenvalue problems. SIAM Review, 15(2):318–334.

Golub, G.H. and van Loan, C.F. (1996) Matrix Computation. John Hopkins, Baltimore, USA 3 edition.

Gosselin, C. and Ruel, M. (2007) Advantages of monitoring the performance of industrial processes. In ISA EXPO 2007, Reliant Center, Houston, TX, USA.

Granger, C.W., Maasoumi, E., and Racine, J. (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis, 25(5):649–669.

Graybill, F. (1958) Determining sample size for a specified width confidence interval. The Annals of Mathematical Statistics, 29(1):282–287.

Graybill, F.A. and Connell, T.L. (1964) Sample size required for estimating the variance within d units of the true value. The Annals of Mathematical Statistics, 35(1):438–440.

Graybill, F.A. and Morrison, R.D. (1960) Sample size for a specified width confidence interval on the variance of a normal distribution. Biometrics, 16(4):636–641.

Greenwood, J.A. and Sandomire, M.M. (1950) Sample size required for estimating the standard deviation as a percent of its true value. Journal of the American Statistical Association, 45(250):257–260.

Gupta, P.L. and Gupta, R.D. (1987) Sample size determination in estimating a covariance matrix. Computational Statistics & Data Analysis, 5(3):185–192.

Gut, A. (2005) Probability: A Graduate Course. Springer Verlag, New York.

Hall, P., Marshall, D., and Martin, R. (1998) Incrementally computing eigenspace models. In Proceeding of British Machine Vision Conference, pages 286–295, Southampton.

Hall, P., Marshall, D., and Martin, R. (2000) Merging and splitting eigenspace models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(9):1042–1049.

Hall, P., Marshall, D., and Martin, R. (2002) Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image and Vision Computing, 20(13-14):1009–1016.

Hampel, F.R. (1974) The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69:383–393.

Hawkins, D.M. (1974) The detection of errors in multivariate data using principal components. Journal of the American Statistical Association, 69(346):340–344.

Hawkins, D.M. (1993) Cumulative sum control charting: An underutilized SPC tool. Quality Engineering, 5(3):463–477.

Hawkins, D.M. and Olwell, D.H. (1998) Cumulative Sum Charts and Charting for Quality Improvements. Springer Verlag, New York, NY, USA.

He, Q.B., Kong, F.R., and Yan, R.Q. (2007) Subspace-based gearbox condition monitoring by kernel principal component analysis. Mechanical Systems & Signal Processing, 21(4):1755–1772.

He, Q.B., Yan, R.Q., Kong, F.R., and Du, R.X. (2009) Machine condition monitoring using principal component representations. Mechanical Systems & Signal Processing, 23(4):446–466.

Helland, K., Berntsen, H., Borgen, O., and Martens, H. (1991) Recursive algorithm for partial least squares regression. Chemometrics & Intelligent Laboratory Systems, 14(1-3):129–137.

Henderson, C.R. (1975) Unbiased estimation and prediction under a selection model. Biometrics, 31(2):423–444.

Horn, J.L. (1965) A rationale and test for the number of factors in factor analysis. Psychometrica, 30(2):73–77.

Höskuldsson, A. (1988) PLS regression models. Journal of Chemometrics, 2(3):211–228.

Höskuldsson, A. (1994) The H-principle: new ideas, algorithms and methods in applied mathematics and statistics. Chemometrics & Intellingent Laboratory Systems, 23(1):1–28.

Höskuldsson, A. (1995) A combined theory for PCA and PLS. Journal of Chemometrics, 9(2):91–123.

Höskuldsson, A. (1996) Prediction Methods in Schience and Technology. Thor Publishing, Copenhagen, Denmark.

Höskuldsson, A. (2008) H-methods in applied sciences. Journal of Chemometrics, 22(3-4):150–177.

Hotelling, H. (1935) The most predictable criterion. Journal of Educational Psychology, 26(2):139–142.

Hotelling, H. (1936) Relations between two sets of variates. Biometrica, 28(3-4):321–377.

Hotelling, H. (1947) Multivariate quality control, illustrated by the air testing of sample bombsights. In Eisenhart, C., Hastay, M.W., and Wallis, W.A., editors, Selected Techniques of Statistical Analysis, pages 111–184. McGraw-Hill, New York.

Howlett, R.J., de Zoysa, M.M., Walters, S.D., and Howson, P.A. (1999) Neural network techniques for monitoring and control of internal combustion engines. In Proceedings of the International Symposium on Intelligent Industrial Automation, Genova, Italy.

Hu, N., Chen, M., and Wen, X. (2003) The application of stochastic resonance theory for early detecting rub-impact fault of rotor system. Mechanical Systems and Signal Processing, 17(4):883–895.

Hu, Q., He, Z., Zhang, Z., and Zi, Y. (2007) Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMS ensemble. Mechanical Systems & Signal Processing, 21(2):688–705.

Hunter, J.S. (1986) The exponentially weighted moving average. Journal of Quality Technology, 18(4):203–210.

Hyvarinen, A. (1999) Gaussian moments for noisy independent component analysis. IEEE Signal Processing Letters, 6:145–147.

Hyvärinen, A., Karhunen, J., and Oja, E. (2001) Independent Component Analysis. John Wiley & Sons, New York.

Iserman, R. (1993) Fault diagnosis of machines via parameter estimation and knowledge processing: Tutorial paper: Fault detection, supervision and safety for technical processes. Automatica, 29(4):815–835.

Isermann, R. (2006) Fault-Diagnosis Systems—An Introduction from Fault Detection to Fault Tolerance, volume XVIII of Robotics. Springer Verlag GmbH.

Isermann, R. and Ballé, P. (1997) Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5):709–719.

Ishikawa, K. (1985) What is Total Quality Control? Prentice Hall, Englewood Cliffs, NJ, USA.

Isserlis, L. (1918) On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables. Biometrika, 12(1/2):134–139.

Jackson, J.E. (1959) Quality control methods for several related variables. Technometrics, 1(4):359–377.

Jackson, J.E. (1980) Principal components and factor analysis: Part I: Principal components. Journal of Quality Control, 12(4):201–213.

Jackson, J.E. (2003) A Users Guide to Principal Components. Wiley Series in Probability and Mathematical Statistics. John Wiley, New York.

Jackson, J.E. and Morris, R.H. (1956) Quality control methods for two related variables. Industrial Quality Control, 12(7):2–6.

Jackson, J.E. and Morris, R.H. (1957) An application of multivariate quality control to photographic processing. Journal of the American Statistical Association, 52(278):186–199.

Jackson, J.E. and Mudholkar, G.S. (1979) Control procedures for residuals associated with principal component analysis. Technometrics, 21:341–349.

Jaw, L.C. (2005) Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step. In Proceedings of the ASME Turbo Expo 2005: Power for Land, Sea, and Air, pages 683–695, Reno, USA.

Jaw, L.C. and Mattingly, J.D. (2008) Aircraft Engine Controls: Design, System Analysis, And Health Monitoring. American Institute of Aeronautics and Astronautics Education Series.

Jolliffe, I.T. (1972) Discarding variables in principal component analysis. I: Artificial data. Applied Statistics, 21(2):160–173.

Jolliffe, I.T. (1973) Discarding variables in a principal component analysis. II: Real data. Applied Statistics, 22(1):21–31.

Jolliffe, I.T. (1986) Principal Component Analysis. Springer, New York.

Juran, J.M. and Godfrey, A.B. (2000) Juran's Quality Handbook. McGraw Hill, New York, 5th edition.

Kaiser, H.F. (1960) The application of electronic computers to factor analysis. Educational & Psychological Measurement, 20(1):141–151.

Kaspar, M.H. and Ray, W.H. (1992) Chemometric method for process monitoring. AIChE Journal, 38(10):1593–1608.

Kaspar, M.H. and Ray, W.H. (1993) Partial least squares modelling as successive singular value decompositions. Computers & Chemical Engineering, 17(10):985–989.

Kenney, J., Linda, Y., and Leon, J. (2002) Statistical process control integration systems and methods for monitoring manufacturing processes. United States Patent, patent number 6445969.

Kiencke, U. and Nielsen, L. (2000) Automotive Control Systems. Springer-Verlag, Berlin, Heidelberg.

Kim, K. and Parlos, A.G. (2003) Reducing the impact of false alarms in induction motor fault diagnosis. Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, 125(1):80–95.

Knutson, J.W. (1988) Techniques for determining the state of control of a multivariate process. In Proceedings of the Seventh Annual Control Engineering Conference, pages 1–14, Rosemount, IL, USA. Tower Conference Management.

Ko, J.M. and Ni, Y.Q. (2005) Technology developments in structural health monitoring of large-scale bridges. Engineering Structures, 27(12):1715–1725.

Kosanovich, K.A. and Piovoso, M.J. (1991) Process data analysis using multivariate statistical methods. In Proceedings of the American Control Conference, Chicago, IL, USA.

Kourti, T. (2005) Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4):213–246.

Kourti, T. and MacGregor, J.F. (1995) Process analysis, monitoring and diagnosis using multivariate projection methods. Chemometrics & Intelligent Laboratory Systems., 28:3–21.

Kourti, T. and MacGregor, J.F. (1996) Multivariate SPC methods for process and product management. Journal of Quality Technology, 28:409–428.

Kramer, M.A. and Palowitch, B.L. (1987) A rule-based approach to fault diagnosis using the signed directed graph. AIChE Journal, 33:1067–1078.

Kresta, J.V., MacGregor, J.F., and Marlin, T.E. (1989) Multivariate statistical monitoring of process performance. In AIChE Annual Meeting, San Francisco, CA, USA.

Kresta, J.V., MacGregor, J.F., and Marlin, T.E. (1991) Multivariate statistical monitoring of process operating performance. The Canadian Journal of Chemical Engineering, 69:35–47.

Kruger, U., Chen, Q., Sandoz, D.J., and McFarlane, R.C. (2001) Extended PLS approach for enhanced condition monitoring of industrial processes. AIChE Journal, 47(9):2076–2091.

Kruger, U. and Dimitriadis, G. (2008) Diagnosis of process faults in chemical systems using a local partial least squares approach. AIChE Journal, 54(10):2581–2596.

Kruger, U., Kumar, S., and Littler, T. (2007) Improved principal component modelling using the local approach. Automatica, 43(9):1532–1542.

aKruger, U., Zhou, Y., Wang, X., Rooney, D., and Thompson, J. (2008a) Robust partial least squares regression—part III, outlier analysis and application studies. Journal of Chemometrics, 22(5):323–334.

bKruger, U., Zhou, Y., Wang, X., Rooney, D., and Thompson, J. (2008b) Robust partial least squares regression: Part I, algorithmic developments. Journal of Chemometrics, 22(1):1–13.

cKruger, U., Zhou, Y., Wang, X., Rooney, D., and Thompson, J. (2008c) Robust partial least squares regression: Part II, new algorithm and benchmark studies. Journal of Chemometrics, 22(1):14–22.

Kumar, S., Martin, E.B., and Morris, A.J. (2002) Detection of process model changes in pca based performance monitoring. In Proceedings of the American Control Confernece, pages 2719–2724, Anchorage, AK.

Kwon, O.K., Kong, H.S., Kim, C.H., and Oh, P.K. (1987) Condition monitoring techniques for an internal combustion engine. Tribology International, 20(3):153–159.

Lane, S., Martin, E.B., Morris, A.J., and Glower, P. (2003) Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Transactions of the Institute of Measurement and Control, 25(1):17–35.

Lee, D.S. and Vanrolleghem, P.A. (2003) Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis. Biotechnology and Bioengineering, 82(4):489–497.

Lehane, M., Dube, F., Halasz, M., Orchard, R., Wylie, R., and Zaluski, M. (1998) Integrated diagnosis system (IDS) for aircraft fleet maintenance. In Mostow, J. and Rich, C., editors, Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI '98), Menlo Park, CA, USA. AAAI Press.

Lennox, B., Montague, G.A., Hiden, H.G., Kornfeld, G., and Goulding, P.R. (2001) Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering, 74(2):125–135.

Leone, F.C., Rutenberg, Y.H., and Topp, C.W. (1950) The use of sample quasi-ranges in setting confidence intervals for the population standard deviation. Journal of the American Statistical Association, 56(294):260–272.

Li, W., Yue, H., Valle-Vervantes, S., and Qin, S.J. (2000) Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5):471–486.

Liang, Y.Z. and Kvalheim, O.M. (1996) Robust methods for multivaraite analysis—a tutorial review. Chemometrics & Intelligent Laboratory Systems, 32(1):1–10.

Liapounoff, A.M. (1900) Sur une proposition de la théorie des probabilités. Bulletin de l'Academie Impériale des Sciences de St. Pétersbourg, 5(13):359–386.

Liapounoff, A.M. (1901) Nouvelle forme du théoréme sur la limite de probabilités, Mémoires de l'Academie Impériale des Sciences de St. Pétersbourg. Classe Physicomathématique, 8(12):1–24.

Lieftucht, D., Kruger, U., and Irwin, G.W. (2004) Improved diagnosis of sensor faults using multivariate statistics. In Proceedings of the American Control Conference, pages 4403–4407, Boston.

aLieftucht, D., Kruger, U., and Irwin, G.W. (2006a) Improved reliability in diagnosing faults using multivariate statistics. Computers & Chemical Engineering, 30(5):901–912.

bLieftucht, D., Kruger, U., Irwin, G.W., and Treasure, R.J. (2006b) Fault reconstruction in linear dynamic systems using multivariate statistics. IEE Proceedings, Part D—On Control Theory and Applications, 153(4):437–446.

Lieftucht, D., Völker, M., Sonntag, C., et al. (2009) Improved fault diagnosis in multivariate systems using regression-based reconstruction. Control Engineering Practice, 17(4):478–493.

Lindberg, W., Persson, J.-A., and Wold, S. (1983) Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and ligninsulfonate. Analytical Chemistry, 55(4):643–648.

Lindgren, F., Geladi, P., and Wold, S. (1993) The kernal algorithm for PLS. Journal of Chemometrics, 7(1):45–59.

Ljung, L. (1999) System Identification: Theory for the User. Prentice Hall, Upper Saddle River, NJ, USA, 2nd edition.

Lohmoeller, J.B. (1989) Latent Variable Path Modelling with Partial Least Squares. Physica-Verlag, Heidelberg, Germany.

Lucas, J.M. and Saccucci, M.S. (1990) Expnentially weighted moving average schemes: Properties and enhancements. Technometrics, 32(1):1–12.

MacGregor, J.F. (1997) Using on-line process data to improve quality: challenges for statisticians. International Statistical Review, 65(3):309–323.

MacGregor, J.F., Jaeckle, C., Kiparissides, C., and Koutoudi, M. (1994) Process monitoring andd iagnosis by multiblock plsmethods. AIChE Journal, 40(5):826–838.

MacGregor, J.F. and Kourti, T. (1995) Statistical process control of multivariate processes. Control Engineering Practice, 3(3):403–414.

MacGregor, J.F., Marlin, T.E., Kresta, J.V., and Skagerberg, B. (1991) Multivariate statistical methods in process analysis and control. In AIChE Symposium Proceedings of the 4th International Conference on Chemical Process Control, pages 79–99, New York. AIChE Publication, No. P-67.

Malhi, A. and Gao, R. (2004) PCA-based feature selection scheme for machine defect classification. IEEE Transaction on Instrumentation and Measurement, 53(6):1517–1525.

Malinowski, E.R. (1977) Theory of error in factor analysis. Analytical Chemistry, 49(4):606–612.

Marcon, M., Dixon, T.W., and Paul, A. (2005) Multivariate SPC applications in the calcining business. In 134th Annual Meeting & Exhibition (TMS 2005), Moscone West Convention Center, San Francisco, CA, USA 13–17 February.

Mardia, K.V., Kent, J.T., and Bibby, J.M. (1979) Multivariate Analysis. Probability and Mathematical Statistics. Academic Press, London.

Maronna, R.A. (1976) Robust M-estimator of multivariate location and scatter. The Annals of Statistics, 4(1):51–67.

Martin, E.B., Morris, A.J., and Lane, S. (2002) Monitoring process manufacturing performance. IEEE Control Systems Magazine, 22(5):26–39.

Mason, R.L. and Young, J.C. (2001) Multivariate Statistical Process Control with Industrial Applications. ASA-SIAM series on statistical and applied probability. ASA-SIAM, Philadelphia, PA, USA.

Mastronardi, N., van Camp, E., and van Barel, M. (2005) Divide and conquer algorithms for computing the eigendecomposition of symmetric diagonal-plus-semiseparable matrices. Numerical Algorithms, 39(4):379–398.

McDowell, N., McCullough, G., Wang, X., Kruger, U., and W., I.G. (2008) Fault diagnostics for internal combustion engines—current and future techniques. In Proceedings of the 8th International Conference on Engines for Automobiles, Capri (Naples), Italy.

McFarlane, R.C., Reineman, R.C., Bartee, J.F., and Georgakis, C. (1993) Dynamic simulator of a model IV Fluid catalytic cracking unit. Computers and Chemical Engineering, 17(3):275–300.

Meronk, M. (2001) The application of model based predictive control and multivariate statistical process control in industry. Master's thesis, The University of Manchester, Manchester, UK.

Miletic, I., Quinn, S., Dudzic, M., Vaculik, V., and Champagne, M. (2004) An industrial perspective on implementing on-line applications of multivariate statstics. Journal of Process Control, 14(8):821–836.

Miller, P., Swanson, R.E., and Heckler, C.F. (1998) Contribution plots: A missing link in multivariate quality control. Applied Mathematics and Computer Science, 8(4):775–792.

Ming, R., Haibin, Y., and Heming, Y. (1998) Integrated distribution intelligent system architecture for incidents monitoring and diagnosis. Computers in Industry, 37(2):143–151.

Møller, S.F., von Frese, J., and Bro, R. (2005) Robust methods for multivariate data analysis. Journal of Chemometrics, 19(10):549–563.

Monostori, L. and Prohaszka, J. (1993) A step towards intelligent manufacturing: modelling and monitoring of manufacturing processes through artificial neural networks. CIRP Annals—Manufacturing Technology, 42(1):485–488.

Montgomery, D.C. (2005) Introduction to Statistical Quality Control. John Wiley & Sons, Hoboken, NJ, USA 5th edition.

Morud, T. (1996) Multivariate statistical process control; example from the chemical process industry. Journal of Chemometrics, 10:669–675.

Mosteller, F. and Wallace, D.L. (1963) Inference in an authorship problem. Journal of the American Statistical Association, 58(302):275–309.

Muirhead, R.J. (1982) Aspects of Multivariate Statistical Theory. John Wiley & Sons, New York, NY, USA.

Narasimhan, S. and Shah, S.L. (2008) Model identification and error covariance estimation from noisy data using PCA. Control Engineering Practice, 16(1):146–155.

Nelson, P.R.C., MacGregor, J.F., and Taylor, P.A. (2006) The impact of missing measurements on PCA and PLS prediction and monitoring applications. Chemometrics & Intelligent Laboratory Systems, 80(1):1–12.

Nelson, P.R.C., Taylor, P.A., and MacGregor, J.F. (1996) Missing data methods for PCA and PLS: score calculations with incomplete observations. Chemometrics & Intelligent Laboratory Systems, 35(1):45–65.

Nimmo, I. (1995) Adequate address abnormal situation operations. Chemical Engineering Progress, 91(1):36–45.

Nomikos, P. and MacGregor, J.F. (1994) Monitoring of batch processes using multiway principal component analysis. AIChE Journal, 40:1361–1375.

Nomikos, P. and MacGregor, J.F. (1995) Multivariate SPC charts for monitoring batch processes. Technometrics, 37(1):41–59.

Oakland, J.S. (2008) Statistical Process Control. Butterworth-Heinemann, Oxford, UK, 6th edition.

Paige, C.C. (1980) Accuracy and effectiveness of the Lanczos algorithm for the symmetric eigenproblem. Linear Algebra and its Applications, 34:235–258.

Parlett, B.N. (1980) The Symmetric Eigenvalue Problem. Prentice Hall, Englewood Cliffs, NJ, USA.

Pearson, C. (1901) On lines and planes of closest fit to systems of points in space. Phil. Mag., Series B., 2(11):559–572.

Pfafferott, J., Herkela, S., and Wambsganß, M. (2004) Design, monitoring and evaluation of a low energy office building with passive cooling by night ventilation. Energy and Buildings, 36(5):455–465.

Phillips, G.R. and Eyring, M.E. (1983) Comparison of conventional and robust regression analysis of chemical data. Analytical Chemistry, 55(7):1134–1138.

Piovoso, M.J. and Kosanovich, K.A. (1992) Process data chemometric. IEEE Transactions on Instrumentation and Measurement, 41(2):262–268.

Piovoso, M.J., Kosanovich, K.A., and Pearson, P.K. (1991) Monitoring process performance in real-time. In Proceedings of the American Control Conference, Chicago, IL, USA.

Powers, W.F. and Nicastri, P.R. (1999) Automotive vehicle control challenges in the twenty-first century. In Proceedings of the 14th IFAC World Congress, pages 11–29, Beijing, P.R. China.

Pranatyasto, T.N. and Qin, S.J. (2001) Sensor validation and process fault diagnosis for FCC units under MPC feedback. Control Engineering Practice, 9(8):877–888.

Pujadó, P.R. and Moser, M. (2006) Catalytic reforming. In Handbook of Petroleum Processing, pages 217–237. Springer, The Netherlands.

Qin, S.J. (1998) Recursive PLS algorithms for adaptive data modelling. Computers and Chemical Engineering, 22(4-5):503–514.

Qin, S.J., Cherry, G., Good, R., Wang, J., and Harrison, C.A. (2006) Semiconductor manufacturing process control and monitoring: a fab-wide framework. Journal of Process Control, 16(3):179–191.

Qin, S.J. and Dunia, R. (2000) Determining the number of principal components for best reconstruction. Journal of Process Control, 10(2-3):245–250.

Qin, S.J., Valle, S., and Piovoso, M.J. (2001) On unifying multiblock analysis with application to decentralized process monitoring. Journal of Chemometrics, 15(9):715–742.

Qing, Z. and Zhihan, X. (2004) Design of a novel knowledge-based fault detection and isolation scheme. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(2):1089–1095.

Raich, A. and Çinar, A. (1996) Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. AIChE Journal, 42(4):995–1009.

Ramaker, H.-J., van Sprang, E.N., Westerhuis, J.A., and Smilde, A.K. (2004) The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring. Chemometrics & Intelligent Laboratory Systems, 73(2):181–187.

Rännar, S., Geladi, P., Lindgren, F., and Wold, S. (1995) A PLS kernel algorithm for data sets with many variables and fewer objects. part II: Cross-validation, missing data and examples. Journal of Chemometrics, 9(6):459–470.

Rännar, S., Lindgren, F., Geladi, P., and Wold, S. (1994) A PLS kernel algorithm for data sets with many variables and fewer objects. part I: Theory and algorithm. Journal of Chemometrics, 8(2):111–125.

Rissanen, J. (1978) Modelling by shortest data description. Automatica, 14(2):465–471.

Rocke, D.M. and Woodruff, D.L. (1996) Identification of outliers in multivariate data. Journal of the American Statistical Association, 91:1047–1061.

Rousseeuw, P.J. (1984) Least median of squares regression. Journal of the American Statistical Association, 79:871–880.

Rousseeuw, P.J. and Croux, C. (1993) Alternatives to median absolute deviation. Journal of the American Statistical Association, 88:1273–1283.

Rousseeuw, P.J. and Driessen, K. (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3):212–223.

Rousseeuw, P.J. and Hubert, M. (2011) Robust statistics for outlier detection. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1):73–79.

Rozett, R.W. and Petersen, E.M. (1975) Methods of factor analysis of mass spectra. Analytical Chemistry, 47(8):1301–1308.

Russell, N.S., Farrier, D.R., and Howell, J. (1985) Evaluation of multinormal probabilities using Fourier series expansions. Journal of the Royal Statistical Society. Series C (Applied Statistics), 34(1):49–53.

Satterthwaite, F.E. (1941) Synthesis of variance. Psychometrica, 6:309–316.

Schmidt-Traub, H. and Górak, A. (2006) Integrated Reaction and Separation Operations: Modelling and Experimental Validation. Chemische Technik/Verfahrenstechnik. Springer-Verlag, Berlin.

Schubert, U., Kruger, U., Arellano-Garcia, H., Feital, T., and Wozny, G. (2011) Unified model-based fault diagnosis for three industrial application studies. Control Engineering Practice, 19(5):479–490.

Schuler, H. (2006) Automation in chemical industry. ATP Automatisierungstechnische Praxis, 54(8):363–371.

Schwarz, G. (1978) Estimating the dimension of a model. Annals of Statistics, 6(2):461–464.

Sharma, S.K. and Irwin, G.W. (2003) Fuzzy coding of genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(4):344–355.

Shewhart, W.A. (1931) Economic Control of Quality of Manufactured Product. Van Nostrand Reinhold, Princeton, NJ, USA.

Shewhart, W.A. (1939) Statistical Method from the Viewpoint of Quality Control. The Graduate School of the Department of Agriculture, Washington DC, USA. Reprinted by Dover: Toronto, 1986.

Shin, R. and Lee, L.S. (1995) Use of fuzzy cause-effect digraph for resolution fault diagnosis of process plants I. fuzzy cause-effect digraph. Industrial & Engineering Chemistry Research, 34:1688–1702.

Shing, C.T. and Chee, P.L. (2004) Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant. IEEE Transactions on Energy Conversion, 19(2):369–377.

Simani, S., Patton, R.J., and Fantuzzi, C. (2002) Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques. Springer-Verlag New York.

Simoglou, A., Martin, E.B., and Morris, A.J. (2000) Multivariate statistical process control of an industrial fluidised-bed reactor. Control Engineering Practice, 8(8):893–909.

Skogestad, S. (2007) The do's and dont's of distillation column control. Chemical Engineering Research and Design, 85(1):13–23.

Smith, G.M. (2003) Statistical Process Control and Quality Improvement. Prentice Hall, Upper Saddel River, NJ, USA, 5th edition.

Söderström, T. (2007) Error-in-variable methods in system identification. Automatica, 43(7):939–958.

Söderström, T. and Stoica, P. (1994) System Identification. Prentice Hall, Upper Saddle River, NJ, USA.

Sohn, H., Allen, D.W., Worden, K., and Farrar, C.R. (2005) Structural damage classification using extreme value statistics. Transactions of the ASME. Journal of Dynamic Systems, Measurement and Control, 127(1):125–132.

Stahel, W.A. (1981) Robust Estimation, Infinitisimal Optimality and Covariance Matrix Estimator. PhD thesis, ETH Zurich, Zurich, Switzerland.

Stander, C.J., Heyns, P.S., and Schoombie, W. (2002) Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions. Mechanical Systems & Signal Processing, 16(6):1005–1024.

Staszewski, W.J. and Tomlinson, G.R. (1997) Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition. Mechanical Systems & Signal Processing, 11(5):673–692.

Stewart, D. and Love, W.A. (1968) A general canonical correlation index. Psychological Bulletin, 70(3):160–163.

Stewart, G.W. and Sun, J.-G. (1990) Matrix Perturbation Theory. Academic Press, San Diego, CA, USA.

Stone, M. (1974) Cross-validatory choice and assessment of statistical prediction (with discussion). Journal of the Royal Statistical Society (Series B), 36:111–133.

Taguchi, G. (1986) Introduction to Quality Engineering and Redesigning Quality Into Products and Processes. Asian Productivity Organisation, Tokyo.

Tan, C. and Mba, D. (2005) Limitation of acoustic emission for identifying seeded defects in gearboxes. Nondestructive Evaluation, 24(1):11–28.

Tate, R.F. and Klett, G.W. (1959) Optimal confidence intervals for the variance of a normal distribution. Journal of the American Statistical Association, 54(287):674–682.

Tates, A.A., Louwerse, D.J., Smilde, A.K., Koot, G.L.M., and Berndt, H. (1999) Monitoring a PVC batch process with multivariate statistical process control charts. Industrial & Engineering Chemistry Research, 38(12):4769–4776.

ten Berge, J.M.F. (1985) On the relationship between Fortier's simultaneous linear prediction and van den Wollenberg's redundancy analysis. Psychometrika, 50(1):121–122.

ter Braak, C.J.F. and de Jong, S. (1998) The objective function of partial least squares regression. Journal of Chemometrics, 12(1):41–54.

Thompson, J.R. and Koronacki, J. (2002) Statistical Process Control—The Deming Paradigm and Beyond. Chapman & Hall/CRC Press, Boca Raton, 2nd edition.

Thompson, W.A. and Endriss, J. (1961) The required sample size when estimating variances. The American Statistician, 15(3):22–23.

Tracey, N.D., Young, J.C., and Mason, R.L. (1992) Multivariate control charts for individual observations. Journal of Quality Technology, 24(2):88–95.

Tumer, I.Y. and Bajwa, A. (1999) A survey of aircraft engine health monitoring systems. In Proceedings of the 35th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Los Angeles, U.S.A.

Upadhyaya, B.R., Zhao, K., and Lu, B. (2003) Fault monitoring of nuclear power plant sensors and field devices. Progress in Nuclear Energy, 43(1-4):337–342.

Valle, S., Li, W., and Qin, S.J. (1999) Selection of the number of principal components: The variance of the reconstruction error criterion compared to other methods. Industrial & Engineering Chemistry Research, 38:4389–4401.

van den Wollenberg, A.L. (1977) Redundancy analysis and alternative for canonical correlation analysis. Psychometrika, 42(2):207–219.

van Huffel, S. and Vandewalle, J. (1991) The Total Least Squares Problem. Society for Industrial and Applied Mathematics, Philadelphia PA, UAS.

van Sprang, E.N.M., Ramaker, H.J., Westerhuis, J.A., Gurden, S.P., and Smilde, A.K. (2002) Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18):3979–3991.

Vedam, H. and Venkatasubramanian, V. (1999) PCA-SDG based process monitoring and fault detection. Control Engineering Practice, 7(7):903–917.

Velicer, W.F. (1976) Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3):321–327.

Veltkamp, D.J. (1993) Multivariate monitoring and modeling of chemical processes using chemometrics. In International Forum on Process Analytical Chemistry IFPAC, volume 5 of Process Control & Quality, pages 205–217, Galveston, TX, USA.

Venkatasubramanian, V., Rengaswamy, R., Yin, K., and Kavuri, S.N. (2003) A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27(3):293–311.

Walczak, B. and Massart, D.L. (1996) The radial basis functions—partial least squares approach as a flexible non-linear regression technique. Analytica Chimica Acta, 331:177–185.

Wang, W. (2008) Autoregressive model-based diagnostics for gears and bearings. Insight-Non-Destructive Testing and Condition Monitoring, 50(8):414–418.

Wang, X., Kruger, U., and Irwin, G.W. (2005) Process monitoring approach using fast moving window PCA. Industrial & Engineering Chemistry Research, 44(15):5691–5702.

Wang, X., Kruger, U., Irwin, G.W., McCullough, G., and McDowell, N. (2008) Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis. IEEE Transactions on Control Systems Technology, 16(1):122–129.

Wang, X., Kruger, U., and Lennox, B. (2003) Recursive partial least squares algorithms for monitoring complex industrial processes. Control Engineering Practice, 11(6):613–632.

Wangen, L.E. and Kowalski, B.R. (1989) A multiblock partial least squares algorithm for investigating complex chemical systems. Journal of Chemometrics, 3(1):3–20.

Wax, M. and Kailath, T. (1985) Detection of signals by information theoretic criteria. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(2):387–392.

Wentzell, P.D., Andrews, D.T., Hamilton, D.C., Faber, K., and Kowalski, B.R. (1997) Maximum likelihood principal component analysis. Journal of Chemometrics, 11(4):339–366.

Westergren, K.E., Hans Höberg, H., and Norlén, U. (1999) Monitoring energy consumption in single-family houses. Energy and Buildings, 29(3):247–257.

Westerhuis, J.A., Kourti, T., and F., M.J. (1998) Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12(5):301–321.

Wikström, C., Albano, C., Eriksson, L., et al. (1998) Multivariate process and quality monitoring applied to an electrolysis process; part I. process supervision with multivariate control charts. Chemometrics & Intelligent Laboratory Systems, 42:221–231.

Willink, T. (2008) Efficient adaptive adaptive SVD algorithm for MIMO applications. IEEE Transactions on Signal Processing, 56(2):615–622.

Wilson, D.J.W. (2001) Plant-wide multivariate SPC in fiber manufacturing. Chemical Fibers International, 51(1):72–73.

Wise, B.M. and Gallagher, N.B. (1996) The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6(6):329–348.

aWise, B.M., Ricker, N.L., and Veltkamp, D.J. (1989a) Upset and sensor failure detection in multivariate processes. Technical report, Eigenvector Research, Inc., Wenatchee, WA, USA.

bWise, B.M., Ricker, N.L., and Veltkamp, D.J. (1989b) Upset and sensor detection in a multivariate process. In AIChE Annual Meeting, San Francisco, CA, USA.

Wise, B.M., Veltkamp, D.J., Davis, B., Ricker N.L. and Kowalski, B.R. (1988) Principal Components Analysis for Monitoring the West Valley Liquid Fed Ceramic Melter. Waste Management '88 Proceedings, pp, 811–818, Tucson AZ USA.

aWold, H. (1966a) Estimation of principal components and related models by iterative least squares. In Krishnaiah, P.R., editor, Multivariate Analysis, pages 391–420. Academic Press, NY USA.

bWold, H. (1966b) Non-linear estimation by iterative least squares procedures. In David, F., editor, Research Papers in Statistics. Wiley, NY USA.

Wold, S. (1978) Cross validatory estimation of the number of principal components in factor and principal component models. Technometrics, 20(4):397–406.

Wold, S., Esbensen, K., and Geladi, P. (1987) Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2:37–52.

Wold, S., Ruhe, A., Wold, H., and Dunn, W.J. (1984) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalised inverses. SIAM Journal on Scientific and Statistical Computing, 5(3):735–743.

Wu, E.H.C., Yu, P.L.H., and Li, W.K. (2009) A smooth bootstrap test for independence based on mutual information. Computational Statistics and Data Analysis, 53:2524–2536.

Wu, J., Hsu, C., and Wu, G. (2008) Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36(3):6244–6255.

Yang, Y.M. and Guo, C.H. (2008) Gaussian moments for noisy unifying model. Neurocomputing, 71:3656–3659.

Yoon, S. and MacGregor, J.F. (2000) Statistical causal model-based approaches to fault detection and isolation. AIChE Journal, 46(9):1813–1824.

Yoon, S. and MacGregor, J.F. (2001) Fault diagnosis with multivariate statistical models part I: Using steady state fault signatures. Journal of Process Control, 11(4):287–400.

Young, P.J. (1994) A reformulation of the partial least squares regression algorithm. SIAM Journal on Scientific Computing, 15(1):225–230.

Yue, H.H. and Qin, S.J. (2001) Reconstruction-based fault identification using a combined index. Industrial & Engineering Chemistry Research, 40(20):4403–4414.

..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
13.58.113.128