Weitere Kapitel dieses Buchs durch Wischen aufrufen
Signal processing of vibration signals from rotating machinery has been an active research area for recent years. Especially, discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. However, the number of retained DWT features can be still too large to be used for standard multivariate statistical process control (SPC) techniques although DWT significantly reduces the dimensionality of the data. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most of methods require a parametric distributional assumption that restricts their feasibility to specific problem of process control and thus limits their applications. This study introduced new feature extraction technique to alleviate the high dimensionality problem of implementing multivariate SPC when the quality characteristic is a vibration signal from bearing system. A set of multiscale wavelet scalogram features was generated to reduce the dimensionality of data, and is combined with the bootstrapping technique as nonparametric density estimation to set up an upper control limit of control chart. Our example and numerical simulation of a bearing system demonstrated that the proposed method has satisfactory fault-discriminating ability without any distributional assumption.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Bakir, S. (2006): Distribution-free quality control charts based on signed rank-like statistics. Communications in Statistics: Theory and Methods, 35, pp. 743–757.
Bajgier, S.M. (1992): The use of bootstrapping to construct limits on control charts. Proc. Decision Science Institute, San Diego, CA, pp. 1611–1613.
Chakraborti, S., Van der Laan, P. and Bakir, S. T. (2001): Nonparametric control chart: an overview and some results. Journal of Quality Technology, 33(3), pp. 304–315.
Chou, Y.-M., Mason, R. L., 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.
Cong, F., Chen, J., and Dong, G. (2010): “Research on the order selection of the autoregressive modeling for rolling bearing diagnosis”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 224(10), pp. 2289–2297.
Dyer, D. and Stewart, R. M., (1978): “Detection of rolling element bearing damage by statistical vibration analysis”, Journal of Vibration and Acoustics: Transactions of the ASME, 100(2), pp. 229–235.
Efron, B. (1979): Bootstrap method: another look at jackknife. Annals of Statistics, 7(1), pp. 1–26.
Fan, J. (1996): “Test of significance based on wavelet thresholding and Neyman’s truncation”, Journal of American Statistical Association, 91, pp. 674–688.
Ganesan, R., Das, T. K., and Venkataraman, V. (2004): “Wavelet-based multiscale statistical process monitoring: A literature review”, IIE Transactions, 36, pp. 787–806.
Goswami, J. C. and Chan, A. K. (1999): Fundamentals of Wavelets: Theory, Algorithms, and Applications. Wiley, New York, NY.
Hall, P., Poskitt, D. S., and Presnell, D. (2001): “Functional data-analytic approach to signal discrimination”, Technometrics, 43(1), pp. 1–9.
Hotelling, H. (1947): Multivariate quality control, in Techniques of Statistical Analysis. Eisenhart, C., Hastay, M. W. and Wills, W. A. (eds), McGraw-Hill, New York, NY, pp. 111–184.
Jardine, A. K. S., Lin D., Banjevic, D. (2006): “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing, 20(7), pp. 1483–1510.
Jeong, M. K., Chen, D., Lu, J. C. (2003): “Thresholoded scalogram and its applications in process fault detection”, Applied Stochastic Models in Business and Industry, 19, pp. 231–244.
Jones, L. A. and Woodall, W. H. (1998): “The performance of bootstrap control charts” , Journal of Quality Technology, 30(4), pp. 362–375.
Jung, U., Jeong, M. K. and Lu, J. C. (2006): “A vertical-energy-thresholding procedure for data reduction with multiple complex curves”, IEEE Transactions on Systems, Man, and Cybernetics-Part B, 36(5), pp. 1128–1138.
Jung, U. and Koh, B. H. (2009): “Structural damage localization using wavelet-based silhouette statistics”, Journal of Sound and Vibration, 321, pp. 590–604.
Kim, S. H., Alexopoulos, C., Tsui, K. L. and Wilson, J. R. (2007): “A distribution-free tabular CUSUM chart for autocorrelated data” , IIE Transactions, 39(3), pp. 317–330.
Koh, B. H., Nagarajaiah, S., Phan, M. Q. (2008): “Reconstructing structural changes in a dynamic system from experimentally identified state-space models”, Journal of Mechanical Science and Technology, 22(1), pp. 103–112.
Law, S. S., Li, X. Y., Zhu, X. Q., and Chan, S. L. (2005): “Structural damage detection from wavelet packet sensitivity”, Engineering Structures, 27, pp. 1339–1348.
Lei, Y., He, Z., and Zi, Y. (2011): “EEMD method and WNN for fault diagnosis of locomotive roller bearings” , Expert Systems with Applications, 38(6), pp. 7334–7341.
Li, H., Deng, X., and Dai, H. (2007): “Structural damage detection using the combination method of EMD and wavelet analysis”, Mechanical Systems and Signal Processing, 21, pp. 298–306.
Li, Z., Xia, S., Wang, J. and Su, X. (2006): “Damage detection of cracked beams based on wavelet transform”, International Journal of Impact Engineering, 32, pp. 1190–1200.
Lin, J. and Zhang, A. (2005): “Fault feature separation using wavelet-ICA filter”, NDT&E International, 38(6), pp. 421–427.
Lio, Y. L. and Park, C. (2008): “A bootstrap control chart for Birnbaum-Saunders percentiles”, Quality and Reliability Engineering International, 24, pp. 585–600.
Liu, R. Y. and Tang, J. (1996): “Control charts for dependent and independent measurements based on bootstrap methods”, Journal of the American Statistical Association, 91, pp. 1694–1700.
Liu, R. Y., Singh, K. and Teng, J. H. (2004): “DDMA-charts: nonparametric multivariate moving average control charts based on data depth”, Allgemeines Statistisches Archiv, 88(2), pp. 235–258.
Mallat, S. G. (1989): A Wavelet Tour of Signal Processing. Academic Press, San Diego.
McFadden, P.D., and Smith, J. D. (1984): “Model for the vibration produced by a single point defect in a rolling element bearing”, Journal of Sound and Vibration, 96(1), pp. 69–82.
Mason, R. L., Young, J. C. (2002): Multivariate Statistical Process Control with Industrial Applications. ASA/SIAM: Philadelphia, PA.
Palacz, M., and Krawczuk, M. (2002), Vibration parameters for damage detection in structures, Journal of Sound and Vibration, Vol.249, No.5, 999–1010.
Peng, Z., Chu, F., and He, Y. (2002): “Vibration signal analysis and feature extraction based on reassigned wavelet scalogram” , Journal of Sound and Vibration, 253(5), pp. 1087–1100.
Phaladiganon, P., Kim, S. B., Chen, V. C. P., Baek, J. G. and Park, S. K. (2011): “Bootstrap-based T2 multivariate control charts”, Communications in Statistics-Simulation and Computation, 40, pp. 645–662.
Polansky, A. M. (2005): “A general framework for constructing control charts”, Quality and Reliability Engineering International, 21, pp. 633–653.
Prabhakar, S., Mohanty, A. R., Sekhar, A. S. (2002): “Application of discrete wavelet transform for detection of ball bearing race faults” , Tribology International, 35(12), pp. 793–800.
Qiu, P. (2008): “Distribution-free multivariate process control based on log-linear modeling”, IIE Transactions, 40(7), pp. 664–677.
Ramsay, J. O., and Silverman, B. W. (1997): “Functional linear models for scalar responses.” Functional Data Analysis. Springer New York, pp. 157–177.
Randall, R. B. and Antoni, J. (2011): “Rolling element bearing diagnostics–A tutorial”, Mechanical Systems and Signal Processing, 25(2), pp. 485–520.
Rioul, O. and Vetterli, M. (1991): “Wavelets and signal processing”, IEEE Signal Processing Magazine, pp. 14–38.
Royston, J. P. (1983): “Sone techniques for assessing multivariate normality based on the Shapiro-Wilk W”, Applied Statistics, 32(2), pp. 121–133.
Scargle, J. D. (1997): “Wavelet methods in astronomical time series analysis”, Application of Time Series Analysis in Astronomy and Meteorology, Chapman & Hall, New York, pp. 226–248.
Seppala, T., Moskowitz, H., Plante, R., and Tang, J. (1995): “Statistical process control via the subgroup bootstrap”, Journal of Quality Technology, 27, pp. 139–153.
Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., and Nadler, B.R. (2003), A review of structural health monitoring literature: 1996 ~ 2001, Technical Reports LA-13976-MS, Los Alamos National Laboratory.
Sukchotrat, T., Kim, S. B., and Tsung, F. (2010): “One-class classification-based control chart for multivariate process monitoring”, IIE Transactions, 42, pp. 107–120.
Swamidas, A.S.J. and Chen, Y. (1995), Monitoring crack growth through change of modal parameters, Journal of Sound and Vibration, Vol.186, No.2, 325–343.
Yu, Y., YuDejie, Junsheng, C. (2006): “A roller bearing fault diagnosis method based on EMD energy entropy and ANN”, Journal of Sound and Vibration, 294(1–2), pp. 269–277.
Vidakovic, B. (1999): Statistical Modeling by Wavelets. John Wiley & Sons.
Wang W. and Wong A. K., (2002): “Autoregressive model-based gear fault diagnosis”, Journal of Vibration and Acoustics: Transactions of the ASME, 124, pp. 172–179.
Woodall, W. H. (2000): “Controversies and contradictions in statistical process control” , Journal of Quality Technology, 32(4), pp. 341–350.
Woodall, W. H. and Montgomery, D. C. (1999): “Research issues and ideas in statistical process control” , Journal of Quality Technology, 31(4), pp. 376–386.
Zhang, H. and Albin, S. (2007): “Determining the number of operational modes in baseline multivariate SPC data.” IIE Transactions 39(12) pp. 1103–1110.
- Nonparametric Wavelet-Based Multivariate Control Chart for Rotating Machinery Condition Monitoring
- Springer Singapore
in-adhesives, MKVS, Neuer Inhalt/© Zühlke, Technisches Interface Design/© scyther5 | Getty Images | iStock