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Simplified time series representations for efficient analysis of industrial process data

Published online by Cambridge University Press:  07 November 2003

ESA ALHONIEMI
ESA ALHONIEMI
Affiliation:
Department of Information Technology, University of Turku, Turku, Finland
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Abstract

The data storage capacities of modern process automation systems have grown rapidly. Nowadays, the systems are able to frequently carry out even hundreds of measurements in parallel and store them in databases. However, these data are still rarely used in the analysis of processes. In this article, preparation of the raw data for further analysis is considered using feature extraction from signals by piecewise linear modeling. Prior to modeling, a preprocessing phase that removes some artifacts from the data is suggested. Because optimal models are computationally infeasible, fast heuristic algorithms must be utilized. Outlines for the optimal and some fast heuristic algorithms with modifications required by the preprocessing are given. In order to illustrate utilization of the features, a process diagnostics framework is presented. Among a large number of signals, the procedure finds the ones that best explain the observed short-term fluctuations in one signal. In the experiments, the piecewise linear modeling algorithms are compared using a massive data set from an operational paper machine. The use of piecewise linear representations in the analysis of changes in one real process measurement signal is demonstrated.

Keywords

Piecewise Linear ModelingPreprocessingProcess AnalysisProcess DataTime Series
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 2 , May 2003 , pp. 103 - 114
Copyright
2003 Cambridge University Press

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References

REFERENCES

Bakshi, B.R. (1999). Multiscale analysis and modeling using wavelets. Journal of Chemometrics, 13(3–4), 415–434.Google Scholar
Bakshi, B.R. & Stephanopoulos, G. (1994). Representation of process trends—III. A multiscale extraction of trends from process data. Computers and Chemical Engineering, 18(4), 267–302.Google Scholar
Basseville, M. & Nikiforov, I.V. (1993). Detection of Abrupt Changes—Theory and Application. Englewood Cliffs, NJ: Prentice–Hall. Available on-line at http://www.irisa.fr/sigma2/kniga/.
Bellman, R. (1961). On the approximation of curves by line segments using dynamic programming. Communications of the ACM, 4(6), 284.Google Scholar
Bishop, C.M. (1995). Neural Networks for Pattern Recognition. New York: Oxford University Press.
Cantoni, A. (1971). Optimal curve fitting with piecewise linear functions. IEEE Transactions on Computers, C-20(1), 59–67.Google Scholar
Chen, B.H., Wang, X.Z., Yang, S.H., & McGreavy, C. (1999). Application of wavelets and neural networks to diagnostic system development, 1, Feature extraction. Computers and Chemical Engineering, 23(7), 899–906.Google Scholar
Cheung, J.T.-Y. & Stephanopoulos, G. (1990). Representation of process trends—part I. A formal representation framework. Computers and Chemical Engineering, 14(4/5), 495–510.Google Scholar
Chu, K.K.W. & Wong, M.H. (1999). Fast time-series searching with scaling and shifting. Proc. 18th ACM SIGMOD–SIGACT–SIGART Symp. on Principles of Database Systems, pp. 237–248.
Djurić, P.M. (1994). A MAP solution to off-line segmentation of signals. Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Vol. 4, pp. 505–508.
Faloutsos, C., Ranganathan, M., & Manolopoulus, Y. (1994). Fast subsequence matching in time-series databases. Proc. 1994 ACM SIGMOD Int. Conf., pp. 419–429.
Guralnik, V. & Srivastava, J. (1999). Event detection from time series data. Proc. Fifth ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 33–42.
Hawkins, D.M. (1976). Point estimation of the parameters of piecewise regression models. Applied Statistics, 25(1), 51–57.Google Scholar
Himberg, J., Korpiaho, K., Mannila, H., Tikanmäki, J., & Toivonen, H. (2001). Time series segmentation for context recognition in mobile devices. Proc. 2001 IEEE Int. Conf. Data Mining, pp. 203–210.
Hyvärinen, A. (1999). Survey on independent component analysis. Neural Computing Surveys, 2, 94–128. Available on-line at http://www.cse.ucsc.edu/NCS/.Google Scholar
Imai, H. & Iri, M. (1986). An optimal algorithm for approximating a piecewise linear function. Journal of Information Processing, 9(3), 159–162.Google Scholar
Keogh, E. & Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. Proc. Third Int. Conf. Knowledge Discovery and Data Mining, pp. 24–30.
Konstantinides, K. & Natarajan, B.K. (1994). An architecture for lossy compression of waveforms using piecewise-linear approximation. IEEE Transactions on Signal Processing, 42(9), 2449–2454.Google Scholar
Kourti, T. & MacGregor, J.F. (1995). Process analysis, monitoring, and diagnosis using multivariate projection methods. Chemometrics & Intelligent Laboratory Systems, 28(1), 3–21.Google Scholar
Li, R.F. & Wang, X.Z. (2002). Dimension reduction of process dynamic trends using independent component analysis. Computers and Chemical Engineering, 26(3), 467–473.Google Scholar
Love, P.L. & Simaan, M. (1988). Automatic recognition of primitive changes in manufacturing process signals. Pattern Recognition, 4(21), 333–342.Google Scholar
McLeod, S., Nesic, Z., Davies, M.S., Dumont, G.A., Lee, F., Lofkrantz, E., & Shaw, I. (1998). Paper machine data analysis and display using wavelet transforms. IEEE Industry Applications 1998, Dynamic Modeling Control Applications for Industry Workshop, pp. 59–62.
Nesic, Z., Davies, M., & Dumont, G. (1996). Paper machine data compression using wavelets. Proc. 1996 IEEE Int. Conf. Control Applications, pp. 161–166.
Nygaard, R., Melnikov, G., & Katsaggelos, A.K. (2001). A rate distortion optimal ECG coding algorithm. IEEE Transactions on Biomedical Engineering, 48(1), 28–40.Google Scholar
Oliver, J.J., Baxter, R.A., & Wallace, C.S. (1998). Minimum message length segmentation. Proc. Second Pacific-Asia Conf. Knowledge Discovery and Data Mining, pp. 222–233.
Pavlidis, T. (1973). Waveform segmentation through functional approximation. IEEE Transactions on Computers, C-22(7), 689–697.Google Scholar
Pavlidis, T. (1974). Segmentation of plane curves. IEEE Transactions on Computers, C-23(8), 860–870.Google Scholar
Prandoni, P., Goodwin, M., & Vetterli, M. (1997). Optimal time segmentation for signal modeling and compression. Proc. 1997 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Vol. 3, pp. 2029–2032.
Rengaswamy, R. & Venkatasubramanian, V. (1995). A syntactic pattern-recognition approach for process monitoring and fault diagnosis. Engineering Applications of Artificial Intelligence, 8(1), 35–51.Google Scholar
The MathWorks, Inc. (1999). Using Matlab. Natick, MA: The MathWorks, Inc.
Vedam, H. & Venkatasubramanian, V. (1997). A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis. Proc. American Control Conf., pp. 309–313.
Wang, X.Z. (1999). Data Mining and Knowledge Discovery for Process Monitoring and Control. London: Springer.
Wu, L.-D. (1984). A piecewise linear approximation based on a statistical model. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(1), 41–45.CrossRefGoogle Scholar
Xiong, Z., Herley, C., Ramchandran, K., & Orchard, M.T. (1994). Flexible time segmentations for time-varying wavelet packets. Proc. IEEE-SP Int. Symp. on Time-Frequency and Time-Scale Analysis, pp. 9–12.
Zhang, H., Tangirala, A.K., & Shah, S.L. (1999). Dynamic process modeling using multiscale PCA. Proc. 1999 IEEE Canadian Conf. Electrical and Computer Engineering, pp. 1579–1584.