Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 18, 2015
Ineˆs M. Cec ́ılio James R. Ottewill Nina F. Thornhill

Abstract

Irregularly spaced measurements are a common quality problem in real data and preclude the use of several feature ex- traction methods, which were developed for measurements with constant sampling intervals. Feature extraction methods based on nearest neighbors of embedded vectors are an example of such methods. This paper proposes the use of a time- based construction of embedded vectors and a weighted similarity metric within nearest neighbor-based methods in order to extend their applicability to irregularly sampled measurements. The proposed idea is demonstrated within a method of univariate detection of transient or spiky disturbances. The result obtained with an irregularly sampled measurement is benchmarked by the original regularly sampled measurement. Although the method was originally implemented for off-line analysis, the paper also discusses modifications to enable its online implementation.

How to Cite

M. Cec ́ılio I. ., R. Ottewill, J. ., & F. Thornhill, N. (2015). Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2562
Abstract 156 | PDF Downloads 125

##plugins.themes.bootstrap3.article.details##

Keywords

fault detection, fault diagnosis, similarity measures, signal analysis, irregular sampling rate, nearest neighbors

References
Babji, S., & Tangirala, A. K. (2010). Source separation in systems with correlated sources using NMF. Digital Signal Processing, 20(2), 417–432.

Bauer, M., & Thornhill, N. F. (2008). A practical method for identifying the propagation path of plant-wide disturbances. Journal of Process Control, 18(7–8), 707–719.

Bevrani, H. (2009). Robust power system frequency control. Springer.

Bos, R., de Waele, S., & Broersen, P. M. T. (2002). Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data. IEEE Transactions on Instrumentation and Measurement, 51(6), 1289–1294.

Cec ́ılio, I. M., Ottewill, J. R., Fretheim, H., & Thornhill, N. F. (2015). Multivariate detection of transient disturbances for uni- and multirate systems. IEEE Transactions on Control System Technology, 23(4), 1477–1493.

Cec ́ılio, I. M., Ottewill, J. R., Pretlove, J., & Thornhill, N. F. (2014). Nearest neighbors method for detecting transient disturbances in process and electromechanical systems. Journal of Process Control, 24(9), 1382–1393.

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.

Choudhury, M. A. A. S., Shah, S. L., & Thornhill, N. F. (2004). Diagnosis of poor control-loop performance using higher-order statistics. Automatica, 40(10), 1719–1728.

Isaksson, A. J. (1993). Identification of ARX – models sub- ject to missing data. IEEE Transactions on Automatic Con- trol, 38(5), 813–819.

Kantz, H., & Schreiber, T. (2003). Nonlinear time series analysis. Cambridge University Press.

Lall, U., & Sharma, A. (1996). A nearest neighbor bootstrap for resampling hydrologic time series. Water Resources Research, 32(3), 679–693.

Rehfeld, K., Marwan, N., Heitzig, J., & Kurths, J. (2011). Comparison of correlation analysis techniques for irregularly sampled time series. Nonlinear Processes in Geophysics, 18(3), 389–404.

Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000). Data-driven methods for fault detection and diagnosis in chemical processes (1st ed.). Springer.

Scargle, J. D. (1989). Studies in astronomical time series analysis. III. Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data. Astrophysical Journal, 343, 874–887.

Stockmann, M., Haber, R., & Schmitz, U. (2012). Source identification of plant-wide faults based on k nearest neigh- bor time delay estimation. Journal of Process Control, 22(3), 583–598.

Stoica, P., Li, J., & He, H. (2009). Spectral analysis of nonuniformly sampled data: a new approach versus the periodogram. IEEE Transactions on Signal Processing, 57(3), 843–858.

Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. SIAM Journal on Mathematical Analysis, 29(2), 511–546.

Tangirala, A. K., Kanodia, J., & Shah, S. L. (2007). Non- Negative matrix factorization for detection and diagnosis of plantwide oscillations. Industrial & Engineering Chemistry Research, 46(3), 801–817.

Thornhill, N. F. (2005). Finding the source of nonlinearity in a process with plant-wide oscillation. IEEE Transactions on Control Systems Technology, 13(3), 434–443.

Thornhill, N. F., Shah, S. L., Huang, B., & Vishnubhotla, A. (2002). Spectral principal component analysis of dynamic process data. Control Engineering Practice, 10(8), 833–846.

Zang, X., & Howell, J. (2007). Isolating the source of whole- plant oscillations through bi-amplitude ratio analysis. Control Engineering Practice, 15(1), 69–76.

Zumbach, G., & Mu ̈ller, U. (2001). Operators on inhomogeneous time series. International Journal of Theoretical and Applied Finance, 4(01), 147–177.
Section
Technical Research Papers