Commenced in January 2007
Paper Count: 30172
Issues in Spectral Source Separation Techniques for Plant-wide Oscillation Detection and Diagnosis
Abstract:In the last few years, three multivariate spectral analysis techniques namely, Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF) have emerged as effective tools for oscillation detection and isolation. While the first method is used in determining the number of oscillatory sources, the latter two methods are used to identify source signatures by formulating the detection problem as a source identification problem in the spectral domain. In this paper, we present a critical drawback of the underlying linear (mixing) model which strongly limits the ability of the associated source separation methods to determine the number of sources and/or identify the physical source signatures. It is shown that the assumed mixing model is only valid if each unit of the process gives equal weighting (all-pass filter) to all oscillatory components in its inputs. This is in contrast to the fact that each unit, in general, acts as a filter with non-uniform frequency response. Thus, the model can only facilitate correct identification of a source with a single frequency component, which is again unrealistic. To overcome this deficiency, an iterative post-processing algorithm that correctly identifies the physical source(s) is developed. An additional issue with the existing methods is that they lack a procedure to pre-screen non-oscillatory/noisy measurements which obscure the identification of oscillatory sources. In this regard, a pre-screening procedure is prescribed based on the notion of sparseness index to eliminate the noisy and non-oscillatory measurements from the data set used for analysis.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327506Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1206
 S.M. Kanbur, D. Iono, N.R. Tanvir and M.A. Hendry. On the use of principal component analysis in analysing cepheid light curves. Monthly Notices of the Royal Astronomical Society, 329(1):126-134, 2002.
 B.R. Bakshi. Multiscale PCA with application to multivariate statistical process monitoring. AIChE, 44(7):1596-1610, 1998.
 D. Peter, W. Mitchell, and T. Lohnes. Maximum likelihood principal component analysis with correlated measurement errors: theoritical and practical considerations. Chemometrics and Intelligent Laboratory Systems, 45(1):65-85, 1999.
 X. Li and X. Yao. Multiscale process monitoring in machining. IEEE Transactions on Industrial Electronics, 52(3):924-925, 1998.
 D. Guillamet and J. Vitriz. A new iris recognition method using independent component analysis. In Pattern Recognit. Lett.,24, 2003.
 N.F. Thornhill and A. Horch. Advances and new directions in plantwide disturbance detection and diagnosis. Control Engineering Practice, 15(10):1196-1206, 2007.
 D.D. Lee and H.S. Seung. Learning the parts of object by nonnegative matrix factorization. Nature, 401(3):788-791, 1999.
 A.K. Tangirala, J. Kanodia and S.L. Shah. Non negative matrix factorization for detection and diagnosis of plantwide oscillations. Industrial Engineering and Chemistry Research,46(3):801-817, 2007.
 N.F. Thornhill, S.L. Shah and B.Huang. Detection and diagnosis of unit wide oscillations. Process Control and Instrumentation, 26, 2000.
 N.F. Thornhill, S.L. Shah, B.Huang and A.Vishnubhotla. Spectral principal component analysis of dynamic process data. Control Engineering Practice, 10833-846, 2002.
 Patrik O. Hoyer. Nonnegative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 514571469, 2004
 A.H. Rinen, J. Karhunen and E. Oja. Independent component analysis. In Wiley, New York, 2001.