Detecting non-linearity induced oscillations via the dyadic filter bank property of multivariate empirical mode decomposition. (December 2017)

• Record Type: Journal Article
• Authors: Aftab, Muhammad Faisal; Hovd, Morten; Sivalingam, Selvanathan
• Is Part Of: Journal of process control. Volume 60(2017)
• Page Start: 68
• Page End: 81
• Publication Date: 2017-12
• Subjects: Non-linearity induced oscillations -- Harmonic content -- Dyadic filter bank property -- Empirical mode decomposition -- Intra wave frequency modulation; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.08.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Automatic harmonics identification to detect non-linearity induced oscillation. Harmonic extraction via dyadic filter bank property of MEMD. Hilbert Huang Transform is used to calculate degree of non-linearity (DNL). Variable with the maximum DNL is the source of non-linearity induced oscillation. Applicable to non-stationary data as well as multiple sources of oscillations. Abstract: Non-linearity induced oscillations in control loops are characterized by the presence of higher order harmonics. In this paper the dyadic filter bank property of the multivariate empirical mode decomposition (MEMD) is exploited to reveal the harmonic content of the oscillatory signal to indicate the presence of non-linearity. Once the harmonics are identified the extent of non-linearity is evaluated automatically using degree of non-linearity measure (DNL) introduced in our previous work[11] . Although detection of non-linearity via harmonics is an old concept; any automatic method has still not been reported. Moreover, the existing methods suffer from the restrictive assumption of signal stationarity. The proposed method is more robust in identifying the non-linearity induced oscillations and is adaptive and data driven in nature and thus requires no a priori assumption about the underlying process dynamics. The proposed method can also differentiate among the different sources of multiple oscillations, for example combinations of nonlinearity and linear sources or two nonlinear sources. Apart from detecting the non-linearities the proposed method can also contribute in locating the source of non-linearity, thereby reducing the maintenance time to a considerable extent. The robustness and effectiveness of the proposed method is established using industrial case studies and results are compared with existing methods based on higher order statistics[7] and surrogate based methods[8] .