An integrated scheme for oscillation detection and diagnosis from industrial data
Shu Xu1, Willy Wojsznis2, Mark Nixon3, Michael Baldea1 and Thomas F. Edgar1
(1) McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, (2) Innovation Center, Emerson Process Management, Austin, TX, (3)Process Management, Emerson, Austin, TX
As an important type of plant-wide disturbances, oscillations generated in a single unit can propagate to several units in the plant and can negatively affect the overall control performance of the process. Thus, it is necessary to detect and diagnose such oscillations in three steps: (1) isolating relevant process variables containing such oscillations; (2) diagnosing the root cause; (3) finding the occurring time. For step (1), the spectra envelope method (Jiang et al., 2007) provides an intuitive way to visualize the dominant frequencies in the multivariate data set and a fast way to select corresponding variables containing such frequencies so that the users no long need to perform frequency analysis on individual variables. For step (2), the transfer entropy defined in Equation (1) (Schreiber, 2000) measures the information transfer from x to by evaluating the reduction of uncertainty while assuming predictability(Ping et al., 2013), and it outperforms other causality analysis methods such as the Granger's causality (Yuan & Qin, 2014) when the process cannot be approximated by a linear model. For step (3), the wavelet power spectrum demonstrated by Figure 1provides an intuitive way to find the time information of the frequency change corresponding to oscillation occurring. In this paper, an integrated scheme is proposed, which consists of above methods corresponding to each step: a spectral envelope method used for identifying variables having common oscillations, a transfer entropy method used for root cause diagnosis, and a wavelet power spectrum used for finding the oscillation occurring time based on the root cause variable. Industrial case studies are presented to demonstrate the proposed scheme.
Figure 1 Wavelet power spectrum demonstration (Aguiar-Conraria & Soares, 2011)
Jiang, H., Shoukat Choudhury, M. A. A., & Shah, S. L. (2007). Detection and diagnosis of plant-wide oscillations from industrial data using the spectral envelope method. Journal of Process Control, 17(2), 143-155.
See more of this Group/Topical: Computing and Systems Technology Division