An Integrated Scheme for Oscillation Detection and Diagnosis from Industrial Data

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.

                                      

where p represents the complete or conditional probability density function, ,  ,  is the sampling period, and h is the prediction horizon.

Figure 1 Wavelet power spectrum demonstration (Aguiar-Conraria & Soares, 2011)

(a), where   (b) wavelet power spectrum of  (c) Global wavelet power spectrum°ªaverage wavelet power for each frequency (d) Fourier power spectral density Reference

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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.

Ping, D., Fan, Y., Tongwen, C., & Shah, S. L. (2013). Direct Causality Detection via the Transfer Entropy Approach. Control Systems Technology, IEEE Transactions on, 21(6), 2052-2066.

Schreiber, T. (2000). Measuring Information Transfer. Physical Review Letters, 85(2), 461-464.

Yuan, T., & Qin, S. J. (2014). Root cause diagnosis of plant-wide oscillations using Granger causality. Journal of Process Control, 24(2), 450-459.