Process oscillation detection and diagnosis – a frequency-based methodology framework

Significance Statement

Modern facilities, especially within smart manufacturing environment, are massively instrumented with IP-enabled intelligent sensors and controllers gathering on-time process information at a high sampling rate. These devices produce abundant process data beyond the capacity of conventional analytical tools based on first-principles models and facilitate the development of data-driven methods. One application of data-driven methods is to detect and diagnose plant-wide oscillation problems often faced by process engineers.

This paper addresses the challenge of plant oscillations caused by poorly tuned controllers; actuator nonlinearities or valve stiction at one point often propagate to the whole operating unit or the whole plant because of a highly integrated system, leading to inferior quality products or even plant safety issues. Conventional physical modeling tools cannot extract useful information from abundant plant data on quickly locating the root cause of oscillation and the relevant variables affected along propagation routes, especially with the interference of noise.

In this paper, the authors proposed a novel framework within the frequency domain to provide systematic diagnosis, which consists of steps like spectral envelope-based band selection, spectral transfer entropy based cause-effect analysis and root cause validation by wavelet power spectrum. Test cases based on simulated data and industrial data provided by Eastman Chemical Company are provided.

The results demonstrate successful applications of such a framework: the common frequency reflecting abnormal oscillations and associated contributing indicators, cause-effect relationship between those variables and the resulting process topology within frequency band selected, and the oscillation occurring time can be all visualized intuitively. The negative influence of noise coming from contaminated raw plant measurements is mitigated.

In summary, the frequency-based methodology framework presented in their research provides a convenient and promising data-driven tool to overcome the plant-wide oscillation detection and diagnosis challenge faced by process engineers.

 

Process oscillation detection and diagnosis. Advances in Engineering

About the author

Shu Xu is a postdoctoral fellow in the Department of Chemical Engineering at Purdue University. His current research focuses on quantifying process flexibility and resiliency. He received his PhD degree from McKetta Department of Chemical Engineering at the University of Texas at Austin. 

About the author

Michael Baldea is Assistant Professor in the McKetta Department of Chemical Engineering at the University of Texas at Austin. He obtained his Diploma (2000) and MSc (2001) from “Babes-Bolyai” University in Cluj-Napoca, Romania, and his PhD (2006) from the University of Minnesota, all in chemical engineering. His research concentrates on the modeling, analysis, optimization and control of process and energy systems, areas in which he has published over 60 refereed papers. He is the recipient of several research and service awards, including the NSF CAREEER Award, the Moncrief Grand Challenges Prize, the Model-Based Innovation Prize from Process Systems Enterprise, and the Best Referee Award from the Journal of Process Control. 

About the author

Thomas F. Edgar is the Abell Chair in Engineering Professor of Chemical Engineering at the University of Texas at Austin and Director of the UT Energy Institute.  Dr. Edgar received his B.S. degree in chemical engineering from the University of Kansas and a Ph.D. from Princeton University.  For the past 40 years, he has concentrated his academic work in process modeling, control, and optimization, with over 400 articles and book chapters. Dr. Edgar has received major awards from AIChE, ASEE, and AACC and is a member of the National Academy of Engineering.

 

About the author

Mark Nixon was lead architect for DeltaV from its inception through 2005. In 2006 he took a very active role in the design and standardization of WirelessHART. He currently leads the applied research group where he is pursuing his interests in control, big data analytics, wireless, operator interfaces, and advanced graphics. He holds over 90 patents and has coauthored four books on wireless and control. He is an ISA Fellow and a member of the Automation Hall of Fame. He received his bachelors from the University of Waterloo in Canada. 

About the author

Terry Blevins received a Master of Science in Electrical Engineering from Purdue University in 1973.  He lead the development of DeltaV advanced control products and coauthored the book Wireless Control Foundation and  ISA bestselling books Advanced Control Foundation and Control Loop Foundation.  Terry is a member of Control Magazine’s Process Automation Hall of Fame and an ISA Fellow.   Presently, he is a principal technologist in the applied research team at Emerson Process Management.   

About the author

Willy Wojsznis has earned Engineering Degree in Electrical Engineering in 1964 and PhD from Technical University of Warsaw in 1973. Since 1991 Willy is with Emerson Process Management developing advanced control products. Recently he is involved in Big Data research.

His research and development resulted in thirty eight US patents and over fifty technical conference and journal papers. He coauthored ISA bestseller books Advanced Control Unleashed, Advanced Control Foundation, Wireless Control Foundation, and a chapter of ISA/CRC Instrumentation Handbook. Willy is inducted into a Control Magazine’s Process Automation Hall of Fame and is ISA Fellow and IEEE senior member. 

 

Journal Reference

Root Cause Diagnosis of Plant-Wide Oscillations Based on Information Transfer in the Frequency Domain

Ind. Eng. Chem. Res., 2016, 55 (6), pp 1623–1629.

Shu Xu1, Michael Baldea1, Thomas F. Edgar1, Willy Wojsznis2, Terrence Blevins2, Mark Nixon2

[expand title=”Show Affiliations”]
  1. McKetta Department of Chemical Engineering,The University of Texas at Austin, Austin, Texas 78712, United States
  2. Process Systems and Solutions,Emerson Process Management, Round Rock, Texas 78759, United States
[/expand]

Abstract

Plant-wide oscillations generated in a single unit can negatively affect the overall control performance of the process; thus, it is necessary to detect them and diagnose their root cause. However, the interference of noise and the need for oscillation propagation routes pose more challenges for process engineers. In this paper, the concept spectral transfer entropy is proposed and its connection to the spectral Granger causality is derived. Moreover, an information transfer method incorporating spectral envelope algorithm and spectral transfer entropy is applied to provide new diagnostic guidance, whose feasibility and effectiveness have been demonstrated by both simulated and industrial case studies. Compared with current methods, the new procedure enjoys the following advantages: (a) performing oscillation detection and diagnosis within a targeted frequency range and mitigating the effects of measurement noise outside the bandwidth; (b) provides an nominal causal map reflecting the oscillation propagation pattern. The root cause obtained by the method in the industrial case is further validated by a wavelet power spectrum.

Copyright © 2016 American Chemical Society.

Go To Ind. Eng. Chem. Res

Check Also

Computational Insights into High-Pressure Equilibria of Supercritical Gases in Ammonia - Advances in Engineering

Computational Insights into High-Pressure Equilibria of Supercritical Gases in Ammonia