Improved oscillation detection via noise-assisted data analysis

• Published in: Control engineering practice Vol. 81; pp. 162 - 171
• Main Authors: Aftab, Muhammad Faisal, Hovd, Morten, Sivalingam, Selvanathan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2018
• Subjects: Dyadic filter bank property; Mode mixing; Multiple oscillations; Multivariate empirical mode decomposition; Mode mixing; Multiple oscillations; Dyadic filter bank property; Multivariate empirical mode decomposition
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2018.08.019

Oscillation detection is usually a precursor to more advanced performance monitoring steps such as plant wide oscillation detection and root cause detection. Therefore any false or missed detection can have serious implications. Oscillation detection is a challenging problem due to the presence of noise and multiple modes in the plant data. This paper presents an improved and robust automatic oscillation detection algorithm based on noise-assisted data analysis that can handle multiple oscillatory modes in the presence of both coloured and white noise along with non-stationary effects. The dyadic filter bank property of multivariate empirical mode decomposition has been used to accurately detect the oscillations and to calculate the associated characteristics. This work improves upon the existing auto covariance function based methods. The robustness and reliability of the proposed scheme is demonstrated via simulation and industrial case studies.