Improved oscillation detection via noise-assisted data analysis

被引：19

作者：

Aftab, Muhammad Faisal ^{[1
]}

Hovd, Morten ^{[1
]}

Sivalingam, Selvanathan ^{[2
]}

机构：

[1] NTNU, Dept Engn Cybernet, Trondheim, Norway

[2] Siemens AS, Trondheim, Norway

来源：

CONTROL ENGINEERING PRACTICE | 2018年 / 81卷

关键词：

Multivariate empirical mode decomposition; Mode mixing; Multiple oscillations; Dyadic filter bank property; EMPIRICAL MODE DECOMPOSITION; NONNEGATIVE MATRIX FACTORIZATION; CONTROL LOOPS; MULTIPLE OSCILLATIONS; FILTER BANK; DIAGNOSIS;

D O I：

10.1016/j.conengprac.2018.08.019

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：162 / 171

页数：10

共 31 条

[1] Detecting non-linearity induced oscillations via the dyadic filter bank property of multivariate empirical mode decomposition [J].