Wavelet Transform Based Methodology for Detection and Characterization of Multiple Oscillations in Nonstationary Variables

作者：Naghoosi Elham; Huang Biao^*

来源：Industrial & Engineering Chemistry Research, 2017, 56(8): 2083-2093.

DOI：10.1021/acs.iecr.6b03075

Diagnosing the root cause of a propagated oscillation in the operation requires detection of all process variables that are oscillating with similar frequencies followed by application of an appropriate root cause diagnosis procedure. Oscillations in chemical processes are usually caused by controller tuning, valve problems, or external oscillatory disturbances. There are several methods proposed in literature for root cause diagnosis of oscillations within the system. However, most of the methodologies can only work for a specific type of oscillation. For example, the methodologies based on quantifying the nonlinearity of variables can help with root cause diagnosis of a valve-induced oscillation but cannot help if the oscillation actually has controller tuning or due to an external oscillatory disturbance. Therefore, before trying to find out which loop within the system has caused the oscillation, it is important to categorize the oscillation meaning to learn if the oscillation is caused by a nonlinear valve within the system, controller tuning or an external disturbance to choose an appropriate diagnostic procedure. The different characteristics of these three oscillation types are studied in the literature with methodologies to distinguish them from each other. However, the proposed methodologies can work reliably when there is only one oscillatory component present in the variables and cannot help in cases of multiple oscillations. Also, nonstationary trends and noise within variables are yet a challenging issue in detection and diagnosis of oscillations. This paper presents a comprehensive oscillation detection and characterization procedure based on wavelet transform. The methodology is capable of both detection and independent characterization of multiple oscillation frequencies in variables as well as implementing automatic noise and nonstationary trend removal algorithms. Advantages of the proposed method are illustrated through analysis of data sampled from an industrial process as well as simulations.

• 出版日期2017-3-1