A method for detecting non-stationary oscillations in process plants

Abstract

This paper proposes a method for detecting oscillations in non-stationary time series based on the statistical properties of zero-crossings. The main development presented is a technique to remove non-stationary trend component from the analysed signals before applying an oscillation detection procedure. First, the method extracts the signal's baseline that is utilized to stationarize the signal. Then, an index describing the regularity of the stationarized signal's zero-crossings is computed in order to determine the presence of oscillation. The properties and performance of the method are analysed in simulation studies. Furthermore, the method is comprehensively tested with industrial data in a comparative study in which the proposed method is tested against other oscillation detection methods using industrial benchmark data and in tests on paperboard machine process . Finally, the simulation and industrial results are analysed and discussed.

Detecting Nonlinear Oscillations in Process Control Loop Based on an Improved VMD

“…A penalty term α and Lagrangian multiplier λ are adopted to convert (1) into an augmented Lagrangian (2) that can be iteratively solved by Alternating Direction Method of Multipliers (ADMM), which is briefly described in Algo. 1. More details can be found in Ref.…”

“…Oscillation is one of the major issues in many process industries, which may result in degradation of control loop performance such as increased energy consumption, waste of raw material, sometimes a less uniform end product and even compromised stability and safety [1]- [3]. As is mentioned in [4], nonlinearities, bad controller tuning and external disturbances are three main causes of oscillation where 30% of these control loops are oscillating because of the valve problem.…”

Use of Fast Multivariate Empirical Mode Decomposition for Oscillation Monitoring in Noisy Process Plant

“…Oscillation is one of the most common abnormal phenomena that has evoked considerable interest in both academic and process industries in recent years . Oscillations that propagate throughout a plant can severely impact the process with the consequences of inferior-quality products, increased energy consumption, and even compromised stability and safety. , This has led to a rapid development of research in fields of oscillation detection and diagnosis. , …”

“…Typically, techniques including integral absolute error (IAE), autocovariance function (ACF), , discrete cosine transform (DCT), the statistical indicators of zero-crossings, linear predictive coding (LPC), and machine learning realized the detection of oscillations in stationary processes. Recently, advances in signal acquisition have promoted the applications of time–frequency tools for adaptively extracting the oscillations from nonlinear and nonstationary processes.…”

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

“…Common industrial practice is either to exclude from the analysis time intervals exhibiting transients or to completely drop out signals with transients . However, to allow accurate oscillation detection, the oscillation should persist during eight cycles at minimum and excluding time intervals with transients can hinder that .…”