Multiple oscillations detection in control loops by using the DFT and Raleigh distribution ★ ★This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada; the National Natural Science Foundation of China [61174161, 61304141, 61375077]; the specialized Research Fund for the Doctoral Program of Higher Education of China [20130121130004]; and the Fundamental Research Funds for the Central Universities in China [201212G005].

Zhang, Kangkang; Huang, Biao; Ji, Guoli

doi:10.1016/j.ifacol.2015.09.580

Cited by 12 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the frequency domain, Babuska et al 26 put forth a frequency-based method, leveraging power spectral density amplitude ratios to detect oscillations. Similarly, Zhang et al 27 employed the amplitude within the discrete Fourier transform to detect oscillations.…”

Section: ■ Introductionmentioning

confidence: 99%

Shape-Based Pattern Recognition Approaches toward Oscillation Detection

Memarian,

Damarla,

Huang

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

Oscillation in control loops is a frequent problem in the process industries. These oscillations directly impact product quality, leading to a decreased plant profit. Additionally, oscillations increase energy consumption and waste raw materials and pose a significant restriction on the performance of the operational unit. Therefore, it is essential to isolate the loops that exhibit oscillations. In this work, two practical and effective methods are proposed to detect oscillations in process control loops. Both methods are aimed at detecting the presence of a triangle-like shape in the "D vs process variable (PV)" [or "D vs controller output (OP)"] plot to identify oscillations in the control loops. Here, "D" represents the Euclidean distance, which involves the deviations from the mean values of the variables OP and PV. Method 1 accomplishes this objective in an unsupervised manner by fitting a nonlinear algebraic function to the data: "D" and "PV". Method 2 uses a deep convolutional neural network for detecting the triangle-like shape. The performance of both the methods was evaluated by applying them to benchmark control loops sourced from various industries, including chemical, paper, mining, and metal industries, along with control loops in a local refinery unit. While both methods have their own advantages and application scenarios, the results demonstrated that both the proposed methods identified oscillatory control loops for the majority of the cases studied.

show abstract

Section: ■ Introductionmentioning

confidence: 99%

Shape-Based Pattern Recognition Approaches toward Oscillation Detection

Memarian,

Damarla,

Huang

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, we are interested in determining different types of Martian ionospheric irregularities by developing an automatic detection algorithm that could separate depletions, enhancements, and oscillations. From the perspective of signal processing, this goal falls into the disciplines of peak detection and oscillation detection that have been developed for a long time involving tremendous basic algorithms, such as the Hilbert (e.g., Benitez et al., 2001), Fourier (Zhang et al., 2015), or Wavelet (e.g., Wee et al., 2008) transform‐based spectra analysis, morphology filtering (e.g., Serra, 1994), Kalman filtering (Tzallas et al., 2006), Gaussian second derivative filtering (e.g., Fredriksson et al., 2009), etc. Although those methods have been applied in various industrial and academic domains, the problem is that the more generally applicable the algorithm, the more parameters (i.e., window size, amplitude, distance) are needed.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Detection and Naive Classification of the Martian Ionospheric Irregularities

Wan,

Zhong,

Hao

et al. 2024

JGR Space Physics

View full text Add to dashboard Cite

The abundant observations and research established a detailed category of the terrestrial ionospheric irregularities, which significantly advanced our understanding of how the Earth system's complicated physical and chemical process generates the intermediate‐scale structures of the charged particles. Motivated by a future attempt at categorizing the Martian ionospheric irregularity, this study designs a method for naive classification of the plasma density depletion, enhancement, and oscillation based on the in situ measurements of the Martian ionosphere. The technique consists of several procedures: trend estimation, detrending and candidate extraction, and parameterization. The classification is achieved through a machine‐learning‐like process using some testing artificial density profiles. A preliminary credence test shows a good performance in separating the terrestrial low‐latitude Equatorial Plasma bubble (depletion) and mid‐latitude Median‐scale Traveling Ionospheric Disturbance (oscillation). Another detection experiment of the Martian plasma depletion events (collected by Basuvaraj et al. (2022a, https://doi.org/10.1029/2022je007302)) showed a recall rate (i.e., true positive) of 38% but with a high precision of 67.8%. Therefore, we believe the proposed method could convincingly extract different Martian ionospheric irregularities and help uncover the climatological characteristics in the future.

show abstract

Oscillation detection in process industries – Part I: Review of the detection methods

Dambros

Trierweiler

Farenzena

2019

Journal of Process Control

View full text Add to dashboard Cite

Cited by 12 publications

References 18 publications

Shape-Based Pattern Recognition Approaches toward Oscillation Detection

Shape-Based Pattern Recognition Approaches toward Oscillation Detection

An Automatic Method for Detection and Naive Classification of the Martian Ionospheric Irregularities

Oscillation detection in process industries – Part I: Review of the detection methods

Contact Info

Product

Resources

About