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Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

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Measurement Science and Technology
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Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized variational mode decomposition (VMD) and improved deep belief network-extreme learning machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian–Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of generalized Gaussian distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract GDE. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.
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Measurement Science and Technology
Meas. Sci. Technol. 35 (2024) 075103 (15pp) https://doi.org/10.1088/1361-6501/ad3be0
Improved DBO-VMD and optimized
DBN-ELM based fault diagnosis for
control valve
Dengfeng Zhang1, Chi Zhang1,, Xiaodong Han2and Cunsong Wang1
1Institute of Intelligent Manufacturing, Nanjing Tech University, Nanjing 210009, People’s Republic of
China
2Institute of Telecommunication and Navigation Satellites, China Academy of Space Technology,
Beijing, People’s Republic of China
E-mail: zc18336113545@163.com and zhdf@njtech.edu.cn
Received 5 February 2024, revised 2 April 2024
Accepted for publication 8 April 2024
Published 16 April 2024
Abstract
Control valves play a vital role in process production. In practical applications, control valves
are prone to blockage and leakage faults. At the small control valve openings, the vibration
signals exhibit the drawbacks of signicant interference and weak fault characteristics, which
causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on
optimized variational mode decomposition (VMD) and improved deep belief network-extreme
learning machine (DBN-ELM) is proposed. Firstly, good point set population initialization,
nonlinear convergence factor, and adaptive Gaussian–Cauchy mutation strategies are applied in
the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO
(IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the
generalized dispersion entropy (GDE) is formed by the combination of generalized Gaussian
distribution and rened composite multiscale uctuation-based dispersion entropy. The
maximum correlation coefcient modal components are applied to extract GDE. Finally, the
IDBO is applied to optimize the parameters of the DBN-ELM network to improve the
classication performance of control valve faults. The comparative experiment results
demonstrate that the proposed model can extract effective features and the diagnostic accuracy
reaches 99.87%.
Keywords: control valve, fault diagnosis, generalized Gaussian distribution (GGD),
rened composite multiscale uctuation-based dispersion entropy
1. Introduction
As important actuactors, the control valves are widely applied
in process industries such as petrochemical production, chem-
ical industry, and environmental protection [1]. The faults of
control valves not only affect their performance, but also slow
down the progress of chemical engineering production, and
even lead to safety accidents [2]. Multiple types of faults occur
in the control valves including blockage, leakage, stickiness of
the valve stem, and spring failure [3,4]. The most prevalent
Author to whom any correspondence should be addressed.
faults are blockage and leakage [5]. For example, the valve
leakages account for 54% of UF6 gas accidents [6]. Therefore,
develop a fault diagnosis system for the control valve that
holds signicant application value.
Currently, the model-based and data-driven methods for
control valve fault diagnosis have become dominant [7]. The
model-based methods detect features by establishing fault
mechanism models. Andrade et al [8] developed a nonlinear
autoregressive neural network model based on the decision
trees models for the fault diagnosis. Han et al [2] proposed a
canonical variate analysis model and utilized the square of the
residual Mahalanobis distance as a diagnosis index to improve
the model performance. However, many important parameters
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... There is a fascinating habit among dung beetles in which they form dung balls and then roll them to the ideal position. For the dung ball to roll straight, the dung beetle relies on celestial cues [21]. The dung beetles update their position as they roll the ball. ...
... The absolute difference between dimensions X r2 and X r3 forces uniform movement, reducing population diversity and hindering search agent evolution. ( 1) 1, 2, 3 t r r r X t X X X r r r i (21) where 1 r , 2 r , 3 r is a randomly selected index. Figure 6. ...
... Update the new position according to formulas (20) and (21) to generate a new solution. The core of the proposed IDBO-SVM algorithm is to utilize the global search ability and classification performance of the IDBO algorithm to complete accurate fault classification. ...
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