A fault diagnosis method for an electro-hydraulic directional valve based on intrinsic mode functions and weighted densely connected convolutional networks

Shi, Chong; Ren, Yan; Tang, Hesheng; Mupfukirei, Leaven Romeo

doi:10.1088/1361-6501/abfad2

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Axial piston pumps, which play a crucial role in hydraulic systems, unavoidably fail suddenly owing to the harsh working conditions, causing significant economic losses and even casualties [1][2][3][4][5][6]. Therefore, developing axial piston pump fault diagnosis techniques is necessary to maintain the performance of hydraulic systems [7,8].…”

Section: Introductionmentioning

confidence: 99%

FEM simulation-determined band pass filter method with continuously changed bandwidth for fault detection in axial piston pumps

Guo¹,

Si²,

Liu³

et al. 2022

Meas. Sci. Technol.

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The development of bearing fault detection methods is of great significance for the performance maintenance of axial piston pumps. However, the reciprocating movement induced strong natural periodic impulses that completely submerged the fault characteristic frequencies of the axial piston pump. To solve this problem, a finite element method (FEM)-based band-pass filter method was proposed, combined with minimum entropy deconvolution. However, the performance is determined by the selected band-pass filter bandwidth and the pre-treated denoise techniques. In the present study, an improved version of the FEM-based band-pass filter method was developed by continuously changing the bandwidth of the filter. First, the central frequency was determined using the FEM-based band-pass filter method. Second, the bandwidths of the constructed band-pass filters were continuously changed with a certain incremental step to obtain multiple filtered signals from the raw signals. Third, the normalized Hilbert envelope spectra were collected from the filtered signals. Finally, the projection figure is obtained by automatically taking the spectrum lines with maximum amplitudes in the normalized Hilbert envelope spectra, and the fault features are further determined. The experimental results show that the bearing faults in the axial piston pump can be successfully detected using the proposed method. Compared to the original FEM-based band-pass filter method, the improved version does not require bandwidth selection of the band-pass filter and pre-treated denoising method.

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Section: Introductionmentioning

confidence: 99%

FEM simulation-determined band pass filter method with continuously changed bandwidth for fault detection in axial piston pumps

Guo¹,

Si²,

Liu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Common signal decomposition methods include EMD, variational mode decomposition (VMD), and so on. For example, Shi et al [16] utilized EMD to obtain modal components and then employed the Relief method to select features to achieve accurate leakage fault recognition of valves. Shi et al [17] employed the VMD to process the vibration and pressure signals of the valve, then extracted time-frequency domain features.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Han

et al. 2024

Meas. Sci. Technol.

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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 generalized dispersion entropy. 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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“…Zhang et al [12] developed a graphical model capable of simultaneously detecting multiple faults while reducing dependence on statistical methods. Shi et al [13] proposed a method based on Intrinsic Mode Functions (IMF) and one-dimensional WDenseNet for diagnosing internal leakage faults in directional control valves. Conti et al [14] selected current, acoustic emission, and vibration signals as the most promising monitoring technique.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

Liu,

Zhao,

Zhang

2024

Actuators

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In the process industrial systems, flow control valves are deemed vital components that ensure the system’s safe operation. Hence, detecting faults in control valves is of significant importance. However, the stable operating conditions of flow control valves are prone to change, resulting in a decreased effectiveness of the conventional fault detection method. In this paper, an online fault detection approach considering the variable operating conditions of flow control valves is proposed. This approach is based on residual analysis, combining LightGBM online model with Seasonal and Trend decomposition using Loess (STL). LightGBM is a tree-based machine learning algorithm. In the proposed method, an online LightGBM is employed to establish and continuously update a flow prediction model for control valves, ensuring model accuracy during changes in operational conditions. Subsequently, STL decomposition is applied to the model’s residuals to capture the trend of residual changes, which is then transformed into a Health Index (HI) for evaluating the health level of the flow control valves. Finally, fault occurrences are detected based on the magnitude of the HI. We validate this approach using both simulated and real factory data. The experimental results demonstrate that the proposed method can promptly reflect the occurrence of faults through the HI.

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A fault diagnosis method for an electro-hydraulic directional valve based on intrinsic mode functions and weighted densely connected convolutional networks

Cited by 11 publications

References 33 publications

FEM simulation-determined band pass filter method with continuously changed bandwidth for fault detection in axial piston pumps

FEM simulation-determined band pass filter method with continuously changed bandwidth for fault detection in axial piston pumps

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

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

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