Research on Signal Feature Extraction of Natural Gas Pipeline Ball Valve Based on the NWTD-WP Algorithm

Liu, Yang; Li, Shuxun; Wang, Zhihui; Hou, Jianjun; Zhang, Xuedong

doi:10.3390/s23104790

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…As can be seen from figure 9, the predicted values of the Stacking ensemble model are closer to the true values and the prediction is better than each single model. Long-term gas transmission work, unstandardized operation, natural disasters and so on will lead to natural gas pipeline trunk line leakage or explosion and other dangerous situations, the need to perform fire operation on the pipeline When the ball valve leakage in the form of unilateral leakage, the ball valve can still play the role of truncation, can be avoided to empty the ignition point of the natural gas between the two adjacent pipeline segments, reduce economic losses [51]. Therefore, accurate detection of unilateral and bilateral internal leakage of ball valves is very important.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

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Research on internal leakage detection of the ball valves based on stacking ensemble learning

Shi,

Deng,

Yang

et al. 2024

Meas. Sci. Technol.

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Natural gas is an important clean energy source that is mainly transported through pipelines. The ball valve is a crucial piece of control equipment for the pipeline transportation system for natural gas, and the failure of internal leakage of the ball valve will seriously affect the natural gas transmission and increase the risk of sudden safety accidents. In response to the problems of the limitations of a single machine learning model in the traditional ball valve internal leakage rate prediction methods and failure to qualitatively analyze unilateral and bilateral internal leakage recognition of ball valve, a study of ball valve internal leakage detection based on Stacking ensemble learning is proposed. A total of 15 time and frequency domain feature parameters were obtained by feature extraction of 125 and 96 sets of raw acoustic emission signals from the ball valve; the parameters of a single machine learning model were adjusted by Bayesian optimization grid search. An internal leakage rate prediction model and an internal leakage recognition model are constructed, and the proposed model is compared and analyzed with a single model through a field ball valve internal leakage test. The results indicate that the Stacking ensemble learning model outperforms each single machine learning model in terms of SMAPE (17.2583), RMSE (1.1009), and MAE (0.9375) for internal leakage rate prediction. The Stacking ensemble learning model outperformed the single machine learning model in terms of accuracy (1.0000), recall (1.0000), precision (1.0000), FAR(0), and F1-score (1.0000) for internal leakage recognition. Stacking ensemble learning significantly enhances the model’s ability to detect internal ball valve leaks.

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Section: Analysis Of Resultsmentioning

confidence: 99%

“…The internal leakage of ball valves is usually determined by regular shutdown and disassembly inspection, which may bring secondary damage [50]. Therefore, research on accurate and efficient nondestructive detection methods for internal leakage is needed [51].…”

Section: Internal Leakage Rate Prediction Model 411 Stacking Ensemble...mentioning

confidence: 99%

Research on internal leakage detection of the ball valves based on stacking ensemble learning

Shi,

Deng,

Yang

et al. 2024

Meas. Sci. Technol.

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“…Selection of threshold: It can be understood from the characteristics of the noise distribution that under the first decomposition scale, the wavelet decomposition coefficients have the highest noise content, which rapidly decays as the wavelet threshold decomposition levels increase. Considering the correction of noise variance on the threshold size, the fixed threshold is adjusted according to the formula as shown in Equation (18). Finally, the calculation formula for the fixed threshold is shown in Equation (19).…”

Section: Selection Of Denoising Model Basic Parametersmentioning

confidence: 99%

“…The simulation results indicated that the proposed method has good denoising effects. Yang et al [ 18 ] presented a denoising model that combines a dual-parameter threshold quantization function with wavelet packet algorithms. The experimental validation demonstrated the feasibility and superiority of this method for denoising pipeline valve leakage signals.…”

Section: Introductionmentioning

confidence: 99%

A New Denoising Method for Belt Conveyor Roller Fault Signals

Hao,

Zhang,

Gao

et al. 2024

Sensors

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In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method’s denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

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Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

Qin,

Yang,

2024

Structural Health Monitoring

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An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficients by extending the definition of the Shannon information entropy, and the non-convex sparse regularization function is minimized by synergistic swarm optimization algorithm. Then, the wavelet node coefficients are weighted according to the second-order cyclostationarity index of the wavelet packet node to further enhance the sparsity of the reconstructed signal. Moreover, the optimal decomposition level of GSWPT is adaptively selected by the maximum sparsity and cyclostationarity criterion. Particularly, in order to verify the bearing fault detection performance of GSWPT in practical engineering, a bearing fault dynamic model of traction motor in high-speed train was established based on Hertz contact theory and the fourth-order Runge-Kutta method to obtain simulated data under strong Gaussian white noise, and a corresponding test platform was constructed to collect experimental data under different operating conditions. Finally, the applications on the simulated and experimental signals of traction motor bearings in high-speed trains demonstrate that GSWPT significantly outperforms the conventional wavelet packet transform, dual-tree complex wavelet packet transform, blind deconvolution, modal decomposition, and Infogram methods to some extent for fault detection.

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Research on Signal Feature Extraction of Natural Gas Pipeline Ball Valve Based on the NWTD-WP Algorithm

Cited by 3 publications

References 28 publications

Research on internal leakage detection of the ball valves based on stacking ensemble learning

Research on internal leakage detection of the ball valves based on stacking ensemble learning

A New Denoising Method for Belt Conveyor Roller Fault Signals

Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

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