Deep belief network-based internal valve leakage rate prediction approach

Zhu, Shen-Bin; Li, Zhenlin; Zhang, Shimin; Yingyu,; Zhang, Haifeng

doi:10.1016/j.measurement.2018.10.020

Cited by 46 publications

(12 citation statements)

References 23 publications

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“…Methods Error [11] Equation fitting Between 9.2% and 29.7% [12,13] Power spectral density Approximately ±7.8% full scale [14] Spectrum of frequencies Not mentioned [15] Neural-network-based correlation model ≤1% [16] Nonlinear regression method, kernel partial least-squares regression ≤27% [17] Least-squares method Not given [18] K-medoids clustering Overall accuracy 96.28% [19] Regression-based deep belief network ≤20% [20] Regression method Accuracy between 74.5% and 98.8% [21] Gaussian process regression Not given…”

Section: Referencesmentioning

confidence: 99%

“…In [18], a model based on k-medoids clustering was used to recognize internal valve leakage rates. In [19], a regression-based deep belief network (DBN) was proposed to predict the internal leakage rates of a valve in a natural gas pipeline. In [20], The k-nearest neighbors and support vector machine classification algorithms were employed to classify the valve conditions before the estimation of the valve flow rate through the regression model.…”

Section: Introductionmentioning

confidence: 99%

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Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

Zhao

Zhu

et al. 2020

Applied Sciences

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Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves.

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Section: Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

Zhao

Zhu

et al. 2020

Applied Sciences

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“…The deep belief network (DBN) is an important model in deep learning. It not only has the advantages of traditional neural networks but also has a strong ability for information fusion [18]. Wang et al [19] studied a deep wind speed prediction framework and an intelligent approach based on a DBN, which can improve the precision and efficiency of wind speed prediction.…”

Section: Introductionmentioning

confidence: 99%

Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

et al. 2021

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Satellite altimetry and tide gauges are the two main techniques used to measure sea level. Due to the limitations of satellite altimetry, a high-quality unified sea level model from coast to open ocean has traditionally been difficult to achieve. This study proposes a fusion approach of altimetry and tide gauge data based on a deep belief network (DBN) method. Taking the Mediterranean Sea as the case study area, a progressive three-step experiment was designed to compare the fused sea level anomalies from the DBN method with those from the inverse distance weighted (IDW) method, the kriging (KRG) method and the curvature continuous splines in tension (CCS) method for different cases. The results show that the fusion precision varies with the methods and the input measurements. The precision of the DBN method is better than that of the other three methods in most schemes and is reduced by approximately 20% when the limited altimetry along-track data and in-situ tide gauge data are used. In addition, the distribution of satellite altimetry data and tide gauge data has a large effect on the other three methods but less impact on the DBN model. Furthermore, the sea level anomalies in the Mediterranean Sea with a spatial resolution of 0.25° × 0.25° generated by the DBN model contain more spatial distribution information than others, which means the DBN can be applied as a more feasible and robust way to fuse these two kinds of sea levels.

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“…The diagnosis effect of data-driven method mainly depends on the quantity and quality of data and the conditions of collecting data, and it has low requirements for experience knowledge and fault mechanism. Therefore, it has been actively studied in the field of RC fault diagnosis, among which local mean decomposition [1,16], deep confidence network and back-propagation neural network [17][18][19], support vector machine (SVM) [9,20], k approximate regression [18,21], Bayesian estimation algorithm [10,22], big data [23], and other technologies have been successfully applied. The method of combining model and data-driven is to diagnose the system fault by fusing the system operation data with the system fault model.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

et al. 2020

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High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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Deep belief network-based internal valve leakage rate prediction approach

Cited by 46 publications

References 23 publications

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

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