Intelligent leak level recognition of gas pipeline valve using wavelet packet energy and support vector machine model

Zhang, Haifeng; Li, ZhenLin; Ji, Zhongli; Bi, Zhiqiang

doi:10.1784/insi.2012.55.12.670

Cited by 11 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently the characteristics calculated for VILAES include the standard deviation, root mean square, variance, energy, spectrum peak, wavelet packet entropy, kurtosis, and so forth. Among them, the standard deviation and root mean square can best characterise the VILAES [5–9].…”

Section: Modelling Processmentioning

confidence: 99%

See 1 more Smart Citation

Mixed multiple‐variable modelling of acoustic emission signals for valve internal leakage detection

2021

IET Science Measure & Tech

View full text Add to dashboard Cite

Theoretical analysis and experimental data show that the acoustic emission (AE) technology can detect the internal leakage state of the valve online. One of its key technologies is to determine the relationship between the characteristic of valve internal leakage AE signal (VILAES) and leakage rate and various parameters. However, most of the currently established models are a single-variable model. In addition, the repeated and verification experiments are rarely introduced, and most experiments are conducted under a large leakage rate. To this end, the mixed multiple-variable model is built to describe the relationship between the characteristics of VILAES and leakage rates, pressures, valve calibres and flow coefficients for the same type of valve. In the experiments, two rounds of experiments were performed for the same valve; then, a round of experiments was carried out after moving the sensor, and two rounds of experiments were conducted for two valves with the same parameters. In modelling, the VILAES is filtered first, and then the characteristics of the filtered signal are calculated. The mixed multiple-variable model is established by the leastsquares support vector machine. The results show that the mixed multiple-variable model could realise online internal leakage for valves with different calibres and flow coefficients under different pressures.

show abstract

Section: Modelling Processmentioning

confidence: 99%

“…Aiming at the high dimension of characteristic, linear decision analysis and kernel principal component analysis were used to reduce the dimension of characteristic and the training time of modelling. The fitting degree of modelling was 0.997, and the average absolute proportion error was 0.045 [7].…”

Section: Introductionmentioning

confidence: 99%

Mixed multiple‐variable modelling of acoustic emission signals for valve internal leakage detection

2021

IET Science Measure & Tech

View full text Add to dashboard Cite

show abstract

“…In Ni et al (2014), the feature entropy has been introduced as the feature parameter and then forming into the feature vectors that has been put into the SVM optimized by the particle swarm optimization (PSO) for inspecting and positioning pipeline leakages. In Zhang et al (2013), after processing the pipeline signals by wavelet analysis, energy entropy has been extracted as the characteristic of signal, and then the feature vectors have been input into SVM for detecting leakage. In Wang and Wang (2015), EEMD and approximate entropy (AE) have been combined to extract the characteristics of pipeline leakage signal, and then the pipeline signals have been classified and identified by SVM.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

Zhou

Zhang

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Pipeline leakage is the main reason that affects normal operation of the pipeline. In this paper, a feature recognition method for pipeline acoustic signals based on vocational mode decomposition (VMD) and exponential entropy (EE) is investigated, which could extract the characteristics of pipeline signals and further accurately identify the pipeline acoustic signals under different working conditions. First, the VMD is used to decompose the collected acoustic signals into a number of mode components, during which process the optimal mode number (i.e., K-value) is determined by combining local characteristic scale decomposition (LCD) and correlation analysis methods. Then, the characteristic content of each mode component is analyzed with the help of the determined correlation coefficient (CC) threshold. If the correlation coefficient of a mode component is greater than the threshold, then the mode component is selected as the feature component. Subsequently, the EE values of the selected feature components are calculated to form the feature vectors corresponding to different kinds of pipeline signals. Finally, the feature vectors are input into support vector machine (SVM) to classify and recognize the different pipeline states. The experimental results demonstrate that the proposed method can identify the pipeline signals under different working conditions, and the recognition accuracy is up to [Formula: see text]. By analyzing and comparing with methods of EE-SVM, original data-SVM, VMD-singular spectrum entropy (SSE) and VMD-information entropy (IE), it is further verified that the proposed method is feasible and superior to the methods.

show abstract

“…In 2008, Riahi et al [9] used an artificial neural network system to differentiate between leakage and corrosion signals in AE testing of aboveground storage tank floors. Zhang et al [10] proposed a method to detect the leakage of the gas pipeline valve by using AE technique and SVM (Support Vector Machine) was applied to recognize the leak level of the valve accurately. And in the field of tool wear monitoring, Zhu et al [11], Varma and Baras [12], Zhang et al [13], and Chen et al [14] both used HMM (Hidden Markov Model) to recognize the different tool wear states.…”

Section: Introductionmentioning

confidence: 99%

Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector

Zhang

Zhu

et al. 2015

Shock and Vibration

View full text Add to dashboard Cite

Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods.

show abstract

Intelligent leak level recognition of gas pipeline valve using wavelet packet energy and support vector machine model

Cited by 11 publications

References 10 publications

Mixed multiple‐variable modelling of acoustic emission signals for valve internal leakage detection

Mixed multiple‐variable modelling of acoustic emission signals for valve internal leakage detection

Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector

Contact Info

Product

Resources

About