Petroleum Pipe Leakage Detection and Location Embeded in SCADA

2004 ◽  
Author(s):  
Likun Wang ◽  
Jian Li ◽  
Ke Peng ◽  
Shijiu Jin ◽  
Zhuang Li
Keyword(s):  
Wavelet Analysis ◽  
Data Exchange ◽  
Wavelet Packet ◽  
Economic Loss ◽  
Frequency Component ◽  
False Alarms ◽  
Leakage Detection ◽  
Wavelet Packet Analysis ◽  
Oil Pipeline ◽  
Scada System

With the increase of the age of the transport oil pipeline and the man-made destruction to pipeline, leaks are often found. The system for pipeline leakage detection and location must be established to find leakage and locate the leak positions to reduce serious environmental pollution and economic loss caused by leakage. The negative pressure wave method is an effective way to locate the leak position, because over 98 percent pipe leakage in China is paroxysmal. There is a SCADA (supervisory control and data acquisition) system to monitor operation for long transport petroleum pipe, but the function of leakage detection and location is not included in existing SCADA system in China. This paper used Dynamic Data Exchange (DDE) method to obtain pipe operation parameters such as pressure, flow rate, temperature, bump current, valve position and so on from the SCADA system. That takes full advantage of the abundant data collection function of the SCADA system to provide data for leakage detection and location. The wavelet packet analysis-based fault diagnosis method can directly use the change of parameters such as energy of frequency component to detect faults without system model. In the paper, a wavelet packet analysis-based characteristic extraction method is used to extract the characteristic information of leak pressure signals. The eigenvector indexes along with the parameters obtained from the SCADA system can be used to avoid false alarms. Wavelet analysis was used to locate leak positions accurately in this paper. Such a wavelet analysis-based leakage detection and location scheme embedded in the SCADA system has been successfully applied to a pipeline in PetroChina. Practical run demonstrated its well effect.

scholarly journals Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xianming Lang ◽  
Zhiyong Hu ◽  
Ping Li ◽  
Yan Li ◽  
Jiangtao Cao ◽  
...  
Keyword(s):  
Sound Velocity ◽  
Wavelet Packet ◽  
Ultrasonic Signal ◽  
Deep Belief Network ◽  
Independent Component ◽  
Fine Tuning ◽  
Local Optimum ◽  
Wavelet Packet Analysis ◽  
Belief Network ◽  
Oil Pipeline

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBNICR) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBNICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

scholarly journals UAV and Its Approach in Oil and Gas Pipeline Leakage Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Saheed Akande ◽  
Adedotun Adetunla ◽  
Tosin Olanrewaju ◽  
Adeyinka Adeoye
Keyword(s):  
Response Time ◽  
Gas Sensors ◽  
Oil And Gas ◽  
Detection System ◽  
Economic Loss ◽  
False Alarms ◽  
Leakage Detection ◽  
Response System ◽  
System A ◽  
Area Of Interest

The synergy of vibration and gas sensors with unmanned aerial vehicles for a low-response-time Leakage Detection System (LDS) is explored in this work. Several pipeline accidents have occurred, most of which were triggered by untimely detection of pipe leakages in systems conveying oil and gas in many developing countries. The consequences of this include human casualties, environmental degradation, economic loss, and loss of resources. To limit the damages caused by inevitable leakages, a low-time-response system for leakage detection is required. Response time derived from the LDS is compared to the typical response time obtained from an existing system to determine the efficiency of the developed system. A comparative analysis of the response time of the designed LDS and existing systems reveals that the designed LDS response time is 1146.7% faster and having a pictorial view of the localized area of interest would go a long way to preventing unnecessary mobilization for site visits and eradicating the costly effect of false alarms.

Study on Oil Pipeline Leakage Detection Based on Stress Wave Detection Technique and Wavelet Analysis

2013 ◽  
Vol 694-697 ◽  
pp. 1368-1371
Author(s):  
Jie Zhang ◽  
Xiao Dan Guan ◽  
Yan Sun ◽  
Xue Jie Wei
Keyword(s):  
Wavelet Analysis ◽  
Stress Wave ◽  
Detection System ◽  
Vibration Signal ◽  
Detection Technique ◽  
Vibration Sensor ◽  
Processing Unit ◽  
Leakage Detection ◽  
Oil Pipeline ◽  
Wave Detection

Based on stress wave detection technique and wavelet analysis, oil pipeline leakage detection system is designed. Through vibration sensor put on the pipeline, vibration signal of pipeline is collected. The signal is used to preliminary assessment with DSP processing unit within the system, and then the feature of threat events is extracted. The information is uploaded to the centre of security central station of system by GPRS signal. At this, signal wavelet analysis is done for leakage detection. Based on LabVIEW platform, on oil pipeline leakage monitoring interface, threat events is alarmed and shown in electronic map and the system is convenient for staff to handle with. With the test, recognized police rate of system is superior to 85%, rate of false alarm is 15% below. All knocking signal of pipeline is given right alarm information. The noise by people walk, car, wind and rain is effectively filtered.

Application of Wavelet Analysis to Fault Diagnosis of Mechanical Equipment

2012 ◽  
Vol 590 ◽  
pp. 325-328
Author(s):  
Shan Zhen Xu ◽  
Cheng Wang
Keyword(s):  
Wavelet Analysis ◽  
Simulation Analysis ◽  
Wavelet Packet ◽  
Gear Transmission ◽  
Good Time ◽  
Mechanical Equipment ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Gear Fault ◽  
Adaptive Ability

Aiming at the characteristics of mechanical gear transmission, taking automobile main reducer as research object, this paper analyzes the gear transmission of failure mechanism and failure characteristics. According to the good time frequency character and adaptive ability of wavelet analysis, it proposes gear fault information extraction method based on wavelet packet analysis and have carried out simulation analysis. The results show that the method of wavelet packet analysis can effectively detect mutations in the signal part and noise to achieve the diagnosis of mechanical system failures.

Real-time localization of micro-damage in concrete beams using DIC technology and wavelet packet analysis

2021 ◽  
pp. 104198
Author(s):  
Kaiyang Zhou ◽  
Dong Lei ◽  
Jintao He ◽  
Pei Zhang ◽  
Pengxiang Bai ◽  
...  
Keyword(s):  
Real Time ◽  
Concrete Beams ◽  
Wavelet Packet ◽  
Wavelet Packet Analysis ◽  
Damage In Concrete

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

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