Application of an Improved ISODATA Method to Pipeline Leak Detection

Increasingly serious leak problem in pipeline transportation has not only affected the operation of pipelines but also caused loss of precious resource and environmental damage. Based on the analysis of the occurrence of negative pressure waves and the unsupervised learning of pattern recognition, the Interactive Self-organizing Data Analysis Technique Algorithm （ISODATA） method was used to classify the negative pressure waves and then the states of pipelines could be determined. K L transformation was used to eliminate the correlativity of feature parameters and to reduce the dimensionality of feature vector space to speed up calculation. Experimental results validated the accuracy and practical value of this method.

Leak Detection of Gas Pipelines Based on Characteristics of Acoustic Leakage and Interfering Signals

When acoustic method is used in leak detection for natural gas pipelines,the external interferences including operation of compressor and valve,pipeline knocking,etc.,should be distinguished with acoustic leakage signals to improve the accuracy and reduce false alarms.In this paper,the technologies of extracting characteristics of acoustic signals were summarized.The acoustic leakage signals and interfering signals were measured by experiments and the characteristics of time-domain,frequency-domain and time-frequency domain were extracted.The main characteristics of time-domain are mean value,root mean square value,kurtosis,skewness and correlation function,etc.The features in frequency domain were obtained by frequency spectrum analysis and power spectrum density,while time-frequency analysis was accomplished by short time Fourier transform.The results show that the external interferences can be removed effectively by the characteristics of time domain,frequency domain and time-frequency domain.It can be drawn that the acoustic leak detection method can be applied to natural gas pipelines and the characteristics can help reduce false alarms and missing alarms.

Demand-Driven Memory Leak Detection Based on Flow-and Context-Sensitive Pointer Analysis

We present a demand-driven approach to memory leak detection algorithm based on flow- and context-sensitive pointer analysis. The detection algorithm firstly assumes the presence of a memory leak at some program point and then runs a backward analysis to see if this assumption can be disproved. Our algorithm computes the memory abstraction of programs based on points-to graph resulting from flow- and context-sensitive pointer analysis. We have implemented the algorithm in the SUIF2 compiler infrastructure and used the implementation to analyze a set of C benchmark programs. The experimental results show that the approach has better precision with satisfied scalability as expected.

Detection and analysis of gas tightness of continuous annealing furnaces

When a cold rolled strip is being treated in a continuous annealing furnace which is full of protective gas, the gas tightness of the furnace body, the connected facilities and the gas channels become an important indicator that directly affects the product＇s surface quality and shows the technical level of the design, the manufacture and the installation. By considering the problems of the gas tightness of a vertical annealing furnace in the installation and maintenance, this thesis evaluates the gas tightness indicator and gas tightness related level of the furnace body and the circulation duct, while studying and analyzing the technologies of negative-pressure leak detection and sealing.

Development and Application of Methane Leakage Monitoring System for Gas Transmission Pipeline

Oil and gas pipeline transportation,as a relatively safe way of oil and gas transportation,undertakes most of the transportation tasks of crude oil and natural gas.Oil and gas pipeline accidents affect a wide range of consequences.Therefore,the oil and gas pipeline leakage detection is paid more and more attention.In this paper,ultra-low power methane gas sensor is selected to collect methane gas concentration in the air,and wireless network technology is used to build a wireless network sensor system with 4G function.Through the sensor distribution along the pipeline,it can intuitively and accurately judge whether there is a micro-leakage in the pipeline,and understand the diffusion situation after the leakage.The sensor system has high reliability and stability,and has high value of popularization and application.

In-Line Inspection of Pipeline Defects Detection Using Ring-Type Laser

Pipeline plays a vital role in transporting fluids like oils, water, and petrochemical substances for longer distances. Based on the materials they carry, prolonged usage may cause the initiation of defects in the pipeline. These defects occur due to the formed salt deposits, chemical reaction happens between the inner surface and the transferring substance, prevailing environmental conditions, etc. These defects, if not identified earlier may lead to significant losses to the industry. In this work, an in-line inspection system utilizes the nondestructive way for analyzing the internal defects in the petrochemical pipeline. This system consists of a pipeline inspection robot having two major units namely the visual inspection unit and the power carrier unit. The visual inspection unit makes use of a ring-type laser diode and the camera. The laser diode serves as a light source for capturing good quality images of inspection. This unit is controlled by the Arduino in the power carrier unit which provides the necessary movement throughout the pipe. The inspected images captured by the camera are further processed with the aid of NI vision assistant software. After applying the processing function parameters provided by this software, the defect location can be clearly visualized with high precision. Three sets of defects are introduced in a Polylactide (PLA) pipe based on its position and angle along the circumference of the pipe. Further, this robot system serves as a real-time interactive image synchronization system for acquiring the inspected images. By comparing the actual and calculated defect size, the error percentage obtained was less than 5%.

ANALYSIS OF FIRST TRANSIENT PRESSURE OSCILLATION FOR LEAKDETECTION IN A SINGLE PIPELINE

The leak detection is of great importance in the reliable operation and management of a pipeline system. Recently, attention is shifted to the use of the time domain or frequency domain methods based on the transient analysis. These methods sometimes require accurate pressure signals obtained during the transient period or by creating ideal conditions in testing. This paper proposes a method that does not require transient simulations over the whole or an extended period of time, but uses the first transient pressure oscillation to detect leaks. The method considers the propagation of the pressure oscillation wave created from a fast valve closure and the reflected damp wave from the leak. A leak in the pipe gives rise to reflected waves which in turn create discontinuities in the observed signal at the measurement section. The timing of the reflected damp wave and the magnitude represent the location and the size of the leak, respectively. An analytical expression is derived based on the Method Of Characteristic （MOC） for the relationship between the leakage and the reflected magnitude. The leak detection procedure based on the method is also given. Then the reliability of the method is tested on numerically simulated pressure signals and experimental pressure signals with calibrated leak parameters, and the results indicate a successful application and the promising features of the method.

Application of Golay codes to distributed optical fiber sensor for long-distance oil pipeline leakage and external damage detection

A new distributed optical fiber sensor system for long-distance oil pipeline leakage and external damage detection is presented. A smart and sensitive optical fiber cable is buried beneath the soil running along the oil pipeline, which is sensitive to soakage of oil products and mechanical deformation and vibration caused by leaking, tampering, and mechanical impacting. The region of additional attenuation can be located based on the optical time domain reflectometry （OTDR）, and the types of external disturbances can be identified according to the characteristics of transmitted optical power. The Golay codes are utilized to improve the range-resolution performance of the OTDR sub-system and offer a method to characterize the transmitted optical power in a wide range of frequency spectrum. Theoretic analysis and simulation experiment have shown that the application of Golay codes can overcome the shortcomings of the prototype based on the conventional single-pulse OTDR.

DISTRIBUTED OPTICAL FIBER SENSOR FOR LONG-DISTANCE OIL PIPELINE HEALTH

A fully distributed optical fiber sensor （DOFS） for monitoring long-distance oil pipeline health is proposed based on optical time domain reflectometry （OTDR）. A smart and sensitive optical fiber cable is installed along the pipeline acting as a sensor, The experiments show that the cable swells when exposed to oil and induced additional bending losses inside the fiber, and the optical attenuation of the fiber coated by a thin skin with periodical hardness is sensitive to deformation and vibration caused by oil leakage, tampering, or mechanical impact. The region where the additional attenuation occurred is detected and located by DOFS based on OTDR, the types of pipeline accidents are identified according to the characteristics of transmitted optical power received by an optical power meter, Another prototype of DOFS based on a forward traveling frequency-modulated continuous-wave （FMCW） is also proposed to monitor pipeline. The advantages and disadvantages of DOFSs based on OTDR and FMCW are discussed. The experiments show that DOFSs are capable of detecting and locating distant oil pipeline leakages and damages in real time with an estimated precision of ten meters over tens of kilometers.

Large scale gas leakage monitoring with tunable diode laser absorption spectroscopy

Tunable diode laser absorption spectroscopy （TDLAS） has been widely employed in atmospheric trace gases detecting and industrial control due to its high sensitivity, selectivity, and rapidity of response. An open path TDLAS system is developed for monitoring large scale methane leakage around the oil refinery. The tunable distributed feedback （DFB） diode laser emits at 1.65 pro. In order to enhance the sensitivity, a system combining long open path and second harmonic detection technique is developed. The test results show that the time resolution is less than 0.1 second and the detection limit is lower than 3.6 ppmv. This system is adapted for monitoring a large scale methane concentration changing trend instead of measuring its absolute concentration.

Real-Time Pressure Based Diagnosis Method for Oil Pipeline Leakage

As detecting the pressure signal is the main method in the real-time leak diagnosis of long pipeline,an abnormal pressure diagnosis method is proposed to make the leak diagnosis rapidly and accurately. Firstly,a combination filter algorithm is designed to realize noise reduction. Then, an anomaly detection algorithm is designed to detect abnormal pressure on the head and tail of the pipeline. Finally, the relevancy of the detected novelties is computed by Pearson correlation coefficient to identify the leakages. The experimental results show that the proposed method can rapidly detect the leakage with few false alarms and accurately locate the position of the leakage.