Pipeline Flaw Detection Research Articles

Time-Domain Output Data Identification Model for Pipeline Flaw Detection Using Blind Source Separation Technique Complexity Pursuit

Vital defect information present in the magnetic field data of oil and gas pipelines can be perceived by developing such non-parametric algorithms that can extract modal features and performs structural assessment directly from the recorded signal data. This paper discusses such output-only modal identification method Complexity Pursuit (CP) based on blind signal separation. An application to the pipeline flaw detection is presented and it is shown that the complexity pursuit algorithm blindly estimates the modal parameters from the measured magnetic field signals. Numerical simulations for multi-degree of freedom systems show that the method can precisely identify the structural parameters. Experiments are performed first in a controlled laboratory environment secondly in real world, on pipeline magnetic field data, recorded using high precision magnetic field sensors. The measured structural responses are given as input to the blind source separation model where the complexity pursuit algorithm blindly extracted the least complex signals from the observed mixtures that were guaranteed to be source signals. The output power spectral densities calculated from the estimated modal responses exhibit rich physical interpretation of the pipeline structures.

A PCA and ELM Based Adaptive Method for Channel Equalization in MFL Inspection

Magnetic flux leakage (MFL) as an efficient method for pipeline flaw detection plays important role in pipeline safety. This nondestructive test technique assesses the health of the buried pipeline. The signal is gathered by an array of hall-effect sensors disposed at the magnetic neutral plane of a pair of permanent magnet in the pipeline inspection gauge (PIG) clinging to the inner surface of the pipe wall. The magnetic flux measured by the sensors reflects the health condition of the pipe. The signal is influenced by not only the condition of the pipe, but also by the lift-off value of the sensors and various properties of electronic component. The consistency of the position of the sensors is almost never satisfied and each sensor measures differently. In this paper, a new scheme of channel equalization is proposed for MFL signal in order to correct sensor misalignments, which eventually improves accuracy of defect characterization. The algorithm proposed in this paper is adaptive to the effects of error on the disposition of the sensor due to manufacturing imperfections and movements of the sensors. The algorithm is tested by data acquired from an experimental pipeline. The results show the effectiveness of the proposed algorithm.

Development of Circuits for Excitation and Reception in Ultrasonic Transducers for Generation of Guided Waves in Hollow Cylinders for Fouling Detection

This paper presents the development of circuits that are used to provide excitation and reception in ultrasonic transducers for the generation of guided waves in hollow cylinders for fouling detection. Fouling is a problem that has been present in the chemical, petroleum, food, and pharmaceutical industries. It consists of the deposition of heavy organic materials and other dissolved solids or the suspension in fluid transport systems. The ultrasonic guided waves present specific characteristics and have demonstrated good results in the detection of flaws in pipelines. The circuits are presented, and experimental results are discussed.

FOULING DETECTION USING ULTRASONIC GUIDED WAVES AND PARAMETER ESTIMATION

Fouling detection is a relevant problem for chemical, oil, food and pharmaceutical industries. Fouling consists of the deposition of heavy organic materials and other dissolved solids or in suspension in fluid transport system. The ultrasonic guided waves present specific characteristics and have demonstrated good results in detection of flaws in pipelines. This work presents the results of the parameters estimation, based on the Gaussian pulses model, of a system that uses guided waves for fouling detection in pipelines. Initially are made simulations and the use of FFT (Fast Fourier Transformer) of the ultrasound signal is proposed to determine the present frequencies and to reduce the number of parameters estimated. With the obtained estimation model, it intends to observe the modifications of the parameters when the fouling occurs in the monitored pipeline.

Improved Ultrasonic Offshore Oil Pipeline Thickness Accurate Detection Using Hilbert-Huang Transform and Elman Neural Network

Pipeline flaw detection and safety evaluation are very important because of internal corrosion usually caused by the presence of the water (salty or not), and external damage by anchors or other equipment. Any possibility of leakage must be detected before leakage occurs and preventive action should be taken to avoid losses of oil and ecological disasters. The ultrasonic method is the most commonly used to detect material loss and/or cracking of the pipeline. The ultrasonic intelligent pig is used to detect the pipeline thickness, but the complicated offshore and pipeline environment, especially the variable sensor lift-off (distance between ultrasonic probe and pipeline wall under detection), reduces the accuracy of pipeline thickness measurement. The Hilbert-Huang transform was used to extract the signal features, then the Elman neural network applied to eliminate the effect of lift-off variation to improve the flaw detection accuracy. Experiments showed that the accuracy of detected time of flight between the transmitted pulse and echo from the pipeline wall as well as the thickness of the pipeline wall were clearly improved.

New instruments and accessories for radiographic monitoring of welded joints of gas pipelines

For gas pipeline flaw detection, the radiographic method of welded joint monitoring is employed extensively.