BEST PRACTICE IN PIPELINE DEFECT ASSESSMENT
Defects in pipelines can be introduced during manufacturing (e.g. laminations), transportation (e.g. fatigue cracking), fabrication (e.g. weld defects) and installation (e.g. dents), and can occur both due to deterioration (e.g. corrosion) and due to external interference (e.g. gouges and dents). To ensure the integrity of the pipeline, operators must be able to both detect and assess the significance of pipeline defects. Furthermore, the importance of accurate engineering models, for which the model uncertainty has been quantified, is important when applying risk-based (structural reliability) methods to pipelines. The past 40 years has seen the development of 'fitnessfor-purpose' methods for assessing the significance of pipeline defects. However, there is no definitive guidance that draws together all of the assessment techniques, or assesses each method against the published test data, or recommends best practice in their application. This paper describes the findings of a recent literature review and defines the objectives of the next phase of a Joint Industry Project which is being sponsored by thirteen international oil and gas companies (BG Technology, BP, CSM, DNV, EMC, Gaz de France, Health and Safety Executive, MOL, PII, SNAM, Statoil, Toho Gas and Total Oil Marine). The deliverable will be a Pipeline Defect Assessment Manual, which will provide the best available techniques for the assessment of pipeline defects.
A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines
The defect assessment process in oil and gas pipelines consists of three stages: defect detection, defect dimension (i.e., defect depth and length) prediction, and defect severity level determination. In this paper, we propose an intelligent system approach for defect prediction in oil and gas pipelines. The proposed technique is based on the magnetic flux leakage (MFL) technology widely used in pipeline monitoring systems. In the first stage, the MFL signals are analyzed using the Wavelet transform technique to detect any metal-loss defect in the targeted pipeline. In case of defect existence, an adaptive neuro-fuzzy inference system is utilized to predict the defect depth. Depth-related features are first extracted from the MFL signals, and then used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. To further improve the accuracy of the defect depth, predicted by the proposed model, highly-discriminant features are then selected by using the weight-based support vector machine (SVM). Experimental work shows that the proposed technique yields promising results, compared with those achieved by some service providers.
The Analysis on Fire Smoke Exhausting Robot Vibration Based on Wavelet Transform
Belt relaxation, which affects a lot on operation reliability of the fire smoke exhausting robot, is hard to detect. In this paper, a new method for belt relaxation feature recognition based on continuous wavelet transform (CWT) is proposed, and a vibration model which simplified the robot as a 2-dof forced vibration system under harmonic excitation is established. The vibration acceleration signal has been collected using the IEPE sensor and data acquisition card, experimental results verified the accuracy of the vibration model, and weak impact signal caused by the belt relaxation was distinguished, that testify the practicability of the CWT method in belt relaxation feature recognition.