Neuro-fuzzy approaches for pipeline condition assessment

Abstract Recent advances in electronics, transducers, ultrasonic and computing technologies, have led to the development of inspection systems for underground facilities such as water lines, sewer pipes, oil and gas pipelines. Recent inspection technologies have been developed that require no human entry into underground structures; they are now fully automated, from data acquisition to data analysis, and eventually to condition assessment, which can be used during the manufacturing as well as maintenance stage. This paper describes the development of an automated data interpretation system for pipeline, which can be used during the manufacturing stage to maintain the highest standard of quality control and it can also be extended to the maintenance stage. The proposed system is highly desirable and useful where a large number of similar samples are to be investigated which can be applied to investigate various defects in metals as well as composites. The interpretation system obtains Ultrasonic C-scan data obtained through an ultrasonic water immersion or air scan system. The proposed system utilizes Artificial Neural Networks (ANN), and Genetic Algorithm to recognize various types of defects in a given specimen. Image processing and Wavelets techniques are used to determine the details of the damage geometry. An Expert System for composite repair mechanism is also being developed using the adaptive neuro-fuzzy inference system (ANFIS), to perform damage condition assessment as well as material degradation evaluation. MATLAB is used in developing a real time automated prototype system. Keywords: Neural networkCondition assessmentInspectionImagingMatlab Acknowledgements The financial support of the Atlantic Innovation fund, as well as NSERC granted to the second author in support of this work is gratefully acknowledged.