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Title: An investigation into the potential of advanced sensor technology to support the maintenance of pipeline distribution systems
Author: Umeadi, Boniface B. N.
ISNI:       0000 0004 2714 5437
Awarding Body: University of Greenwich
Current Institution: University of Greenwich
Date of Award: 2010
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The construction industry has been challenged by the UK Construction Foresight Panel to apply advanced information and communication technology to improve the performance, in terms of sustainability, of the existing built environment and infrastructure. Traditionally, built-environment maintenance is a capital-cost-driven activity that relies either upon the subjective assessment of a built environment and infrastructure condition (i.e. a stock condition survey) to identify maintenance needs, or upon a reactive response to a component failure. The effectiveness and efficiency of the stock condition survey process to support planned maintenance has previously been questioned and a more sustainable approach, based on an objective assessment of a built environment and infrastructure performance, has been suggested. Previous attempts to develop objective-based (though not performance–based) maintenance models have largely failed, due to the limitations of technology, the daunting task of managing large amounts of data, and the inability of mathematically based models to cope with the complexity of real-life situations. This thesis addresses this challenge by exploring the feasibility of a performance-based assessment methodology to determine the maintenance needs of a buried oil steel-pipeline system and the impact that any changes in condition may have on the performance and integrity of related components in the pipeline system. The thesis also contains an evaluation of the ability and effectiveness of piezoelectric elements in pipeline defect (crack) signature detection to predict changes in component performance with data sets derived experimentally using laboratory bench testing. Vibration sound-emission detection techniques performed on various oil steel-pipeline defects, using non-destructive testing methods, were validated using attenuation and waveform analysis. Defect size and progression (i.e. the pattern characteristics of the defect) were monitored, measured and identified through spectrum analysis of multiple emission signals in combination with a number of frequency bands. Two series of tests were undertaken to evaluate the ability of vibration sound emission characteristics to identify steel pipeline defects, including leakage. Test Series 1 established the frequency (waveforms) of the generation of the acoustic emission signal caused by normal fluid dynamics (water flow) through the experimental steel pipe and the resulting signal propagation characteristics. Test Series 2 detected and monitored changes in the signal characteristics for incipient defects: (a) small-nail damage, (b) medium-sized nail damage, (c) large-nail damage and (d) crack to leakage source [sealed holes as a simulated corrosion to total failure]; oil was the fluid medium. The defect sources and leakage signals were also studied, and compared with theoretical models. The results of the theoretical analysis and the laboratory experiments confirmed the ability of non-destructive testing, based on vibration sound emission techniques, to detect and distinguish between different failure modes. The ability to carry out a basic inspection, analysis and report of a pipeline using an integrated-sensor device offers many potential benefits. The use of an integrated-sensor device is expected to provide valuable pipeline management information. Specifically the ability to detect and locate mechanical damage at the incipient stage and provide an assessment of the overall pipeline operating condition, including changes in performance profile and prediction of an estimated time to failure, has been shown to be feasible as part of a pipeline maintenance and rehabilitation programme.
Supervisor: Jones, Keith Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TJ Mechanical engineering and machinery