Pipeline Health Monitoring to Optimise Plant Efficiency

This chapter presents technological innovations that support asset integrity management—a crucial activity for optimising plant efficiency. In ageing thermal and geothermal power plants, critical assets such as steam piping are subject to high pressures and temperatures that accelerate damage mechanisms. Traditionally, the critical locations of these assets undergo routine inspection which is both costly and time consuming and affects the plant reliability and energy availability. There is an increasing trend in the application of non-destructive testing (NDT) and information technologies to in-service monitoring of these assets. The aim of this chapter is to provide a comprehensive overview of the state-of-the-art monitoring technologies for steamlines, with a focus on high temperature ultrasonic guided wave techniques. The enabling technologies, which include high temperature sensors, diagnostic data analysis algorithms and their monitoring performances, are reviewed. These technological advancements enable inspection without interruption of plant operations, and provide diagnosis and prognosis data for condition-based maintenance, increasing plant safety and its operational efficiency

Monitoring of Critical Metallic Assets in Oil and Gas Industry Using Ultrasonic Guided Waves

This chapter presents advancements in structural health monitoring (SHM) using ultrasonic guided waves (UGW) technology for metallic structures to support their integrity and maintenance management. The focus is on pipelines and storage tanks, which are critical assets in the Oil and Gas industry, whose operational conditions can greatly accelerate damage mechanisms. Conventional routine inspections are both costly and time consuming and affect the plant reliability and availability. These operational and economic disadvantages have led to development of SHM systems which can be permanently installed on these critical structures to provide information about developing damage and optimise maintenance planning and ensure structural integrity. These technology advancements enable inspection without interruption to operations, and generate diagnosis and prognosis data for condition-based maintenance, hence increasing safety and operational efficiency. The fundamentals, architecture and development of such SHM systems for pipes and above ground storage tanks are described here

Use of Guided Wave Thickness Resonance for Monitoring Pipeline Wall Thinning Using an Internal PIG

Experimental data and modelling results for pipeline wall thinning confirm a classification of Guided Wave (GW) propagation and detected features based on signal amplitude. This interpretation leads to a decision on a follow up inspection based on High, Medium or Low priority. The severity of defects must be determined; achievable by examining the signal amplitude as a function of metal loss. Specifically, if resonance can be obtained at a particular frequency, the operator can identify the wall thickness loss through the reflected GW energy amplitude. Previous research presented in this chapter identified a suitable strategy to deploy this thickness resonance technique, starting from dispersion curves (DC) development, to the analysis of the thickness loss effect on the DC, and experiments that prove the effectiveness of the methodology

A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates

The demand for flexible large-area optoelectronic devices has been growing significantly during recent years. Roll-to-roll (R2R) printing facilitates the cost-efficient industrial production of different optoelectronic devices. Nonetheless, the performance of these devices is highly dependent on the printing quality and number of defects of R2R printed conductors. The image processing technique is an efficient nondestructive testing (NDT) methodology used to detect such defects. In this study, a computer vision-based assessment tool was utilized to visualize R2R printed silver conductors’ defects on flexible plastic substrates. A multistage defect detection technique was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed conductors with respect to the reference design. The method proved to be a very reliable approach that can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints

Mooring Integrity Management: Novel Approaches Towards In Situ Monitoring

The recent dramatic fluctuations in oil and gas prices are forcing operators to look at radically new ways of maintaining the integrity of their structures. Moreover, the life of old structures has to be extended. This includes the replacement of expensive periodic in-service inspections with cost-efficient structural health monitoring (SHM) with permanently installed sensors. Mooring chains for floating offshore installations, typically designed for a 25-year service life, are loaded in fatigue in a seawater environment. There is no industry consensus on failure mechanisms or even defect initiation that mooring chains may incur. Moorings are safety-critical areas, which by their nature are hazardous to inspect. Close visual inspection in the turret is usually too hazardous for divers, yet is not possible with remotely operated vehicles (ROVs), because of limited access. Conventional non-destructive techniques (NDTs) are used to carry out inspections of mooring chains in the turret of floating production storage and offloading (FPSO) units. Although successful at detecting and assessing the fatigue cracks, the hazardous nature of the operation calls for remote techniques that can be applied continuously to identify damage initiation and progress. Appropriate replacement plans must enhance current strategies by implementing real-time data retrofit

Quality Control of Metal Additive Manufacturing

Metal Additive Manufacturing (AM) is an emerging technology for rapid prototype manufacturing, and the structural integrity of printed structures is extremely important and should meet the specifications and high standards of the above industries. In several metal AM techniques, residual stresses and micro-cracks that occur during the manufacturing procedure can result in irreversible damage and structural failure of the object after its manufacturing. Thus effective quality control of AM is highly required. Most Non-Destructive Testing (NDT) techniques (X-Ray, Computed Tomography, Thermography) are ineffective in detecting residual stresses. Bulk, cost, and resolution are limitations of such technologies. These methods are time consuming both for data acquisition and data analysis and have not yet been successfully integrated into AM technology. However two sets of NDT techniques: Electromagnetic Acoustic Transducers (EMAT) and Eddy Current (EC) Testing, can be applied for residual stress detection for AM techniques. Therefore a crucial and novel extension system incorporation of big data collection from sensors of the both techniques and analysis through machine learning (ML) can estimate the likelihood of the AM techniques to introduce anomalies into the printed structures, which can be used as an on-line monitoring and detection system to control the quality of AM

Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring

This is the post-print version of the final paper published in Renewable Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.A review of current progress in Condition Monitoring (CM) of wind turbine gearboxes and generators is presented, as an input to the design of a new continuous CM system with automated warnings based on a combination of vibrational and Acoustic Emission (AE) analysis. For wind turbines, existing reportage on vibrational monitoring is restricted to a few case histories whilst data on AE is even scarcer. In contrast, this paper presents combined vibration and AE monitoring performed over a continuous period of 5 days on a wind turbine. The vibrational and AE signatures for a healthy wind turbine gearbox and generator were obtained as a function of wind speed and turbine power, for the full normal range of these operational variables. i.e. 5–25 m/s and 0–300 kW respectively. The signatures have been determined as a vital pre-requisite for the identification of abnormal signatures attributable to shaft and gearbox defects. Worst-case standard deviations have been calculated for the sensor data. These standard deviations determine the minimum defect signal that could be detected within the defined time interval without false alarms in an automated warning system.UK Northern Wind Innovation Program NWI

A new synthetic training environment system based on an ICT-approach for manual ultrasonic testing

Training to qualify as a manual ultrasonic inspector takes a long time and costs a considerable amount of money. We developed a virtual training environment using an innovative dead-reckoning optical sensor that yields translational position which offers additional information to operators and examiners alike. The training environment contains a library of test scenarios, shows surface coverage, measures the time of inspection, indicates detected defects and provides a performance score. Our test-bed trial results using a pool of Ultrasonic Test (UT) qualified and unqualified participants on two virtual training blocks that contain 2 flaws each reveal 100% detection and an accuracy of 5mm in locating defects in more than 50% of the measured defect locations