Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.publishedVersio

Online Monitoring System in Wax Formation in the Pipeline

The unwanted solidifying process of crude oil in pipeline is one of the major problems in the oil industry. Waxes, the heaviest saturated paraffin, tend to precipitate during the transportation flow line from the offshore to onshore. Electrical Capacitance Tomography (ECT), a form of Non-Destructive Test (NDT), is a new technique to gain information on the distribution of the contents in closed pipes. The variations on the dielectric properties of the material inside the pipeline are measurable to determine the condition of pipeline. ECT is the most advance monitoring system which inclusive of imaging data other than the numerical data. ECT is selected over other tomography modalities due to its advantages over others. The advantages of ECT can be briefly summarized, namely, it produce fast imaging speed, it release zero radiation, robust, low in cost, non-intrusive and non-invasive, and it can withstands to high pressure and temperature. These advantages suit with the aim to monitor the formation of wax in crude oil pipeline

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

Combustion monitoring for biomass boilers using multivariate image analysis

Les procédés de combustion sont utilisés dans la plupart des industries chimiques, métallurgiques et manufacturières, pour produire de la vapeur (chaudières), pour sécher des solides ou les transformer dans des fours rotatifs (ou autres). Or, les combustibles fossiles qui les alimentent (ex. : gaz naturel) sont de plus en plus dispendieux, ce qui incite plusieurs compagnies à utiliser d’autres sources de combustibles tels que de la biomasse, des rejets inflammables produits par le procédé lui-même ou des combustibles fossiles de moindre qualité. Ces alternatives sont moins coûteuses, mais de composition, et donc de pouvoir calorifique, plus variable. De telles variations dans la chaleur dégagée par la combustion perturbent l’opération des procédés et la qualité des produits qui dépendent de ces installations. De nouvelles stratégies de contrôle de la combustion doivent donc être élaborées afin de tenir compte de cette nouvelle réalité. Il a été récemment démontré que l’énergie dégagée par la combustion est corrélée à l’aspect visuel de la flamme, principalement sa couleur, ce qui permet d’en quantifier les variations par imagerie numérique. L’objectif de ce projet industriel consiste à faire la démonstration que l’analyse d’images multivariées peut servir à l’identification du comportement d’une chaudière à biomasse. La chaudière à biomasse opérée par Irving Pulp & Paper Ltd (Saint-John, Nouveau-Brunswick) fera office d’exemple. Les résultats montrent qu’un modèle bâtit à partir des informations fournies par les images ainsi que les données de procédé donne de bonnes prédictions de la quantité de vapeur produite (R2modèle=93.6%, R2validation=70.1%) et ce, 2,5 minutes à l’avance. Ce projet est la première étape du développement d’une nouvelle stratégie de contrôle automatique de la combustion de biomasse, capable de stabiliser l’énergie dégagée, malgré les variations imprévisibles dans le pouvoir calorifique et les proportions des combustibles utilisés provenant de différentes sources.Biomass is increasingly used in the process industry, particularly in utility boilers, as a low cost source of renewable, carbon neutral energy. It is, however, a solid fuel with some degree of moisture which feed rate and heat of combustion is often highly variable and difficult to control. Indeed, the variable bark properties such as its carbon content or its moisture content have an influence on heat released. Moreover, the uncertain and unsteady bark flow rate increases the level of difficulty for predicting heat released. The traditional 3-element boiler control strategy normally used needs to be improved to make sure the resulting heat released remains as steady as possible, thus leading to a more widespread use biomass as a combustible. It has been shown in the past that the flame digital images can be used to estimate the heat released by combustion processes. Therefore, this work investigates the use of Multivariate Image Analysis (MIA) of biomass combustion images for early detection of combustion disturbances. Applied to a bark boiler operated by Irving Pulp & Paper Ltd, it was shown to provide good predictions, 2.5 minutes in advance, of variations in steam flow rate (R2fit=93.6%, R2val=70.1%) when information extracted from images were combined with relevant process data. This project is the first step in the development of a new automatic control scheme for biomass boilers, which would have the ability to take proactive control actions before such disturbances in the manipulated variable (i.e. bark flow and bark properties) could affect steam production and steam header pressure

Novel Approaches for Nondestructive Testing and Evaluation

Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively

Multi-layer carbon fiber reinforced plastic characterization and reconstruction using eddy current pulsed thermography

Ph. D. Thesis.Carbon fibre composite materials are widely used in high-value, high-profit applications, such as aerospace manufacturing and shipbuilding – due to their low density, high mechanical strength, and flexibility. Existing NDT techniques such as eddy current testing suffers from electrical anisotropy in CFRP (carbon fibre reinforced plastics). Ultrasonic is limited by substantial attenuation of signal caused by the multilayer structure. The eddy current pulsed thermography has previously been applied for composites NDE (non-destructive evaluation)such as impact damage, which has the ability for quick and accurate QNDE(quantitative non-destructive evaluation) inspection but can be challenging for detection and evaluation of sub-surface defects, e.g., delamination and debonding in multiple layer structures. Developing QNDE solutions using eddy current thermography for addressing subsurface defects evaluation in multi-layer and anisotropic CFRP is urgently required. This thesis proposes the application of eddy current pulsed thermography (ECPT) and ECPuCT (eddy current pulse compression thermography) for tackling the challenges of anisotropic properties and the multi-layer structure of CFRP using feature-based and reconstruction-based QNDE and material characterisation. The major merit for eddy current heating CFRP is the volumetric heating nature enabling subsurface defect detectability. Therefore, the thesis proposes the investigation of different ECPT and their features for QNDE of various defects, including delamination and debonding. Based on the proposed systems and QNDE approach, three case studies are implemented for delamination QNDE, debonding QNDE, conductivity estimation and orientation inverse reconstruction using the two different ECPT systems and features, e.g., a pulse compression approach to increase the capability of the current ECPT system, the feature-based QNDE approach for defect detection and quantification, and reconstruction-based approach for conductivity estimation and inversion. The three case studies include 1) investigation of delamination with different depths in terms of delamination location, and depth quantification using K-PCA, proposed temporal feature-crossing point feature and ECPuCT system; 2) investigation of debonding with different electrical and thermal properties in terms of non-uniform heating pattern removal and properties QNDE using PLS approaches, impulse response based feature