High accuracy ultrasonic degradation monitoring

This thesis is concerned with maximising the precision of permanently installed ultrasonic time of flight sensors. Numerous sources of uncertainty affecting the measurement precision were considered and a measurement protocol was suggested to minimise variability. The repeatability that can be achieved with the described measurement protocol was verified in simulations and in laboratory corrosion experiments as well as various other experiments. One of the most significant and complex problems affecting the precision, inner wall surface roughness, was also investigated and a signal processing method was proposed to improve the accuracy of estimated wall thickness loss rates by an order of magnitude compared to standard methods. It was found that the error associated with temperature effects is the most significant among typical experimental sources of uncertainty (e.g. coherent noise and coupling stability). By implementing temperature compensation, it was shown in laboratory experiments that wall thickness can be estimated with a standard deviation of less than 20 nm when temperature is stable (within 0.1 C) using the signal processing protocol described in this thesis. In more realistic corrosion experiments, where temperature changes were of the order of 4 C), it was shown that a wall thickness loss of 1 micron can be detected reliably by applying the same measurement protocol. Another major issue affecting both accuracy and precision is changing inner wall surface morphology. Ultrasonic wave reflections from rough inner surfaces result in distorted signals. These distortions significantly affect the accuracy of wall thickness estimates. A new signal processing method, Adaptive Cross-Correlation (AXC), was described to mitigate the effects of such distortions. It was shown that AXC reduces measurement errors of wall thickness loss rates by an order of magnitude compared to standard signal processing methods so that mean wall loss can be accurately determined. When wall thickness loss is random and spatially uniform, 90% of wall thickness rates measured using AXC lie within 7.5 ± 18% of the actual slope. This means that with mean corrosion rates of 1 mm/year, the wall thickness estimate with AXC would be of the order of 0.75-1.1 mm/year. In addition, the feasibility of increasing the accuracy of wall thickness loss rate measurements even further was demonstrated using multiple sensors for measuring a single wall thickness loss rate. It was shown that measurement errors can be decreased to 30% of the variability of a single sensor. The main findings of this thesis have led to 1) a solid understanding of the numerous factors that affect accuracy and precision of wall thickness loss monitoring, 2) a robust signal acquisition protocol as well as 3) AXC, a post processing technique that improves the monitoring accuracy by an order of magnitude. This will benefit corrosion mitigation around the world, which is estimated to cost a developed nation in excess of 2-5% of its GDP. The presented techniques help to reduce response times to detect industrially actionable corrosion rates of 0.1 mm/year to a few days. They therefore help to minimise the risk of process fluid leakage and increase overall confidence in asset management.Open Acces

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

Context-Enabled Visualization Strategies for Automation Enabled Human-in-the-loop Inspection Systems to Enhance the Situation Awareness of Windstorm Risk Engineers

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. Traditionally, the risk inspection process is highly subjective and depends on the skills of the engineer performing it. This dissertation investigates the sensemaking process of risk engineers while performing risk inspection with special focus on various factors influencing it. This research then investigates how context-based visualizations strategies enhance the situation awareness and performance of windstorm risk engineers. An initial study investigated the sensemaking process and situation awareness requirements of the windstorm risk engineers. The data frame theory of sensemaking was used as the framework to carry out this study. Ten windstorm risk engineers were interviewed, and the data collected were analyzed following an inductive thematic approach. The themes emerged from the data explained the sensemaking process of risk engineers, the process of making sense of contradicting information, importance of their experience level, internal and external biases influencing the inspection process, difficulty developing mental models, and potential technology interventions. More recently human in the loop systems such as drones have been used to improve the efficiency of windstorm risk inspection. This study provides recommendations to guide the design of such systems to support the sensemaking process and situation awareness of windstorm visual risk inspection. The second study investigated the effect of context-based visualization strategies to enhance the situation awareness of the windstorm risk engineers. More specifically, the study investigated how different types of information contribute towards the three levels of situation awareness. Following a between subjects study design 65 civil/construction engineering students completed this study. A checklist based and predictive display based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of windstorm risk engineers. However, the predictive display only helped with certain tasks like understanding the interaction among different components on the rooftop. For remaining tasks, checklist alone was sufficient. Moreover, the decision aids did not place any additional cognitive demand on the participants. This study helped us understand the advantages and disadvantages of the decision aids tested. The final study evaluated the transfer of training effect of the checklist and predictive display based decision aids. After one week of the previous study, participants completed a follow-up study without any decision aids. The performance and situation awareness of participants in the checklist and predictive display group did not change significantly from first trial to second trial. However, the performance and situation awareness of participants in the control condition improved significantly in the second trial. They attributed this to their exposure to SAGAT questionnaire in the first study. They knew what issues to look for and what tasks need to be completed in the simulation. The confounding effect of SAGAT questionnaires needs to be studied in future research efforts

Research and Technology 1996: Innovation in Time and Space

As the NASA Center responsible for assembly, checkout, servicing, launch, recovery, and operational support of Space Transportation System elements and payloads, the John F. Kennedy Space Center is placing increasing emphasis on its advanced technology development program. This program encompasses the efforts of the Engineering Development Directorate laboratories, most of the KSC operations contractors, academia, and selected commercial industries - all working in a team effort within their own areas of expertise. This edition of the Kennedy Space Center Research and Technology 1996 Annual Report covers efforts of all these contributors to the KSC advanced technology development program, as well as our technology transfer activities

Index to 1985 NASA Tech Briefs, volume 10, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1985 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

NASA Tech Briefs, November 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms.

This cumulative thesis introduces a novel field for service robotics: the inspection of marine vessels using mobile inspection robots. In this thesis, three scientific contributions are provided and experimentally verified in the field of marine inspection, but are not limited to this type of application. The inspection scenario is merely a golden thread to combine the cumulative scientific results presented in this thesis. The first contribution is an adaptive, proprioceptive control approach for hybrid leg-wheel robots, such as the robot ASGUARD described in this thesis. The robot is able to deal with rough terrain and stairs, due to the control concept introduced in this thesis. The proposed system is a suitable platform to move inside the cargo holds of bulk carriers and to deliver visual data from inside the hold. Additionally, the proposed system also has stair climbing abilities, allowing the system to move between different decks. The robot adapts its gait pattern dynamically based on proprioceptive data received from the joint motors and based on the pitch and tilt angle of the robot's body during locomotion. The second major contribution of the thesis is an independent ship inspection system, consisting of a magnetic wall climbing robot for bulkhead inspection, a particle filter based localization method, and a spatial content management system (SCMS) for spatial inspection data representation and organization. The system described in this work was evaluated in several laboratory experiments and field trials on two different marine vessels in close collaboration with ship surveyors. The third scientific contribution of the thesis is a novel approach to structural classification using semantic perception approaches. By these methods, a structured environment can be semantically annotated, based on the spatial relationships between spatial entities and spatial features. This method was verified in the domain of indoor perception (logistics and household environment), for soil sample classification, and for the classification of the structural parts of a marine vessel. The proposed method allows the description of the structural parts of a cargo hold in order to localize the inspection robot or any detected damage. The algorithms proposed in this thesis are based on unorganized 3D point clouds, generated by a LIDAR within a ship's cargo hold. Two different semantic perception methods are proposed in this thesis. One approach is based on probabilistic constraint networks; the second approach is based on Fuzzy Description Logic and spatial reasoning using a spatial ontology about the environment