Dissertations / Theses on the topic 'Detecting defects'
To see the other types of publications on this topic, follow the link: Detecting defects.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Detecting defects.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Palmquist, Jonathan. "Detecting defects on cheese using hyperspectral image analysis." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172695.
Full textAbstract:
Defects such as mold and bacterial stains can appear on cheese. Manually detecting defects is a time-consuming, cost-ineffective, and ergonomically unsatisfactory process for a dairy because the quality technicians must inspect each cheese before packaging. Instead, dairies would prefer an automatic detection system, but it is unclear whether reliable options are available. One potential approach is hyperspectral image analysis, which can interpret and classify chemical information from a sample. We collected hyperspectral images from a dairy using a short-wave infrared (SWIR) camera and compared three prediction models: a PLS-discriminant analysis with the software Breeze from the analysis company Prediktera and two classifiers based on a convolutional neural network and a support vector machine, both coded in Python. We found mold and lactobacilli stains using all methods, but dirt was more challenging to detect. Also, the methods had issues with false positives of lactobacilli stains. To improve accuracy, we recommend collecting more data, especially samples with lactobacilli stains and dirt, and using more non-defect cheese for validation. To find smaller defects, we propose that future work should test a visible and near-infrared (VNIR) camera with higher resolution. Though PLS-discriminant analysis did not achieve the highest accuracy, it was not far off and had the most time-effective predictions. Since Breeze already integrates PLS-discriminant analysis, it should remain in focus, but for higher accuracy Prediktera should continue to explore other methods such as neural networks.
2
Baghalian, Amin. "Detecting Structural Defects Using Novel Smart Sensory and Sensor-less Approaches." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3560.
Full textAbstract:
Monitoring the mechanical integrity of critical structures is extremely important, as mechanical defects can potentially have adverse impacts on their safe operability throughout their service life. Structural defects can be detected by using active structural health monitoring (SHM) approaches, in which a given structure is excited with harmonic mechanical waves generated by actuators. The response of the structure is then collected using sensor(s) and is analyzed for possible defects, with various active SHM approaches available for analyzing the response of a structure to single- or multi-frequency harmonic excitations. In order to identify the appropriate excitation frequency, however, the majority of such methods require a priori knowledge of the characteristics of the defects under consideration. This makes the whole enterprise of detecting structural defects logically circular, as there is usually limited a priori information about the characteristics and the locations of defects that are yet to be detected. Furthermore, the majority of SHM techniques rely on sensors for response collection, with the very same sensors also prone to structural damage. The Surface Response to Excitation (SuRE) method is a broadband frequency method that has high sensitivity to different types of defects, but it requires a baseline. In this study, initially, theoretical justification was provided for the validity of the SuRE method and it was implemented for detection of internal and external defects in pipes. Then, the Comprehensive Heterodyne Effect Based Inspection (CHEBI) method was developed based on the SuRE method to eliminate the need for any baseline. Unlike traditional approaches, the CHEBI method requires no a priori knowledge of defect characteristics for the selection of the excitation frequency. In addition, the proposed heterodyne effect-based approach constitutes the very first sensor-less smart monitoring technique, in which the emergence of mechanical defect(s) triggers an audible alarm in the structure with the defect. Finally, a novel compact phased array (CPA) method was developed for locating defects using only three transducers. The CPA approach provides an image of most probable defected areas in the structure in three steps. The techniques developed in this study were used to detect and/or locate different types of mechanical damages in structures with various geometries.
3
Storozhenko, V. A., A. V. Myagkiy, and R. P. Orel. "Filtering of interference of inhomogeneous regular structure in thermal non-destructive control of cellular structures." Thesis, Eskisehir technical university, 2021. https://openarchive.nure.ua/handle/document/18954.
Full textAbstract:
Honeycomb constructions are the most widely used materials in contemporary aviation and space technology. They are the basis for the housings of practically all products of this sector, where reliability of all parts should meet the in-creased requirements. Special attention is paid to the quality of composite materials and to the absence of defects such as the places of adhesion failure (exfoliation) between the skin and the honeycomb filler. Therefore, increase in the efficiency and reliability of thermal flaw detection, based on in-depth analysis of the processes of detecting defects and development of the principles of optimization of both the procedure of control and subsequent processing of the obtained information, is an important and relevant task.
4
Rainer, Alexander. "Detecting critical defects : towards standards for conducting NDE on cast iron trunk mains." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/844891/.
Full textAbstract:
Every day, water networks across the developed world are relied on by billions of people to provide them with a fresh supply of water. Many of these networks are comprised of pipes made from grey cast iron and may have been in service for up to 150 years. Despite their age, some parts of these networks continue to operate with little degradation, whereas in other areas they degrade rapidly: more recently laid pipes are being outlived by their forerunners. In such networks, it is the trunk mains (pipes between 12-60” [300 mm to 1500 mm] in diameter) that are of great concern, since they pose the greatest risk of failure and are already bursting more frequently. Accurate NDE is required to enable the mains in poor health with the highest risk of failure to be identified and replaced before they burst. A review of the published literature has shown that whilst there are many NDE techniques to choose from, many are not practical for application to the mains. The review process also highlighted the kinds of defects present in grey cast iron and an initial stress analysis using strength models and material data published in the literature has suggested defect sizes approaching 5 mm must be able to be detected to prevent catastrophic pipe failure. Ultrasonic inspection has been investigated and shown to work effectively on uncorroded cast iron. Speed of sound values between 4100 – 4600 m s-1 have been observed across several pipes. A speed of sound of 2950 ± 80 m s-1 has been measured for graphitic corrosion, however, inspection on corroded main has not been possible. A complementary magnetic technique, with the potential to scan pipe rapidly in order to identify mains in need of further investigation, as well as providing supplementary condition data, has been trialled and shown to detect corrosion layers up to 6 mm thick. A methodology using a 3D scanner to accurately determine the “ground truth” pipe condition has been developed. This methodology proved to be successful and provided corrosion measurements that were in-keeping with those obtained through standard pit depth measurements. Further, the data showed that traditional pit depth measurements do not always find the deepest external corrosion pits, particularly where the surrounding geometry is complicated. This methodology was used in a live comparison exercise of two, commercially available techniques. This comparison highlighted problems with the surface preparation required by some techniques, which can be quite damaging, and with some proprietary post-processing algorithms – the raw data can be more useful. From this assessment process, it has been possible to specify very detailed schedule for the testing of new NDE techniques in the future.
5
Poudel, Anish. "AN INTELLIGENT SYSTEMS APPROACH FOR DETECTING DEFECTS IN AIRCRAFT COMPOSITES BY USING AIR-COUPLED ULTRASONIC TESTING." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/594.
Full textAbstract:
Circular air-coupled ultrasonic testing (ACUT) setup for the inspection of commercial carbon-carbon composite aircraft brake disks was developed in Intelligent Measurement and Evaluation Laboratory (IMEL) at Southern Illinois University Carbondale (SIUC). The developed test setup utilizes Airstar single channel air-coupled equipment and has only manual A-scan and B-scan capability. The developed ACUT technique is unique compared to the commercial C-scan ultrasonic systems and is proficient, fast, economically feasible, and easy to implement method particularly for the inspection of carbon-carbon (C/C) composites aircraft brake disks. Prior to conducting air-coupled measurements, wobble analysis was carried out. This was important because significant wobbling in the test setup can lead to the interference of the reflected and the incident beam which would result to inaccurate ultrasonic measurements. The measured deviation due to wobbling, surface profile of the disk, design, and experimental error were relatively small. Therefore, these errors were neglected while performing ACUT measurements. For ACUT measurements, several through-transmitted amplitude signals were recorded within the C/C brake disks manually. The images were then reconstructed using Matlab based on the through-transmitted amplitude signals. Finally, a comparison was drawn between the reconstructed images and the C-scan images of the C/C brake disks obtained from the commercial Airstar C-scan ACUT system. Like commercial C-scan ACUT image results, reconstructed images were also able to detect all defects in the commercial C/C brake disks which served for the system verification and validation. In addition, defect, non-defect, and suspected areas within the C/C brake disks were quantified with air-coupled measurement. For this, light microscopy was conducted for every sample made from each C/C brake disks at lower magnification of 10X. It was concluded that it is very difficult to assess the crack or delamination situation based on a 2D micrograph of one depth. Also, it was concluded that an internal porosity and micro-cracks may not be only factors that can be related to defects. Finally, an intelligent systems approach, specifically, fuzzy logic and artificial neural network (ANN) methodologies were implemented for the automatic defect detection in commercial C/C aircraft brake disks by using air-coupled ultrasonic results. For this, a multi-layer perceptron (MLP) with two hidden layers and a scaled conjugate gradient back-propagation (BP) learning algorithm was used for the ANN training. The network training process was performed in an off-line mode using the ANN toolbox in Matlab. The network training was repeated until a steady state was reached, where there was no further change in the synaptic weights. The ANN provided plausible results in detecting the defect areas for different C/C brake disks. It was also demonstrated that the system was able to learn the rules without knowing any algorithm for automatic defect detection.
6
Hassan, Syed Karimuddin and Syed Muhammad. "Defect Detection in SRS using Requirement Defect Taxonomy." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5253.
Full textAbstract:
Context: Defects occurred in the SRS may cause problems in project due to implementation of poor requirements which require extra time, effort, resources and budget to complete it. Reading techniques i.e., checklist based reading (CBR) helps to guide reviewers in identifying defects in software requirement specification (SRS) during individual requirement inspections. Checklists contain potential defects/problems to look for, but often lack clear definitions with examples of the problem, and also their abstractions are different. Therefore, there is a need for identifying existing defects and classifiers and to create a consolidated version of taxonomy. Objectives: We developed taxonomy for requirement defects that are in requirement specifications and compared it with the checklist based approach. The main objective was to investigate and compare the effectiveness and efficiency of inspection techniques (checklist and taxonomy) with M.Sc. software engineering students and industry practitioners by performing a both controlled student and industry experiment. Methods: Literature review, controlled student experiment and controlled industry experiment were the research methods utilized to fulfill the objectives of this study. INSPEC and Google scholar database was used to find the articles from the literature. Controlled student experiment was conducted with the M.Sc. software engineering students and controlled industry experiment was performed with the industry practitioners to evaluate the effectiveness and efficiency of the two treatments that are checklist and taxonomy. Results: An extensive literature review helped us to identify several types of defects with their definitions and examples. In this study, we studied various defect classifiers, checklists, requirement defects and inspection techniques and then built taxonomy for requirement defects. We evaluated whether the taxonomy performed better with respect to checklist using controlled experiments with students and practitioners. Moreover, the results of student experiment (p= 0.90 for effectiveness and p=0.10 for efficiency) and practitioner experiment (p=1.0 for effectiveness and p=0.70 for efficiency) did not show significant values with respect to effectiveness and efficiency. But because of less number of practitioners it is not possible to apply a statistical test since we also have used standard formulas to calculate effectiveness and efficiency. 2 out of the 3 reviewers using taxonomy found more defect types compared to 3 reviewers using checklist. 10-15% more defects have been found by reviewers using taxonomy. 2 out of the 3 reviewers using taxonomy are more productive (measuring in hours) compared to reviewers of checklist. Although the results are quite better than the student experiment but it is hard to claim that reviewers using taxonomy are more effective and efficient than the reviewers using checklist because of less subjects in number. The results of the post experiment questionnaire revealed that the taxonomy is easy to use and easy to understand but hard to remember while inspecting SRS than the checklist technique. Conclusions: Previously researchers created taxonomies for their own purpose or on industry demand. These taxonomies lack clear and understandable definitions. To overcome this problem, we built taxonomy with requirement defects which consists of definitions and examples. No claims are made based on student experiment because of insignificant values with respect to effectiveness and efficiency. Although the controlled industry experiment results showed that taxonomy performed slightly better than the checklist in efficiency i.e., in defect detection rate and effectiveness i.e., number of defect found. From this we can conclude that taxonomy helps guiding the reviewers to indentify defects from SRS but not quite much so it is recommended to perform a further study with practitioners in a large scale for effective results.skarimuddin@yahoo.com, hassanshah357@gmail.com
7
Klíma, Jakub. "Automatické vyhodnocování termovizních snímků fotovoltaických panelů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242855.
Full textAbstract:
This thesis deals with diagnostics of photovoltaic panels especially with infrared diagnostics. There are described defects which we can examine using thermovision and also this thesis explains the cause of their formation. Practical part deals with the design of the program that automatically detects defects on infrared images.
8
Rogers, Stuart Craig. "Defect Detection Microscopy." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2256.
Full textAbstract:
The automotive industry's search for stronger lighter materials has been hampered in its desire to make greater use of Magnesium alloys by their poor formability below 150°C. One current challenge is to identify the complex structure and deformation mechanisms at work and determine which of these are primary contributors to the nucleation of defects. Orientation Imaging Microscopy has been the most accessible tool for microstructural analysis over the past 15 years. However, using OIM to analyze defect nucleation sites requires prior knowledge of where the defects will occur because once the defects nucleate the majority of microstructural information is destroyed. This thesis seeks to contribute to the early detection of nucleation sites via three mechanisms: 1. Detection of cracks that have already nucleated, 2. Detection of surface topography changes that may indicate imminent nucleation and 3. Beam control strategies for efficiently finding areas of interest in a scan. Successive in-situ OIM scans of a consistent sample region while strain is increased, while using the three techniques developed in this thesis, will be employed in future work to provide a powerful defect analysis tool. By analyzing retrieved EBSD patterns we are able to locate defect / crack sites via shadowing on the EBSD patterns. Furthermore, topographical features (and potentially regions of surface roughening) can be detected via changes in intensity metrics and image quality. Topographical gradients are currently only detectable in line with the beam incidence. It is therefore suggested that the tensile specimens to be examined are orientated such that the resulting shear bands occur preferentially to this direction. The ability to refine the scan around these areas of interest has been demonstrated via an off-line adaptive scan routine that is implemented via the custom scan tool. A first attempt at a defect detection framework has been outlined and coded into MATLAB. These tools offer a first step to accessing the information about defect nucleation that researchers are currently seeking.
9
Ngan, Yuk-tung Henry, and 顏旭東. "Patterned Jacquard fabric defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30070880.
Full text
10
Auger, Marc. "Detection of laser-welding defects using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65599.pdf.
Full text
11
Kehoe, A. "Detection and evaluation of defects in industrial images." Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/804357/.
Full text
12
Jandová, Kristýna. "Diagnostické metody plošného rozložení defektů solárních článků." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-233487.
Full textAbstract:
This doctoral thesis deals with analysis of existing area defect detection methods in solar cells and with concept of its innovation and of the development of faster detection method. Results of measurement is analyzing in practical and theoretical part. The most important is LBIC (Light Beam Induced Current) method innovated of different wavelength light source usage and Electroluminescence method. On the bases of this knowledge is created Fast LBIC method and then is created catalog of defects in monocrystalline silicon solar cells.
13
Hunt, Kevin. "Modelling the origin of defects in injection moulded ceramics." Thesis, Brunel University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280892.
Full text
14
Priyosulistyo, Henricus. "Detection of defects in concrete structures using vibration technique." Thesis, University of Strathclyde, 1992. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21555.
Full textAbstract:
This thesis investigates the dynamic behaviour of reinforced concrete beams as they are loaded to failure. Four beams have been investigated. Two types of crack pattern and two types or reinforcement pattern were the main variable parameters. Partially bonded reinforcement as artificially created (by greasing the bars) and positioned at the center third span in two of the four beams investigated. The remaining two beams had conventional bonded reinforcement. Flexural and diagonal splitting patterns were created by loading mechanisms individually applied on two beams of each type of reinforcement. Stage by stage application of static loadings was used. Steady state vibration tests were applied at prior to loadings the beams and at several load stages as gradually increasing defects occurred. There are four parts to this investigation and these are presented in this thesis. The first part investigates the accuracy of several techniques dealing with signal parameters from a digital response spectrum in the signal processing. A logic geometry was developed and was applied on the line spectra of the response spectrum. Numerical evaluation found that the error induced in the proposed technique decreased exponentially with increasing numbers of cycles. A maximum of 0.17% errors may exist when examining 100 cycles of the frequency of interest. A regression analysis was used to achieve further accuracy of the results. The second part investigates the jump phenomenon of mechanical exciters and the sharp drop phenomenon of magnetic exciters. Both of which may confuse the analysis of structural dynamic behaviour. By accounting for the stiffness of the magnetic field of the magnetic exciter in a mathematical model, the jump phenomena was shown to be due to the effect of the reflected force in the excited structure. Practical equations were also proposed to relate absolute to relative parameters. The third part of the thesis concerns the algorithms required in filter processing and includes the development of a computer solution. Two algorithms were developed to obtain coefficients of a polynomial equation which was set up from elementary equations and from a rational function respectively. The algorithms were simple and easy to program. The last part of the thesis discusses the detection of flexural and diagonal splitting defects and non-linear behaviour of the beams during the vibration tests. Static and dynamic comparisons are also discussed. Based on the characteristics of the polar diagrams it was found that several possible types of non-linear damping were demonstrated in the experiments. The typical viscous and non-linear higher polynomial damping existed mostly in the models although the crack pattern and intensity of cracks contributed to changes in the type of damping. In addition the beam models in almost all conditions showed non-linear soft spring behaviour. Diagonal splitting crack patterns can be idenuried from a small decrease of resonant frequency and from the sharp drop of resonant amplitude. The presence of single deep cracks greatly reduced the stiffness. The experiments show that a sharp decrease of resonant frequency indicates that a large amount of residual strain exists. It is concluded that defects of the reinforced concrete beams can be identified from the changes of the dynamic parameters using the proper digital signal analyses. The jump phenomenon is shown to be due to the effect of the reflected force on the moving exciter mass rather than due to the presence of the non-linear soft spring system.
15
Ng, Nga-yi Ada. "Defect detection in semiconductor die images." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B32040799.
Full text
16
Ng, Nga-yi Ada, and 伍雅怡. "Defect detection in semiconductor die images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B32040799.
Full text
17
Xie, Xianghua. "Defect detection in random colour textures." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425096.
Full text
18
Pathak, Ajay Kumar. "Automated defect detection in textured materials." Thesis, Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23295168.
Full text
19
Grönlund, Jakob, and Angelina Johansson. "Defect Detection and OCR on Steel." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157508.
Full textAbstract:
In large scale productions of metal sheets, it is important to maintain an effective way to continuously inspect the products passing through the production line. The inspection mainly consists of detection of defects and tracking of ID numbers. This thesis investigates the possibilities to create an automatic inspection system by evaluating different machine learning algorithms for defect detection and optical character recognition (OCR) on metal sheet data. Digit recognition and defect detection are solved separately, where the former compares the object detection algorithm Faster R-CNN and the classical machine learning algorithm NCGF, and the latter is based on unsupervised learning using a convolutional autoencoder (CAE). The advantage of the feature extraction method is that it only needs a couple of samples to be able to classify new digits, which is desirable in this case due to the lack of training data. Faster R-CNN, on the other hand, needs much more training data to solve the same problem. NCGF does however fail to classify noisy images and images of metal sheets containing an alloy, while Faster R-CNN seems to be a more promising solution with a final mean average precision of 98.59%. The CAE approach for defect detection showed promising result. The algorithm learned how to only reconstruct images without defects, resulting in reconstruction errors whenever a defect appears. The errors are initially classified using a basic thresholding approach, resulting in a 98.9% accuracy. However, this classifier requires supervised learning, which is why the clustering algorithm Gaussian mixture model (GMM) is investigated as well. The result shows that it should be possible to use GMM, but that it requires a lot of GPU resources to use it in an end-to-end solution with a CAE.
20
Song, Keng Yew. "Surface defect detection on textured background." Thesis, University of Surrey, 1993. http://epubs.surrey.ac.uk/844113/.
Full textAbstract:
This thesis addresses the problem of defect detection on complex textural surfaces. In general, whether the texture to be inspected is regular or random, in image terms it is characterized by local variations in pixel grey level values. These normal variations render the problem of texture defect detection extremely difficult as defects are often manifested by grey level changes and their detection requires more than mere pixel comparisons. In the thesis, classical techniques on texture representation are studied and various existing texture defect detection algorithms are reviewed. Three novel algorithms have been developed to tackle the problem of defect detection on random or regular textures. The first two are devoted to the problem of crack detection and the third algorithm is devoted to the problem of detecting regional defects. For texture crack detection, a cojoint spatial and spatial frequency representation, that is, wigner distribution is proposed to model the inspected texture surface. A detailed analysis of the wigner distribution, its properties and the effect of windowing on its crack detection performance are carried out. Two postprocessing methods, ie, probabilistic relaxation labelling and linear filtering are incorporated into the crack detection algorithm to refine the results. The potential of the Wigner model has also been explored by modifying the crack detection algorithm so as to detect other types of defects. For real world applications, an efficient crack detection algorithm based on a new distribution is proposed. The algorithm is shown to produce comparable results but in much shorter time. For regional defect detection, a hybrid chromato-structural approach to colour texture representation is proposed where combined colour texture information is extracted from various chromatic classes associated with the inspected surface. In the approach, a unified defect detection framework which combines a new colour clustering scheme, morphological smoothing and blob analysis are used to capture the relevant combined colour texture information. With this framework, good defect detection results are obtained and presented in this thesis.
21
Foster, Moira. "Defect Detection in Selective Laser Melting." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1874.
Full textAbstract:
Additively manufactured parts produced using selective laser melting (SLM) are prone to defects created during the build process due to part shrinkage while cooling. Currently defects are found only after the part is removed from the printer. To determine whether cracks can be detected before a print is completed, this project developed print parameters to print a test coupon with inherent defects – warpage and cracking. Data recorded during the build was then characterized to determine when the defects occurred.
The test coupon was printed using two sets of print parameters developed to control the severity of warpage and cracking. The builds were monitored using an accelerometer recording at 12500 samples per second, an iphone recording audio at 48000 samples a second, and a camera taking a photo every build layer. Data was analyzed using image comparison, signal amplitude, Fourier Transform, and Wavelet Decomposition.
The developed print parameters reduced warpage in the part by better distributing heat throughout the build envelope. Reducing warpage enabled the lower portion of the part to be printed intact, preserving it to experience cracking later in the build. From physical evidence on the part as well as time stamps from the machine script, several high energy impulse events in the accelerometer data were determined to be when cracking occurred in the build. This project’s preliminary investigation of accelerometers to detect defects in selective laser melting will be used in future work to create machine learning algorithms that would control the machine in real time and address defects as they arise.
22
Fox, Matthew William. "Thermography approaches for building defect detection." Thesis, University of Plymouth, 2016. http://hdl.handle.net/10026.1/4304.
Full textAbstract:
Thermography is one technology, which can be used to detect thermally significant defects in buildings and is traditionally performed using a walk-through methodology. Yet because of limitations such as transient climatic changes, there is a key performance gap between image capture and interpretation. There are however new methodologies currently available, which actively address some of these limitations. By better understanding alternative methodologies, the performance gap can be reduced. This thesis contrasts three thermography methodologies (Walk-through, time-lapse and pass-by) to learn how they deal with limitations and address specific building defects and thermal performance issues. For each approach, practical methodologies were developed and used on laboratory experiments (hot plate) and real dwelling case studies. For the real building studies, 133 dwellings located in Devon and Cornwall (South West England) were studied; this sample represents a broad spectrum of construction types and building ages. Experiments testing these three methodologies found individual strengths and weaknesses for each approach. Whilst traditional thermography can detect multiple defects, characterisation is not always easy to achieve due to the effects of transient changes, which are largely ignored under this methodology. Time-lapse thermography allows the observation of transient changes from which more accurate assessment of defect behaviour can be gained. This is due to improved differentiation between environmental conditions (such as cloud cover and clear sky reflections), actual material thermal behaviour and construction defects. However time-lapse thermography is slow, complex and normally only observes one view. Walk-past thermography is a much faster methodology, inspecting up to 50 dwellings per survey session. Yet this methodology misses many potential defects due to low spatial resolutions, single (external only) elevation inspection and ignoring transient climate and material changes. The implications of these results for building surveying practice clearly indicate that for an improved defect characterisation of difficult to interpret defects such as moisture ingress, thermographers should make use of time-lapse thermography. A review of methodology practicalities illustrates how the need for improved characterisation can be balanced against time and resources when deciding upon the most suitable approach. In order to help building managers and thermographers to decide on the most suitable thermography approach, two strategies have been developed. The first combines different thermography methodologies into a phased inspection program, where spatial and temporal resolution increase with each subsequent thermography inspection. The second provides a decision-making framework to help select the most appropriate thermography methodology for a given scenario or defect.
23
Hu, Yazhe. "Degenerate Near-planar Road Surface 3D Reconstruction and Automatic Defects Detection." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98671.
Full textAbstract:
This dissertation presents an approach to reconstruct degenerate near-planar road surface in three-dimensional (3D) while automatically detect road defects. Three techniques are developed in this dissertation to establish the proposed approach. The first technique is proposed to reconstruct the degenerate near-planar road surface into 3D from one camera. Unlike the traditional Structure from Motion (SfM) technique which has the degeneracy issue for near-planar object 3D reconstruction, the uniqueness of the proposed technique lies in the use of near-planar characteristics of surfaces in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem using only two images. Following the accuracy-enhanced 3D reconstructed road surface, the second technique automatically detects and estimates road surface defects. As the 3D surface is inversely solved from 2D road images, the detection is achieved by jointly identifying irregularities from the 3D road surfaces and the corresponding image information, while clustering road defects and obstacles using a mean-shift algorithm with flat kernel to estimate the depth, size, and location of the defects. To enhance the physics-driven automatic detection reliability, the third technique proposes and incorporates a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from supervised learning approaches which need labeled training images, the road anomaly detection network is trained by road surface images that are automatically labeled based on the reconstructed 3D surface information. In order to collect clear road surface images on the public road, a road surface monitoring system is designed and integrated for the road surface image capturing and visualization. The proposed approach is evaluated in both simulated environment and through real-world experiments. The parametric study of the proposed approach shows the small error of the 3D road surface reconstruction influenced by different variables such as the image noise, camera orientation, and the vertical movement of the camera in a controlled simulation environment. The comparison with traditional SfM technique and the numerical results of the proposed reconstruction using real-world road surface images then indicate that the proposed approach effectively reconstructs high quality near-planar road surface while automatically detects road defects with high precision, accuracy, and recall rates without the degenerate issue.Doctor of Philosophy
Road is one of the key infrastructures for ground transportation. A good road surface condition can benefit mainly on three aspects: 1. Avoiding the potential traffic accident caused by road surface defects, such as potholes. 2. Reducing the damage to the vehicle initiated by the bad road surface condition. 3. Improving the driving and riding comfort on a healthy road surface. With all the benefits mentioned above, it is important to examine and check the road surface quality frequently and efficiently to make sure that the road surface is in a healthy condition. In order to detect any road surface defects on public road in time, this dissertation proposes three techniques to tackle the road surface defects detection problem: First, a near-planar road surface three-dimensional (3D) reconstruction technique is proposed. Unlike traditional 3D reconstruction technique, the proposed technique solves the degenerate issue for road surface 3D reconstruction from two images. The degenerate issue appears when the object reconstructed has near-planar surfaces. Second, after getting the accuracy-enhanced 3D road surface reconstruction, this dissertation proposes an automatic defects detection technique using both the 3D reconstructed road surface and the road surface image information. Although physics-based detection using 3D reconstruction and 2D images are reliable and explainable, it needs more time to process these data. To speed up the road surface defects detection task, the third contribution is a technique that proposes a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from traditional neural network-based detection techniques, the proposed combines the 3D road information with the CNN output to jointly determine the road surface defects region. All the proposed techniques are evaluated using both the simulation and real-world experiments. Results show the efficacy and efficiency of the proposed techniques in this dissertation.
24
Hegstam, Björn. "Defect detection and segmentation inmultivariate image streams." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150456.
Full textAbstract:
OptoNova is a world leading producer of inspection systems for qualitycontrol of surfaces and edges at high rates. They develop their ownsensor systems and software and have taken an interest in investigatingthe possibility of using methods from machine learning to make betteruse of the available sensor data.The purpose of this project was to develop a method for finding sur-face defects based on multivariate images. A previous Master’s projectdone at OptoNova had shown promising results when applying machinelearning methods to inspect the sides of kitchen cabinet doors. Themodel developed for that project was based around using a Differenceof Gaussians scale-space. That was used as a starting ground for thework presented here, with changes made in order to focus on texturedefects on flat surfaces.The final model works by creating a Laplacian image pyramid from asource image. Each pyramid level is processed by a trained image modelthat, given a multivariate image, produces a greyscale image indicatingdefect areas. The outputs of all image models are scaled to the same sizeand averaged together. This gives the final probability map indicatingwhat parts of the sample are defective. The image models consists of afeature extractor, extracting one feature per pixel, and a feature model,which in this project was a Gaussian mixture model. The model wasbuilt in a modular fashion, making it easy to use different features andfeature models.Tests showed the pyramid model to perform better than the previousmodel. Defects characterised by noticeable differences in surface texturegave excellent results, while defects only indicated by slight changes inintensity of the normal texture were generally not found.It was concluded that the developed model shows potential, butmore work needs to be done. More tests need to be run using larger datasets and samples with different texture types, such as wooden surfaces.OptoNova är en världsledande leverantör av inspektionssystem for kvali-tetskontroll av ytor och kanter i hög hastighet. Företaget utvecklar egnasensorsystem och mjukvara, och är intresserade av att undersöka möj-ligheten att bättre utnyttja tillgänglig sensordata genom att användametoder baserade på maskininlärning.Syftet med det här projektet var att utveckla en metod för att upp-täcka ytdefekter i multivariata bilder. Ett tidigare examensarbete gjorthos OptoNova visade på lovande resultat vid inspektion av kanter påköksluckor. Modellen som utvecklades i det projektet använde sig av ettDifference of Gaussians-skalrum. Den modellen användes som utgångs-punkt för det här arbetet med vissa förändringar gjorda för att läggafokus på texturdefekter i plana ytor.Den utvecklade modellen tar in en multivariat bild och genereraren Laplacepyramid. Varje nivå i pyramiden skickas sedan igenom entränad bildmodell som i sin tur producerar en gråskalebild där möjligadefekter är markerade. Samtliga bildmodellers resultat skalas upp tillsamma storlek som ursprungsbilden och en medelvärdesbild beräknas.Detta ger den slutliga defektbilden som visar vilka delar av det inlästaprovet som är defekta. Varje bildmodell består dels av en modul somextraherar särdragsvektorer och dels av en modul som modellerar hurvektorer från oskadade ytor är fördelade i rummet av särdragsvektorer.För det senare användes en Gaussian mixture model (GMM). Modellensmodullära design gör det enkelt att använda olika typer av särdragsvek-torer och modeller för dessa.Tester visade att pyramidmodellen kan prestera bättre än den tidi-gare utvecklade modellen. Utmärkta resultat uppnåddes vid detektionav defekter som karaktäriserades av tydliga avvikelser i textur. Defektersom däremot endast utgjordes av mindre variationer i intensitet hittadesgenerellt sett inte.Det konstaterades att den nya modellen visar på potential till attfungera väl, men att mer arbete fortfarande behöver göras. Framföralltmåste fler tester göras med fler prover, samt prover med varierandeytmönster, såsom träytor.
25
Renshaw, Jeremy Blake. "The mechanics of defect detection in vibrothermography." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389142.
Full text
26
HEGSTAM, BJÖRN. "Defect detection and segmentation inmultivariate image streams." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142069.
Full textAbstract:
OptoNova is a world leading producer of inspection systems for quality control of surfaces and edges at high rates. They develop their own sensor systems and software and have taken an interest in investigating the possibility of using methods from machine learning to make better use of the available sensor data. The purpose of this project was to develop a method for finding surface defects based on multivariate images. A previous Master’s project done at OptoNova had shown promising results when applying machine learning methods to inspect the sides of kitchen cabinet doors. The model developed for that project was based around using a Difference of Gaussians scale-space. That was used as a starting ground for the work presented here, with changes made in order to focus on texture defects on flat surfaces. The final model works by creating a Laplacian image pyramid from a source image. Each pyramid level is processed by a trained image model that, given a multivariate image, produces a greyscale image indicating defect areas. The outputs of all image models are scaled to the same size and averaged together. This gives the final probability map indicating what parts of the sample are defective. The image models consists of a feature extractor, extracting one feature per pixel, and a feature model, which in this project was a Gaussian mixture model. The model was built in a modular fashion, making it easy to use different features and feature models. Tests showed the pyramid model to perform better than the previous model. Defects characterised by noticeable differences in surface texture gave excellent results, while defects only indicated by slight changes in intensity of the normal texture were generally not found. It was concluded that the developed model shows potential, but more work needs to be done. More tests need to be run using larger data sets and samples with different texture types, such as wooden surfaces.OptoNova är en världsledande leverantör av inspektionssystem for kvalitetskontroll av ytor och kanter i hög hastighet. Företaget utvecklar egna sensorsystem och mjukvara, och är intresserade av att undersöka möjligheten att bättre utnyttja tillgänglig sensordata genom att använda metoder baserade på maskininlärning. Syftet med det här projektet var att utveckla en metod för att upptäcka ytdefekter i multivariata bilder. Ett tidigare examensarbete gjort hos OptoNova visade på lovande resultat vid inspektion av kanter på köksluckor. Modellen som utvecklades i det projektet använde sig av ett Difference of Gaussians-skalrum. Den modellen användes som utgångspunkt för det här arbetet med vissa förändringar gjorda för att lägga fokus på texturdefekter i plana ytor. Den utvecklade modellen tar in en multivariat bild och genererar en Laplacepyramid. Varje nivå i pyramiden skickas sedan igenom en tränad bildmodell som i sin tur producerar en gråskalebild där möjliga defekter är markerade. Samtliga bildmodellers resultat skalas upp till samma storlek som ursprungsbilden och en medelvärdesbild beräknas. Detta ger den slutliga defektbilden som visar vilka delar av det inlästa provet som är defekta. Varje bildmodell består dels av en modul som extraherar särdragsvektorer och dels av en modul som modellerar hur vektorer från oskadade ytor är fördelade i rummet av särdragsvektorer. För det senare användes en Gaussian mixture model (GMM). Modellens modullära design gör det enkelt att använda olika typer av särdragsvektorer och modeller för dessa. Tester visade att pyramidmodellen kan prestera bättre än den tidigare utvecklade modellen. Utmärkta resultat uppnåddes vid detektion av defekter som karaktäriserades av tydliga avvikelser i textur. Defekter som däremot endast utgjordes av mindre variationer i intensitet hittades generellt sett inte. Det konstaterades att den nya modellen visar på potential till att fungera väl, men att mer arbete fortfarande behöver göras. Framförallt måste fler tester göras med fler prover, samt prover med varierande ytmönster, såsom träytor.
27
Moshiri, Farzad, Bahareh Mobasher, and Issa Osama Talib. "Detection of defects in timber using dynamic excitation and vibration analysis." Thesis, Växjö University, School of Technology and Design, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-5444.
Full textAbstract:
This thesis evaluates the possibility to detect natural defects, such as knots, in timber boards using dynamic excitation test and ABAQUS software. In the study the edgewise bending direction were compared with axial direction. Dynamic excitation and modal analysis were used to extract the natural frequencies of several sound and artificially defected boards with the help of Signalcalc. Mobylizer software. By using the first edgewise natural frequency, modulus of elasticity (MOE) was calculated. An ABAQUS 2D Finite Element model was utilized to model the board and to extract the frequencies for the six first mode shapes in both axial and edgewise directions. The extracted frequencies from the model were compared with the frequencies from the tests. The analytical and experimental results, from the homogeneous boards, in edgewise direction has similar frequency variations. The defects in the timber boards decreased the natural frequencies. The bending modes with more curvature at the location of the artificial defect displayed more frequency deviation in that mode. The variation in response frequencies for uniform and defected boards was more noticeable in edgewise bending modes than in longitudinal modes.
28
Chaiworawitkul, Sakda 1977. "Detection of surface defects in infrastructure using wavelets and neural networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/84310.
Full textAbstract:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. [225]-[228]).
by Sakda Chaiworawitkul.
M.Eng.
29
Wang, Xiaoting. "Transient thermography for detection of micro-defects in multilayer thin films." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/25174.
Full textAbstract:
Delamination and cracks within the multilayer structure are typical failure modes observed in microelectronic and micro electro mechanical system (MEMS) devices and packages. As destructive detection methods consume large numbers of devices during reliability tests, non-destructive techniques (NDT) are critical for measuring the size and position of internal defects throughout such tests. There are several established NDT methods; however, some of them have significant disadvantages for detecting defects within multilayer structures such as those found in MEMS devices. This thesis presents research into the application of transient infrared thermography as a non-destructive method for detecting and measuring internal defects, such as delamination and cracks, in the multilayer structure of MEMS devices. This technique works through the use of an infrared imaging system to map the changing temperature distribution over the surface of a target object following a sudden change in the boundary conditions, such as the application of a heat source to an external surface. It has previously been utilised in various applications, such as damage assessment in aerospace composites and verification of printed circuit board solder joint manufacture, but little research of its applicability to MEMS structures has previously been reported. In this work, the thermal behaviour of a multilayer structure containing defects was first numerically analysed. A multilayer structure was then successfully modelled using COMSOL finite element analysis (FEA) software with pulse heating on the bottom surface and observing the resulting time varying temperature distribution on the top. The optimum detecting conditions such as the pulse heating energy, pulse duration and heating method were determined and applied in the simulation. The influences of thermal properties of materials, physical dimensions of film, substrate and defect and other factors that will influence the surface temperature gradients were analytically evaluated. Furthermore, a functional relationship between the defect size and the resulting surface temperature was obtained to improve the accuracy of estimating the physical dimensions and location of the internal defect in detection. Corresponding experiments on specimens containing artificially created defects in macro-scale revealed the ability of the thermographic method to detect the internal defect. The precision of the established model was confirmed by contrasting the experimental results and numerical simulations.
30
Oaker, Bradley. "The detection of defects in tubes and plates using guided waves." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11182.
Full textAbstract:
Eddy current testing is the non-destructive test method of choice for the inspection of condenser tubes. However, unplanned shutdowns of power stations, due to unexpected condenser tube failures, still occur despite rigorous eddy current inspection programs. In addition to the improvement required in the reliability of inspections, there is also a need to shorten the duration of inspections.
31
Campbell, Craig Maurice. "Signature analysis techniques for needle bearing defect detection." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/19539.
Full text
32
Ngan, Yuk-tung Henry. "Motif-based method for patterned texture defect detection." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/b40203608.
Full text
33
"Detecting Dissimilar Classes of Source Code Defects." Thesis, 2013. http://hdl.handle.net/10388/ETD-2013-08-1188.
Full textAbstract:
Software maintenance accounts for the most part of the software development cost and efforts, with its major activities focused on the detection, location, analysis and removal of defects present in the software. Although software defects can be originated, and be present, at any phase of the software development life-cycle, implementation (i.e., source code) contains more than three-fourths of the total defects. Due to the diverse nature of the defects, their detection and analysis activities have to be carried out by equally diverse tools, often necessitating the application of multiple tools for reasonable defect coverage that directly increases maintenance overhead. Unified detection tools are known to combine different specialized techniques into a single and massive core, resulting in operational difficulty and maintenance cost increment. The objective of this research was to search for a technique that can detect dissimilar defects using a simplified model and a single methodology, both of which should contribute in creating an easy-to-acquire solution. Following this goal, a ‘Supervised Automation Framework’ named FlexTax was developed for semi-automatic defect mapping and taxonomy generation, which was then applied on a large-scale real-world defect dataset to generate a comprehensive Defect Taxonomy that was verified using machine learning classifiers and manual verification. This Taxonomy, along with an extensive literature survey, was used for comprehension of the
properties of different classes of defects, and for developing Defect Similarity Metrics. The Taxonomy, and the Similarity Metrics were then used to develop a defect detection model and associated techniques, collectively named Symbolic Range Tuple Analysis, or SRTA. SRTA relies on Symbolic Analysis, Path Summarization and Range Propagation to detect dissimilar classes of defects using a simplified set of operations. To verify the effectiveness of the technique, SRTA was evaluated by processing multiple real-world open-source systems,
by direct comparison with three state-of-the-art tools, by a controlled experiment, by using an established Benchmark, by comparison with other tools through secondary data, and by a large-scale fault-injection
experiment conducted using a Mutation-Injection Framework, which relied on the taxonomy developed earlier for the definition of mutation rules. Experimental results confirmed SRTA’s practicality, generality, scalability and accuracy, and proved SRTA’s applicability as a new Defect Detection Technique.
34
Yang, Ren-Kewi, and 楊仁魁. "An Inspection System for Detecting Defects on Chip." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/79258641590958327275.
Full textAbstract:
碩士國立交通大學
資訊科學學系
83
An image inspection system for detecting defects on chip is developed. The system can automatically inspect defects of chips on wafer after operator's alignment.The system consists of three parts: 1. image acquisition, 2. image preprocessing, 3. defect detection. A chip to be inspected has three parts which are bonding pad, active region, and edge region. In bonding pad, the defect is coating.In active region, the defects are abnormal spots and scratches. In edge region, the defects are scratch and crack. These three parts are inspected sequentially for their defects. Good chip must pass all inspections of these three parts. Otherwise, chip is classified into bad chip without the inspection of next part when one part is rejected. Experimental result shows that the recognition rate of good chip can reach 99.21% and the recognition rate of bad chip can reach 90.48%.For inspection time, the average time of inspecting one chip by the system is about 6327.5 ms.
35
Chu, Chien-Cheng, and 朱建政. "The Research on the BGA Surface Defects Detecting System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/27776357187759659286.
Full textAbstract:
碩士元智大學
工業工程與管理學系
91
In the current manufacturing environment, a company still needs to have a faster and a more accurate ways to inspect a Ball Grid Array (BGA) surface defects. Traditionally, the BGA inspection was using gray-level images. However, the background, conduct paths and pads have very similar gray-levels that cannot easy be distinguished. The objectives of the research are: (1) Use some shape and uniformity features without making pattern matching for detecting BGA surface defects. (2) Use color image information instead of gray scales for the inspection. (3) Improve the speed and effectiveness of the inspection system. The traditional process of the image enhancement is to select a suitable color mode and then to proceed on the enhancement. The research proposed a method that to use the gamma correction method to replace the tradition process for image enhancement with the expectations of having better results and faster speeds. Because gamma correction corrects the three color bands (i.e., RGB), it could better separate the image between the high and low contrasts. And it could get the better results in dividing the image into background and foreground by using the Gamma correction and the R color band. As a result, the proposed method can improve the contrast value about 52.09%. Finally, the research uses the eigenvalues of the shape and uniformity to detect the defects. It could find almost all the defects. In using the traditionally enhancement method with a 640* 480 pixels image to do a completely defects detecting needs 1 sec. However, to use the proposed gamma correction method to do the same, needs only 0.3 sec. This research demonstrates the effectiveness of using gamma correction method for separating an image background from its foreground. And the developed method could detect the BGA surface defects without using pattern matching technique that required extensive alignment in both hardware and software.
36
Chin, Kuo-ming, and 秦國銘. "An Auto-detecting System for Surface Defects on Connectors." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/15136333166234472158.
Full textAbstract:
碩士朝陽科技大學
資訊工程系碩士班
96
Exploiting computer vision techniques, an auto-detecting system is developed for surface defects on connectors. The system hardware consists of a CCD (charge-coupled device) camera, a loop-shaped light-source, and an image capture board. Since the surface of connectors is made of stainless steel with high reflectance, it is difficult to locate the defects specifically. This problem is usually solved by applying extra light on the dark area. However, the reflection of the extra light might induce some detection errors. We classify the surface regions as some regions of interest (ROI) and ones that are not of interest (non-ROI) so as to accomplish real-time on-line diagnosis by image positioning and template recognition. Applied image processing techniques include edge detection, image segmentation, binary images, and template recognition. According to the examination results, the system effectively detects the surface defects. The Examination time is 32ms per sample.
37
White, Joshua. "Ultrasonic Tomography for Detecting and Locating Defects in Concrete Structures." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11097.
Full textAbstract:
This thesis evaluates a particular ultrasonic nondestructive testing (NDT) system in order to determine its capabilities and limitations in locating defects in concrete structures; specifically tunnel linings, bridge decks, and pavements. The device, a phased-array ultrasonic tomography (UST) system that utilizes shear waves, is a significant advancement in NDT systems. Consequently, there is a need in structural engineering to verify new technologies by assessing their flaw-detecting capabilities in a variety of structural applications.
The UST technique does not currently have a testing methodology that is field-ready. In order to develop a methodology, the system was evaluated based on its ability to detect simulated defects, then taken to the field to evaluate natural structural defects on public tunnels, pavements, and airport runways. Types of concrete defects the system is used to detect and localize include air- and water-filled voids, vertical cracks, horizontal delaminations, and abnormalities such as clay lumps. The device is also used to determine reinforcement depth and spacing as well as concrete thickness measurements.
This research concludes that the UST system is exceptional at locating horizontal delaminations ranging from 0.05-2.0 mm (0.002-0.079 in.), and is able to differentiate between fully debonded and partially-bonded areas. Vertical cracks could only be detected once they begin to form parallel to the testing surface; however, omission of surface details was found to be a strong indicator of crack presence. Backwall surfaces up to a depth of 762 mm (30 in.) were successfully and accurately determined. Air- and water-filled voids as well as reinforcement details such as layout and depth were also successfully determined and located. With the exception of some medium-sized clay lumps (with a diameter of approximately 102 mm, or 4 in.) surrounding reinforcement, all clay lumps tested were also highly successful.
38
Lai, Winky Wan Kei. "Meniscus thermal analysis for detecting defects in continuous cast slabs." Thesis, 2000. http://hdl.handle.net/2429/10447.
Full textAbstract:
Transverse corner cracking was found to be a predominant problem in continuously
cast slabs with peritectic carbon contents (0.09 - 0.16 wt % C). This thesis is based on an
industrial plant trial carried out at Dofasco's No.l straight mould continuous caster for low
carbon (0.04 - 0.06 wt % C) and peritectic grade (0.09 - 0.10 wt % C) steels. Embedded
thermocouples around the mould periphery located 12, 100, and 250 mm below the
meniscus were used to measure the thermal response of the strand during casting. The
objective of this study is to establish a cross-correlation between the mould thermal response
and the quality of the cast product.
In the analysis, it was found that the A-series thermocouples which were especially
instrumented for this plant trial, located just 12 mm below the meniscus, were the most
sensitive to thermal events at the meniscus. It was found that the A-series thermocouples
around the mould perimeter were capable of monitoring the metal level activity across the
mould, but the magnitude of the signals were too attenuated for use as a tool in metal level
control.
Transverse corner cracks were only found on the peritectic grade slabs and these
cracks tended to form more frequently around transient events such as width or SEN
changes, although they also formed during periods of steady-state. It was found that the
effect of thermal deviation at the edges of a broad face has a significant impact on the
quality of peritectic grade slabs. Transverse corner cracks were found on peritectic grade
slabs that were cast with a temperature deviation at the edges of the broad face exceeding
30°C. Corner cracking is related to differences in mould friction at the corners resulting
from differences in heat transfer and mould flux viscosity. From the inspected samples, the
slab corner with the lowest heat transfer was always found to be free of cracks. It was
postulated that the reduced heat transfer at the colder mould corner has increased friction
and the longitudinal stress from strand withdrawal was re-directed to the three corners with a
lower friction.
39
Chen, Yu-Ting, and 陳郁婷. "Detecting genomic aberrations in patients with developmental delays and congenital defects." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/47667337926971113623.
Full textAbstract:
碩士國立陽明大學
生命科學暨基因體科學研究所
95
Mental retardation/developmental delays and congenital defects are common pediatric problems. However, the etiology and pathogenesis of mental retardation and developmental delays are poorly understood, and in about half of cases the causes remain unknown. Submicroscopic chromosomal rearrangements (smaller than 2-3 Mb) are assumed as one of the most important causes of mental retardation and developmental delays, owing to that can results in segmental aneusomy, alter the gene dosage and destroy the gene functions. Therefore, detecting these chromosomal aberrations and interpreting them is an important work which facilitates identification of the etiology and the genotype-phenotype correlation. In addition, effective pre-selection is also essential because of the technical diversities and cost of screening genomic aberrations. Based on the reasons mentioned above, we propose to find out the causes of these patients’ phenotypes and investigate the genotype-phenotype correlations. Moreover, in most of affecting individuals extensive examines are usually called for to find out the cause of their conditions, therefore we also aim to establish a pre-selection system which could help to sort out proper patients for different examinations and so that improve the detecting efficiency. In this study, we used CGH to screening the genomic aberrations in 98 patients with unexplained mental retardation/developmental delays or congenital defects collected from Taipei Veteran General Hospital, and quantitative real-time PCR was performed to detect subtelomeric rearrangements. Combined with CGH and Q-PCR results, we identified the causative chromosomal anomalies in four patients, including one patient as 1p36 deletion syndrome, two patients as 1qter deletion syndrome, and one patient with unbalanced 16pter and 20q13.2-13.3 translocation. All of these chromosomal rearrangements were further mapping for the breakpoints to define the aberrant size. In case 1 of the 1p36 deletion syndrome, the breakpoint was located in chromosome band 1p36.31, with a deletion of ~5.4 Mb in size. For both case 2 and case 3 of the 1qter deletion syndromes, the breakpoints were located in chromosome band 1p43, with a deletion of ~8.4 Mb and ~9.6 Mb in size, respectively. The case 4 was identified as unbalanced translocation with chromosome 16pter deletion of at least 430 Kb in size and chromosome 20q13.2q13.3 duplication of at least 6.6 Mb in size. Our study supported that chromosomal rearrangements are the major causes of patients with mental retardation/developmental delays or multiple congenital anomalies, and also demonstrated the utilities of CGH in clinical cytogenetics. However, due to the limitation of CGH in detecting submicroscopic chromosomal rearrangements, there are still many patients whose causes of conditions are not yet identified. This accentuates again the importance and necessity to develop a high-resolution but low-cost technique which is suitable for applying in clinical screening.
40
Kou, Chan-Phon, and 郭建峰. "Detecting and Classifying Defects for aluminum foil by Image Processing Techniques." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/92923944745897138222.
Full textAbstract:
碩士國立臺灣科技大學
管理研究所工業管理學程
87
As the competition in the market getting intense,the guality and the price of the product play a critical role in the competitiveness of the product. Companies are therefore focusing their improvement on raising the quality of product and reducing the cost of production. A typical way to achieve both goals is to employ automation technology. One of the indispensible elements in the automatic production line is the automatic inspection capability. This research establishes an image-based inspection system that can be incorporated into automatic production line for detecting and classifying defects of 2D objects. With the low cost of the system and the tremendous saving on the labor,the system provides a feasible solution to improve the quality of the product. Such a system has to be able to capture the appearance of the 2D objects and convert the information into an image file for analysis. With the file,the image is preprocessed,and the features are then extracted and classified. These functions need to be designed with respect to specific domains in which the applications reside. This research considers the inspection of aluminum foil to demonstrate the feasibility and to fine-tune the implemented automatic inspection system.
41
Chia-HaoHsu and 許家豪. "The Study of Detecting the Internal Defects of Wall Tile and Wooden Member of Buildings." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/80926486397068616377.
Full textAbstract:
碩士國立成功大學
土木工程學系碩博士班
98
Taiwan is located at subtropical area, with high temperature and humidity. Under the construction quality, using of materials and irresistible disasters and so on, lots of factors will cause some defects such as the internal damage, the deterioration of buildings and cracks in the surface even make the exterior brick falling, and then making the influences of durability and the appearance of the buildings even endangered the public safety. However, if we want to knockout and rebuilding, it is not only high costs but also cause the safety of buildings. One of the most important is how to acquire the destruction degree and the distribution of the defects for the building to beneficial the follow-up rehabilitation. This study is aimed to the phenomena of the exterior wall tile hollowed and the internal hole of wooden member of buildings via the non-destructive testing. Discussing the applicability and feasibility of differ defects tests. The results are as follows, 1. Comparing by the frequency value of hammer tapping, the velocity value of ultrasonic and the temperature difference value after heating, the related trends of more severe hollowing of the tile, it is correspond to the higher frequency value, higher ultrasonic velocity value, and the higher temperature difference value, and this can assess the extent of tile hollowing. 2. The relationship with the tile of hollowing rate at on-site inspection area and different methods is : hammer tapping, the hollowing rate (y) and the frequency value (x), the linear relationship y = 0.0163x + 7.6165 (R2 = 0.78); ultrasonic detection, the hollowing rate (y) and ultrasonic velocityvalue (x), the linear relationship y = 0.0693x - 144.52 (R2 = 0.86); infrared(IR) thermography, the hollowing rate (y) and maximum temperature difference value (x), the linear relationship y = 39.688x - 551.43 (R2 = 0.90). 3. To compare the results of testing by using infrared thermography technique, ultrasonic detection and hammer tapping to detect the hollowing location and extent of wall tiles, the results are consistent, but the ultrasonic detection and hammer tapping have a higher estimate of the situation hollowing. 4. This study uses ultrasonic technology to detect internal defects of wood member, used in accordance with this research method, it can detect the internal hole of wood member over 3.5 cm diameter.
42
Neves, Lara Souto das. "Automatic defect detection in wind turbine blades: A Deep Learning Model Pipeline for Detection and Classification of Defects in Drone Images." Master's thesis, 2022. http://hdl.handle.net/10362/134713.
Full textAbstract:
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe decarbonisation of the energy industry is key in the global approach to the climate emergency. Wind energy in particular, plays an important role in transitioning the global energy system to more sustainable sources. To do so, it must optimise O&M costs through a data-driven, predictive maintenance approach. When it comes to the maintenance of a wind turbine, the manual inspection of blade photographs - often taken by drones - is a time-consuming and labour intensive process, yet it is fundamental to ensure the turbine’s continued energy production during its lifecycle. This thesis intends to purpose, test, and implement a defect detection pipeline based on the Faster-RCNN deep learning architecture, capable of detecting 6 distinct classes of blade defects to a level of accuracy suitable enough to deploy in A.I.-assisted inspections.
Tendo em vista a emergência climática, é fundamental a descarbonização do sector energético a nível global. A energia eólica, em particular, tem um papel importante na transição do sistema energético atual para fontes de energia inteiramente limpas e sustentáveis. Para tal, é necessário otimizar os custos de operação e manutenção de turbinas eólicas através de uma abordagem preditiva baseada em dados recolhidos regularmente por meio de fontes automáticas, das quais drones estão em primeiro plano. No que toca à manutenção de turbinas eólicas, a inspeção manual de fotografias de pás é um processo muito demorado e de mão-de-obra intensiva sendo, no entanto, fundamental para a continuação de produção de energia ao longo da vida útil da turbina. O objetivo desta tese é propor, testar e implementar uma pipeline de deteção automática de defeitos baseada na arquitetura de modelo de aprendizagem profunda Faster-RCNN. Este modelo de inteligência artificial é capaz de detetar 6 classes distintas de defeitos na pá, a um nível de precisão adequado para auxiliar nas inspeções, reduzindo o tempo de inspeção, a possibilidade de erros de deteção e os custos de mão-de-obra.
43
WANG, FU-CHING, and 王富慶. "Tire Bubble Defects Detection Using ResNet." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ph97y5.
Full textAbstract:
碩士國立雲林科技大學
資訊工程系
107
Digital shearography used to detect tire bubble defects that are unobservable by the naked-eye. The tire manufacturer obtains the tire image through digital shearography, and then judges the bubble defect through the experience operate. The determination of the bubble defects depends not only on the experience and observation of the personnel, but also because there is no uniform judgment standard due to different personnel. This thesis proposes a residual network to detect bubble defects. In the training phase, the whole tire image is divided into several blocks. Use the data augmentation method to increase the training sample, and then input into the network for training；In the test phase, the tire image is pre-processed to select suspected bubble defect areas, and then these suspicious areas are input into the network model for bubble defect classification. The final output is in two categories: bubble-defect and non-defect. In the experimental results, the bubble defect detection rate is about 95%, and the non-defect classification accuracy rate is about 85%. For this method which can help tire manufacturers to further achieve automated inspection and save labor costs.
44
Tuan, Tran Ngoc, and 陳玉俊. "Detection Defects of Bearing by Acoustic Approach." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/85938866248849630879.
Full textAbstract:
碩士明新科技大學
精密機電產業研發碩士外國學生專班
98
ABSTRACT This technical report provides a method, based on classification techniques, for automatic detection defects of rolling element bearings. We used sound pressure measurement by an 824 sound level meter and a real-time analyzer, which is a production of LARSON DAVIS firm. By measuring sound pressure emission from rolling elements bearings on a model with a fixed motor speed for all bearings, one can collect the signatures of the measured signal. We separate the bearings into two groups, which are a good bearing set and a bad bearing set, where the bad bearings are made artificially damage. Through applying a scattering matrix theory find a set of feature, which can distinguish quality of bearings. We further collect the selected features from the table to train a neural network with target output of a good bearing or a bad bearing. After training, the neural network can detect bearing quality accurately.
45
Y, Lee C., and 李傳玉. "Machine Vision Detection of Monitor Screen Defects." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/13633457480277964566.
Full text
46
Wang, Hsuan-Yin, and 王炫尹. "PCB Defects Detection Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u379ep.
Full textAbstract:
碩士國立暨南國際大學
資訊工程學系
107
The annual output value of printed circuit board (PCB) related industry is more than 21 billion US dollars which implies that the quantity of the produced PCBs per year is extremely large. However, the yield rate of PCBs is limited and if defective PCBs cannot be detected and discarded in the early stage of producing an electronic system, then they will lead to large amount of profit loss. Nowadays, many high speed automatic optical inspection systems can be used to classify defective PCBs. However, a closer inspection of the discarded PCBs will reveal that almost 70\% of them are actually misclassified. In this work, we develop an accurate PCB defect re-identification system based on deep learning techniques. We tested the performance of ResNet (Residual Network), DenseNet (Densely Connected Convolutional Network), GoogLeNet (Google Inception Net), and EFMNet (Extremal Feature Map Network) developed by us. A 98\% PCB defect re-identification accuracy is achieved. The developed system can dramatically reduce the false-defect rate.
47
Tu, Wei-Shan, and 杜瑋珊. "Resonance frequency assessment ofdental implant detecting defect." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/39086715308231031334.
Full textAbstract:
碩士國立中央大學
機械工程研究所
97
The dental implant is generally used in patients who are edentulous and missing natural tooth. This study is based on the resonant frequency response to specify criteria for examining the defect direction and defect severity in the bone of dental implant. Both the finite element analysis and the experimental modal analysis are applied to compare the differences between experiment and simulation. In the first stage, resonance frequency analysis (RFA) was applied to estimate stability of bone-implant structure in full size. The variation of RF was used to locate the direction of bone that was non-ossestintegration. The resonance frequency (RF) increased substantially as better stability of bone-implant structure was achieved. In the second stage, The boundary condition was varied to simulate different mandible. The RF with different boundary was used to decide defect type. Then, we collated those data and defined a criterion to detect the defect bone depth with dental implant. The three detection steps include that using the severe RF to decide the mandible type, locating the direction of defect bone, and deciding the defect type. In the end, we prove that RFA was effective method for examining the defect direction and defect severity in the bone of dental implant.
48
Chen, Yu-Ping, and 陳育屏. "Video Defect Detection and Inpainting." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/86206764654466115122.
Full textAbstract:
碩士淡江大學
資訊工程學系碩士班
93
Video inpainting uses spatial-temporal information to repair defects such as spikes and lines on aged films. We propose a series of new algorithms based on adjustable thresholds to repair different varieties of aged films. The main contribution is an automatic spike and dirt detection mechanism. We prove that if appropriate threshold is once decided by the author, almost all damages in an aged video clip can be detected. In addition, the repairing procedure first estimates temporal information and obtain replacement blocks among several frames. Spatial information is then used to repair damages that can not be fixed by temporal information due to fast motion. The results are visually pleasant with most defects removed.
49
Hsieh, Wenlung, and 謝汶龍. "Solder Ball Defect Detection Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/10016529977572684747.
Full textAbstract:
碩士國立聯合大學
光機電整合產業專班
101
In the paper, an automatic detection algorithm is proposed to detect the defects of solder ball in ball grid array (BGA). The defects include polluted solder ball and lack of boundary smoothness in the solder ball. The algorithm applies the color characteristics of pollutants to segment them, computes pollutants’ areas, and judges the results by criteria. Moreover, after finding the solder ball boundary by applying the techniques of color characteristics, morphology, and boundary enhancement, the algorithm adopts fractal dimension to measure the complexity of the segmented boundary and judges the result by criteria. The simulation result shows the ratios of correct defect decision are more than 90%.
50
Hau, Huang Shr, and 黃士豪. "Detection of Lens Defects Using LED Lighting Systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/2c4f5n.
Full textAbstract:
碩士國立高雄應用科技大學
光電與通訊工程研究所
102
This paper aims to do a series of exploration on the flawed lenses according to the different wavelength’s LED(Light-Emitting-Diode), and finds out the best way for detecting the flawed lenses. This paper combines the optical simulation software TracePro and the graphic software SolidWorksfordesigning the solid model in accordance with defect lenses. This paper adopts one kind of double gauss lens group which is most often used on photos taking and the camera lens, this sort of the lens design can raise the effect of focusing, eliminate the chromatism, and design the detection system of defect in lenses that is invisible easily by human eyes through controlling the LED’s wavelength and the focal distance, then bring up one kind of the way for doing lenses defect detection that used on LED lighting system. This way of detection uses the principle that the difference of refraction coefficient between the material and the medium, and the other principle that LED’s beam of light gets into the material interface and causes partial lights refracting back and forming light’s loss, and the dark point on the formation of image, to detect the defect on the lenses. The finding reveals that the effect of lenses defect detection on monochromatic light is better than the polychromatic light, in the monochromatic light, the red-ray LED’s contrast is higher than the other monochromatic light, and it can find out the lenses defect’ location and size, and also can recognize whether the tiny object on the lenses is thedefect bubble or not, or just the dust, fingerprint, and the spot. The contribution in this paper is: the technique for current lenses defect detection all uses the direct detection in appearance, as a result of the detective model’s quantity is large, variety is much, the precision is high, and by using this method not only consumes much time, the efficiency is low, the prime cost is high, and the mis-detected rate for using human eyes to do the detection is high, but for using the LED illumination system that we designed as the lenses defect detection instrument can be effective and find out every location’s defect promptly, and this detective system has some advantages such as low prime cost, easily being gotten, the manipulation is easy and shooting lenses’ defect is rapid.