Detection of dish manufacturing defects using a deep learning-based approach

Quality control is essential to ensure the smooth running of an industrial process. This work proposes to use and adapt a deep learning-based algorithm that will integrate an automatic quality control system at a porcelain dish factory. This system will receive images acquired in real time by high resolution cameras directly placed on production line. The algorithm proposed in this research work will classify the dishes presented in the images as "defective" or "without defect". Therefore, the objective of the system will be the detection of defective dishes, causing fewer defective dishes to reach the market, thus contributing to a better reputation of the factory. This system is based on the application of an algorithm called Convolutional Neural Network. This algorithm requires a large amount of data to be trained and to perform the image classification. Since the COVID-19 pandemic was felt on a larger scale in Portugal at the time of the development of this research work, it was impossible to obtain data directly from the factory. Due to this setback, the data used in this work was artificially generated. By providing the complete images of dishes to the algorithm, it achieved a defect detection accuracy of 92.7% with the first dataset and 91.9%. with the second. When providing the algorithm 100x100 pixel segments of the original images, using the second created dataset, it reached 91.6% accuracy in the classification of these segments, which translated into a 52.0% accuracy rate in the classification of the complete dish images.O controlo de qualidade é fundamental para assegurar o bom funcionamento de um processo industrial. Este trabalho propõe a utilização e adaptação de um algoritmo, baseado em aprendizagem profunda, como parte integrante de um sistema automático de controlo de qualidade numa fábrica de pratos de porcelana. Este sistema receberá imagens adquiridas em tempo real por câmaras fotográficas colocadas diretamente sobre a linha de produção. O algoritmo utilizado classificará os pratos presentes nas imagens como "defeituoso" ou "sem defeito". O objetivo do sistema será, portanto, a deteção de pratos defeituosos, fazendo com que menos pratos com defeito cheguem ao mercado, contribuindo assim para uma melhor reputação da fábrica. Este sistema é baseado na aplicação de uma rede neuronal convolucional. Este tipo de redes requer um elevado número de dados para ser treinado de modo a conseguir realizar a classificação de imagens. Uma vez que a pandemia de COVID-19 se fez sentir em maior escala em Portugal na altura do desenvolvimento deste trabalho, foi impossível a obtenção de imagens provenientes da fábrica. Devido a este contratempo, os dados utilizados neste trabalho foram gerados artificialmente. Ao fornecer imagens completas de pratos ao algoritmo, o mesmo atingiu uma taxa de acerto da deteção de defeitos de 92,7% com o primeiro conjunto de dados e 91,9% com o segundo. Ao fornecer ao algoritmo segmentos de 100x100 pixéis da imagem original, o mesmo atingiu 91,6% de taxa de acerto, o que se traduziu numa taxa de acerto de 52,0% na classificação das imagens completas de pratos

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

Aerial Image Analysis using Deep Learning for Electrical Overhead Line Network Asset Management

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

A Study of the Detection of Defects in Ceramic Insulators Based on Radio Frequency Signatures.

The presence of defects in outdoor insulators ultimately results in the initiation of partial discharge (PD) activity. Because insulation failure and the consequent breakdown of power equipment can occur due to the cumulative adverse effects of partial discharges, it is important to detect PD activity in its early stages. Current techniques used in PD off-line analyses are not suitable for detecting defective insulators in the field. The work presented in this thesis involved the investigation of a number of cases of insulator defects, with the goal of developing an online RF-based PD technique for monitoring ceramic disc insulators that exhibit a variety of defects. The first three classes examined were an intentionally cracked ceramic insulator disc; a disc with a hole through the cap, which creates internal discharges; and a completely broken insulator disc. The fourth class involved an external corona noise using a point-to-plane setup. The defective discs were considered individually and were also incorporated into strings of 2, 3, and 4 insulators as a means of capturing the radiated RF signatures under external high voltage AC power. The captured RF pulses were processed in order to extract statistical, spectral, and wavelet packet based features. Feature reduction and selection is carried out and classification results pertaining to each feature-set type were obtained. To classify the discharges arising from different types of defects, an artificial neural network (ANN) algorithm was applied to the extracted features, and recognition rates of more than 90% were reported for each class. In addition, the position of the defective insulator within the string was varied and high defect classification results exceeding 90% were reported regardless of the position

Partial Discharge Classification Using Acoustic Signals and Artificial Neural Networks and its Application in detection of Defects in Ceramic Insulators

Online condition monitoring of critical assets constitutes one method whereby the electrical insulation industry can help safeguard grids through the avoidance of system outages due to insulation failure. This thesis introduces a novel approach for monitoring the condition of outdoor ceramic insulators based on partial discharge (PD) measurements. The presence of physical defects such as punctures, broken porcelain, and cracks will ultimately lead to the initiation of PD activity in outdoor ceramic insulators. In addition to defects, surface discharges such as that caused by corona and dry band arcing are also very common, particularly in wet and polluted outdoor insulators. Such a discharge activity that originates in these kinds of conditions can cause flashover or insulator failure, resulting in power outages. Measuring early-stage discharge activity is thus very important as a means of avoiding catastrophic situations in power networks. The work presented in this thesis involved initial tests conducted to distinguish between different types of controlled discharges generated in the laboratory. The next step was the implementation of an artificial neural network (ANN) for classifying the type of discharge based on selected features extracted from the measured acoustic signals. First, relatively high-frequency acoustic signals are transformed into low-frequency signals using an envelope detection algorithm imbedded in the commercial acoustic sensor. A fast Fourier transform (FFT) is then applied to each low-frequency signal, and finally, 60 Hz, 120 Hz, and 180 Hz are used as input feature vectors for the developed ANN. This initial research was then extended to include testing of the proposed diagnostic tool on a practical insulation system, and outdoor ceramic insulators were selected for this purpose. Three types of defects were tested under laboratory conditions: a cracked ceramic insulator, a healthy insulator contaminated by wetting with salt water, and a corona generated from a thin wire wound to the ceramic insulator. Both a single disc, and three discs connected in an insulator string were tested with respect to these defects. For both controlled samples and full insulators, a recognition rate of more than 85 % was achieved

Partial Discharge Classification Using Acoustic Signals and Artificial Neural Networks and its Application in detection of Defects in Ceramic Insulators

Online condition monitoring of critical assets constitutes one method whereby the electrical insulation industry can help safeguard grids through the avoidance of system outages due to insulation failure. This thesis introduces a novel approach for monitoring the condition of outdoor ceramic insulators based on partial discharge (PD) measurements. The presence of physical defects such as punctures, broken porcelain, and cracks will ultimately lead to the initiation of PD activity in outdoor ceramic insulators. In addition to defects, surface discharges such as that caused by corona and dry band arcing are also very common, particularly in wet and polluted outdoor insulators. Such a discharge activity that originates in these kinds of conditions can cause flashover or insulator failure, resulting in power outages. Measuring early-stage discharge activity is thus very important as a means of avoiding catastrophic situations in power networks. The work presented in this thesis involved initial tests conducted to distinguish between different types of controlled discharges generated in the laboratory. The next step was the implementation of an artificial neural network (ANN) for classifying the type of discharge based on selected features extracted from the measured acoustic signals. First, relatively high-frequency acoustic signals are transformed into low-frequency signals using an envelope detection algorithm imbedded in the commercial acoustic sensor. A fast Fourier transform (FFT) is then applied to each low-frequency signal, and finally, 60 Hz, 120 Hz, and 180 Hz are used as input feature vectors for the developed ANN. This initial research was then extended to include testing of the proposed diagnostic tool on a practical insulation system, and outdoor ceramic insulators were selected for this purpose. Three types of defects were tested under laboratory conditions: a cracked ceramic insulator, a healthy insulator contaminated by wetting with salt water, and a corona generated from a thin wire wound to the ceramic insulator. Both a single disc, and three discs connected in an insulator string were tested with respect to these defects. For both controlled samples and full insulators, a recognition rate of more than 85 % was achieved

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply

Cellulose nitrate objects in collections: history of science and technology hand in hand with conservation of cultural heritage

Celluloid is a notoriously challenging material for conservation science due to its fast, danger- ous, and complex degradation. The conservation of celluloid heritage continues to be at risk and it is urgent to develop sustainable and efficient preservation strategies. This doctoral pro- ject innovatively addresses this challenge by adopting an interdisciplinary approach combin- ing conservation science with the history of science and technology. By focusing on early in- dustrial formulations and using the first products made with celluloid as case studies, billiard balls and dentures, this project contributes to a new understanding of celluloid’s materiality, historical significance and conservation needs. The history of celluloid billiard balls and celluloid dentures has long remained shrouded in mystery. Historians deemed celluloid billiard balls as an impractical failure. This perception was largely due to a lack of understanding of the billiard balls’ composition. Using a multi- analytical approach to analyze billiard balls from the National Museum of American History (NMAH, Washington, D.C., EUA), it was found that they were made with a bone-cellulose nitrate composite (75%/25% by wt.), patented by John Wesley Hyatt in 1869. By correlating this material information with written sources, the bone-cellulose nitrate composite was found to be consistently employed in celluloid billiard ball production from the 1870s to the 1950s and played a crucial role in bringing the decline of ivory in billiard balls. Similarly, celluloid dentures have been considered a product with limited success and likely insignificant after the 1880s. This study aimed at characterizing the early celluloid-ver- milion (mercury sulfide, HgS) compositions of denture collections from the NMAH and the National Museum of Dentistry, Baltimore, USA. The results of these analysis, combined with source research, revealed an unexpected finding, namely that celluloid remained a significant material in dental prosthetics production up until the 1940s. These findings offer valuable in- sights into the evolution of celluloid billiard balls and dentures manufacture and highlight the importance of continued efforts to identify and understand the various formulations of celluloid, in all its material dimensions, as this knowledge significantly contributes to the con- servation of its cultural heritage. In Portugal, celluloid has entered oblivion since research on early plastics is very scarce. Therefore, a vital objective of this thesis was to contribute to a broader understanding of its historical significance in Portugal. Two comb collections from Casa da Memória de Guimarães and Sociedade Martins Sarmento (Guimarães) were analyzed, revealing the presence of cellu- loid, horn, cellulose acetate, and polystyrene. The results were used, in conjunction with writ- ten and statistical sources, to examine the impact of celluloid on the evolution of the Portu- guese comb industry. The case study of Portugal offers a compelling illustration of the inter- play between horn and celluloid in the shift from traditional industries to modern plastic man- ufacturing sectors. It is now of outmost importance to establish preservation strategies for these collections due to their importance in the history of plastics in Portugal. The experience of analyzing different heritage collection in the USA and Portugal with handheld Raman MIRA DS, demonstrates the efficacy of this technique to characterize cellu- loid and plastics. The device's portability and versatility facilitated in-situ analysis producing high-quality spectra validated through reference comparison and supplemented by μFTIR. This thesis strongly advocates for the broader implementation of handheld Raman spectros- copy in studying plastics heritage. Finally, in response to the need for effective methods to study celluloid degradation, this thesis presents an innovative approach to address this critical research issue. Synchrotron Deep UV multispectral micro luminescence spectroscopy (DUV-PL) was employed for the first time to investigate celluloid heritage, taking advantage of its high sensitivity and spatial resolution. This approach proved essential for detecting early degradation markers and char- acterizing the heterogeneous environments of zinc oxide, which are linked to the manufactur- ing or degradation history. Overall, this thesis shows the considerable potential of DUV-PL for the study of plastics heritage and for the development of innovative methods for its preser- vation.O celulóide é um dos materiais mais desafiantes para as ciências da conservação devido à sua rápida, perigosa e complexa degradação. Apesar de vários esforços passados, a conservação dos objetos históricos de celulóide continua em risco e é urgente desenvolver estratégias de preservação que sejam eficientes e sustentáveis. Este projeto de doutoramento aborda o desa- fio da conservação do celulóide de uma forma inovadora, adotando uma abordagem multi- disciplinar que que combina as ciências da conservação com a história da ciência e tecnologia. Ao focar-se nas formulações industriais primordiais e usando como casos de estudo os pri- meiros produtos fabricados com celulóide, bolas de bilhar e dentaduras, este projeto contribui para uma nova compreensão da materialidade, significância e conservação do celulóide. A história das bolas de bilhar e dentaduras de celulóide há muito que permanece envolta em mistério. As bolas de bilhar de celulóide têm sido consideradas pelos historiadores como um fracasso, sobretudo devido à falta de conhecimento sobre a sua composição. Usando uma abordagem multi-analítica para analisar bolas de bilhar de celulóide do National Museum of American History (Museu Nacional da História Americana, Washington, D.C., EUA), foi des- coberto que estas eram produzidas com uma mistura de osso moído e nitrato de celulose (75%/25% em peso), processo patenteado por John Wesley Hyatt em 1869. Correlacionando esta informação com fontes históricas, esta tese demonstra que este material compósito foi utilizado na produção de bolas de bilhar de celulóide, de forma consistente, desde 1870 até 1950, e desempenhou um papel crucial na abolição do marfim em bolas de bilhar. Do mesmo modo, historicamente, as dentaduras de celulóide têm sido consideradas como um produto com sucesso limitado e provavelmente insignificante após 1880. Este estudo teve como objetivo a caracterização das composições de celulóide-vermelhão (sulfureto de me- cúrio, HgS) encontradas nas coleções de dentaduras do National Museum of American His- tory e do National Museum of Dentistry (Museu Nacional da Odontologia), Baltimore, EUA. Os resultados desta análise, juntamente com análise de fontes escritas, revelaram uma desco- berta inesperada, nomeadamente que as dentaduras de celulóide permaneceram um material significativo na produção de próteses dentárias até 1940. Estes resultados são fundamentais para uma nova perceção sobre a evolução industrial das bolas de bilhar e dentaduras de celu- lóide e destacam a importância de estudar as diversas formulações do celulóide, em todas as suas dimensões materiais, uma vez que este conhecimento contribui significativamente para a conservação da sua cultura material. Em Portugal, existe um risco sério de que o celulóide caia no esquecimento, uma vez que a investigação sobre os chamados pré plásticos é escassa. Por essa razão, esta tese procura também contribuir para uma compreensão mais alargada da significância deste material neste país. Duas coleções de pentes da Casa da Memória de Guimarães e da Sociedade Martins Sar- mento (Guimarães) foram analisadas, revelando a presença de celulóide, chifre, acetato de ce- lulose e poliestireno. Os resultados foram utilizados, em conjunto com fontes escritas e esta- tísticas, para examinar o impacto do celulóide na evolução da indústria portuguesa de pentes. O caso português é ilustrativo da interação entre o chifre e o celulóide na transição de indús- trias tradicionais para a indústria moderna dos plásticos. É urgente estabelecer medidas de preservação para estas coleções dada a sua importância na história dos plásticos em Portugal. A análise de diferentes coleções, tanto nos EUA como em Portugal, com o espectrómetro portátil Raman MIRA DS, demonstrou a eficácia deste equipamento para caracterizar o celu- lóide e plásticos no geral. A portabilidade e versatilidade do equipamento facilitaram a análise in-situ, produzindo espectros de alta qualidade validados por comparação com referências materiais e complementados por μFTIR. Esta tese recomenda a implementação mais ampla da espectroscopia Raman portátil no estudo dos plásticos históricos. Finalmente, em resposta à necessidade de métodos eficazes para estudar a degradação do celulóide, esta tese apresenta uma abordagem inovadora: tirando partido da sua alta sen- sibilidade e resolução espacial, microespectroscopia multiespectral de luminescência UV de sincrotrão (DUV-PL) foi usada pela primeira vez para investigar a degradação do celulóide em objetos históricos. Esta abordagem é demonstrada como essencial para detetar marcadores de degradação nas fases iniciais de degradação e caracterizar os ambientes heterogéneos do pigmento óxido de zinco; ambientes esses ligados à história de manufatura e/ou degradação. Esta tese mostra o grande potencial do DUV-PL para o estudo dos plásticos em geral