Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography

Infrared thermographic inspection system is widely being utilized for defect detection in electrical equipment. Conventional inspection based on the temperature data interpretation and evaluation the condition of the equipment is subjective and depends on the human experts. Implementation of an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In this thesis, an automatic features extraction system from thermal image of defects and the intelligent classification of thermal condition based on neural network are proposed. The proposed system extracts first order histogram based features and grey level co-occurrence matrix features from the segmented regions and evaluates the effectiveness of these features for defect characterization. Three feature selection techniques namely principal component analysis, the discriminant analysis and individual feature performance analysis are employed to find out the useful and important statistical features. In this study, multilayered perceptron network is proposed for classifying thermal condition into two classes namely normal and defective. The multilayered perceptron neural networks are trained using various training algorithms. Additionally, the present research introduces a computer aided defect diagnosis system where the defected region is found by manual thresholding and intensity features are extracted from each segmented region. The results prove that the statistical features are capable to classify thermal condition and the neural networks achieve the accuracy around 73~78

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particularly as a failure within this field can result in damages and fatalities. During assembling and soldering of printed circuit boards (PCBs) the circuit carriers can be subject to errors. Hence, automatic optical inspection (AOI) systems are used for real-time detection of visible flaws and defects in production. This article introduces an application strategy for combining a deep learning concept with an optical inspection system based on image processing. Above all, the target is to reduce the risk of error slip through a second inspection. The concept is to have the inspection results additionally evaluated by a convolutional neural network. For this purpose, different training datasets for the deep learning procedures are examined and their effects on the classification accuracy for defect identification are assessed. Furthermore, a suitable compilation of image datasets is elaborated, which ensures the best possible error identification on solder joints of electrical assemblies. With the help of the results, convolutional neural networks can achieve a good recognition performance, so that these can support the automatic optical inspection in a profitable manner. Further research aims at integrating the concept in a fully automated way into the production process in order to decide on the product quality autonomously without human interference

Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings

Online system for automatic tropical wood recognition

There are more than 3000 wood species in tropical rainforests, each with their own unique wood anatomy that can be observed using naked eyes aided with a hand glass magnifier for species identification process. However, the number of certified personnel that have this acquired skills are limited due to lenghty training time. To overcome this problem, Center for Artificial Intelligence & Robotics (CAIRO) has developed an automatic wood recognition system known as KenalKayu that can recognize tropical wood species in less than a second, eliminating laborious manual human inspection which is exposed to human error and biasedness. KenalKayu integrates image acquisition, feature extraction, classifier and machine vision hardware such as camera, interfaces, PC and lighting. Grey level co-occurrence matrix (GLCM) is used for feature extraction. The features are trained in a back-propagation neural network (BPNN) for classification. This paper focusses more on the database development and the online testing of the wood recognition system. The accuracy of the online system is tested on different image quality such as image taken in low light condition, medium light condition or high light condition

Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions

Automatic classification of oranges using image processing and data mining techniques

Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetables production lines, the quality assurance is done by trained people who inspect the fruits while they move in a conveyor belt, and classify them in several categories based on visual features. In this paper we present an automatic orange’s classification system, which uses visual inspection to extract features from images captured with a digital camera. With these features train several data mining algorithms which should classify the fruits in one of the three pre-established categories. The data mining algorithms used are five different decision trees (J48, Classification and Regression Tree (CART), Best First Tree, Logistic Model Tree (LMT) and Random For- est), three artificial neural networks (Multilayer Perceptron with Backpropagation, Radial Basis Function Network (RBF Network), Sequential Minimal Optimization for Support Vector Machine (SMO)) and a classification rule (1Rule). The obtained results are encouraging because of the good accuracy achieved by the clas- sifiers and the low computational costs.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

An automatic welding defects classifier system

Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases