Automatic X-ray inspection of industrial parts usually uses reference-based methods, in which a set of model images or statistics extracted from the model image set are selected as the benchmark. Based on these methods, many systems are developed and are used extensively for anomaly detection. However, the performance of these systems relies heavily on the model image set. Thus, the selection of the model images is very important. This paper presents an approach for automatically selecting a set of model images to be used in a reference-based assisted defect recognition (ADR) system for anomaly detection of turbine blades of jet engines. The proposed approach to generating a model image set is based on feature extraction. Features are extracted from callout images of ADR, including potential defect indication type, size and location. Experimental results show that the proposed approach is fast and a low false alarm rate with acceptable detection rate is ensured. Moreover, the approach is applicable to different blade types and varied views of the blade. Further validation shows that the approach can be applied to the update of the model image set, when more images are generated from new blades and the model becomes inaccurate for anomaly detection in the new images.
An algorithm for 3D surface reconstruction of large objects using a structured light pattern ranging system is presented. Highly accurate industrial inspection applications have been constrained by the limited range resolution and accuracy of current ranging devices and techniques. To overcome the limited range resolution, the ranging sensor uses a small field-of-view and multiple views. The proposed algorithm fuses surface data patches from the views to construct a large object surface. The algorithm also increases the accuracy of the reconstructed object with efficient numerical analysis and pre-processing. Experimental results show that the algorithm and the current sensor setup can reconstruct an object for inspection applications with the accuracy of approximately 1 mil (2.54μm ) tolerance.
This paper presents a framework for developing a Web-based distributed image processing application system that is flexible, convenient, and scalable. The system uses the existing Web-based technology and image processing methodologies to implement this capability in a distributed computing environment that may include powerful machines to process complex and large images. The system consists of browser, server, service registry and task scheduler, image data storage and management, and knowledge-based image processing services. A server-side application considers the user’s request from a client side. The server host identifies the request and the necessary resources and schedules the computing resources and image processing services. Based on the instructions of the developer’s side (the service provider) a proper knowledge-based on-line assistance is given to the client to select the right algorithm, set up proper parameter values in order to maximize the usage. Developers can modify and upgrade the services at their own site and publish the workable version, its interface, and required resources to the server. The server enables remote invocation of the algorithm by providing a seamless and efficient linkage mechanism. An application for segmentation operation using deformable contour methods for complex images is provided as an example.
Cracks occurred in aircraft engine parts have to be detected as early as possible to prevent engine failure. Fluorescent Penetrant Inspection (FPI), that applies fluorescent materials on metallic surfaces for flaw detection, is a generally accepted technology for nondestructive inspection of surface cracks. The major problem with application of FPI technology is the costly false alarms caused by non-crack fluorescence indications (noise), especially when inspecting used engine parts. A novel crack-detection system for automatic FPI of engine parts using image processing and pattern recognition theories is presented. A strong noise reduction capability and a small number of reliable features for pattern recognition are the two primary characteristics of the system, which contains three major modules: noise-reduction and preclassifier module, feature extraction module, and pattern recognition module including four pattern classifiers. An image synthesizing technique is developed to simulate real-world situations by combining the segmented fluorescence images of man-made cracks with the noisy background of fluorescent images captured from actual used parts. The designed system can eliminate over 80% of noise while retain 94% of crack indication. The total error rate using Fisher's linear classifier is less than 3%, with only 4% of crack misclassification.
This paper describes the functions, the advantages, and the performing steps of preventive control in power plants, and presents the methods to construct this prototype expert system. The differences between preventive control and diagnosis are also described. Preventive control consists of two major areas: (1) Correction of process control of generator system, and (2) prevention of equipment failure. This system uses frames along with rules to construct a large knowledge base. there are three main methods for developing the knowledge base; (1) Constructing root frame and subframes according to the properties of operational process and equipment; (2) determining all parameters used in the knowledge base; and (3) turning the expertise into rules by means of decision trees. Examples are presented for describing this prototype expert system.
Case Based Reasoning (CBR) is one of the recently emerging paradigms for designing intelligent systems. The preliminary studies indicate that the area is ripe for theoretical advances and innovative applications. Heuristic search is one of the most widely used techniques to solve many real world problems for obtaining optimal solutions. In this paper we identify some necessary properties of the heuristic functions to be solved in the CBR paradigm. We designed a CBR system based on these observations and performed several experiments for a heuristic search problem. We also provide an analysis to compare the performance of the CBR system with the A* search algorithm.
This research describes a robust, efficient, and real-time computer vision system that can automatically inspect defects of protruded print characters on injection molded plastic, typewriter print wheels. Possible defect types include insufficient fill, voids, and cracks. These defects can be described as poor edge sharpness, large edge position deviation from an established standard, and irregularities of the inside surface. Template matching is used for character detection and extraction. Matching performance measurements are used to evaluate closeness with respect to a reference print character of accepted quality. A hierarchical structure is used to improve the robustness of position detection and acceptable performance measures in knowledge rules are incorporated to increase the speed of the search. Characters are extracted from the image by a logical 'AND' operation in which a filled, slightly enlarged, uniform gray scale pattern of the print character is used as a template. Feature extraction and matching is done by using a distance image template matching technique which makes the system more robust and effective. Finally, a set of matching measurements is extracted to determine edge sharpness, edge deviation, and smoothness of the inside surface and local matching measurements are used for determining the detail of defects.
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