A deep-learning-based approach for aircraft engine defect detection

Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection.Engineering and Physical Sciences Research Council (EPSRC): 11317

Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge

Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades

Background—Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method—This paper compares the performance of human operators with image processing, artificial intelligence software and 3D scanning for different types of inspection. The results were statistically analysed in terms of inspection accuracy, consistency and time. Additionally, other factors relevant to operations were assessed using a SWOT and weighted factor analysis. Results—The results show that operators’ performance in screen‐based inspection tasks was superior to inspection software due to their strong cognitive abilities, decision‐making capabilities, versatility and adaptability to changing conditions. In part‐based inspection however, 3D scanning outperformed the operator while being significantly slower. Overall, the strength of technological systems lies in their consistency, availability and unbiasedness. Conclusions—The performance of inspection software should improve to be reliably used in blade inspection. While 3D scanning showed the best results, it is not always technically feasible (e.g., in a borescope inspection) nor economically viable. This work provides a list of evaluation criteria beyond solely inspection performance that could be considered when comparing different inspection systems

Civil and Military Airworthiness

Effective safety management has always been a key objective for the broader airworthiness sector. This book is focused on safety themes with implications on airworthiness management. It offers a diverse set of analyses on aircraft maintenance accidents, empirical and systematic investigations on important continuing airworthiness matters and research studies on methodologies for the risk and safety assessment in continuing and initial airworthiness. Overall, this collection of research and review papers is a valuable addition to the published literature, useful for the community of aviation professionals and researchers

Aeronautical Engineering: A continuing bibliography with indexes, supplement 106

This bibliography lists 388 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Near Infrared Thermal Imaging for Process Monitoring in Additive Manufacturing

This work presents the design and development of a near infrared thermal imaging system specifically designed for process monitoring of additive manufacturing. The overall aims of the work were to use in situ thermal imaging to develop methods for monitoring process parameters of additive manufacturing processes. The main motivations are the recent growth in use of additive manufacturing and the underutilisation of near infrared camera technology in thermal imaging. The combination of these two technologies presents opportunities for unique process monitoring methods which are demonstrated here. A thermal imaging system was designed for monitoring the electron beam melting process of an Arcam S12. With this system a new method of dynamic emissivity correction based on tracking the melted material is shown. This allows for the automatic application of emissivity values to previously melted areas of a layer image. This reduces the potential temperature error in the thermal image caused by incorrect emissivity values or the assumption of a single value for a whole image. Methods for determining materials properties such as porosity and tensile strength from the in situ thermal imaging are also shown. This kind of analysis from in situ images is the groundwork for allowing part properties to be tuned at build time and could remove the need for post build testing that would determine if it is suitable for use. The system was also used to image electron beam welding and gas tungsten arc welding. With the electron beam welding of dissimilar metals, the thermal images were able to show the preheating effect that the melt pool had on the materials, the suspected reason for the process’s success. For the gas tungsten arc welding process analysis methods that would predict weld quality were developed, with the aim of later integrating these into the robotic welding process. Methods for detecting the freezing point of the weld bead and tracking slag spots were developed, both of which could be used as indicators of weld quality or defects. A machine learning algorithm was also applied to images of pipe welding on this process. The aim of this was to develop an image segmentation algorithm that could be used to measure parts of the weld in process and inform other analysis, like those above

NASA Tech Briefs, April 1990

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

NASA Tech Briefs, December 1989

Topics include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, and Life Sciences