As an important part of aero-engines, blades are used to compress the air entering the engine and produce a lot of thrusts. Catastrophic and sudden accidents caused by blade failure seriously ...threaten aero-engine operation safety, hence it is necessary to check blades regularly to ensure their health and reliability. As a common non-destructive testing technology, borescope inspection is widely used in health monitoring and maintenance of aero-engine blades. However, traditional borescope inspection mainly relies on artificial vision and it is a time-consuming and experience-dependent process. In this paper, a deep learning-based blade damage detection method is proposed to endow borescope inspection with intelligence. The proposed method pays more attention to texture information, which reflects the types of damages. It is applicable to the situation requiring higher localization accuracy due to the balance between coarse-grained and fine-grained localization. In this method, the enhanced Mask R-CNN network with three functions of damage mode classification, damage localization, and damage area segmentation is constructed. Moreover, a texture-focus multi-scale feature fusion network is used to give more attention to the shallow texture information which reflects the shape of damage. Balanced L1 loss is introduced to balance coarse-grained and fine-grained localization by adjusting the gradient and loss of easy samples. We also propose practical evaluation metrics for blade damage detection and make detailed evaluations and discussions. Extensive experiments are conducted on simulated and real aero-engine damaged blade datasets to verify the effectiveness and progressiveness of our method, and the results show the method has great potential for intelligent detection of aero-engine in-situ blade damage.
Surface damage detection is vital for diagnosis and monitoring of aeroengine blade. At present, borescope inspection is the dominant technology. Several inspectors hold borescope to inspect the ...blades one by one through naked eyes on the apron. The inspection of turbine blades even requires drilling into narrow aeroengine tail nozzle. The manual visual inspection is high cost and low efficiency. To improve detection efficiency and economic benefit, we propose an intelligent borescope inspection method in this article. Facing the problem of weak damage information caused by background noise and unsatisfactory illumination, local window transformer network efficiently models pixel-to-pixel relations with the help of global self-attention mechanism, and shifted window strategy is used to conduct information exchange. The capacity of global modeling is beneficial for capturing detailed damage outline. Besides, to learn label relations as prior and embed it into model, semantic information of different damages is aggregated by a two-layer graph convolution network. The global label graph network provides global prior by modeling label dependencies based on the samples in dataset. Finally, the image features and label features are fused to provide rich feature representation for mode recognition and damage localization. We validate the effectiveness of the proposed method on three datasets, including simulated blade, aluminum, and real blade datasets. The results demonstrate that the proposed method has superior performance with 84.9 mAP on simulated blade dataset and satisfactory visualization results on real blade dataset.
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.
Background—The inspection of aircraft parts is critical, as a defective part has many potentially adverse consequences. Faulty parts can initiate a system failure on an aircraft, which can lead to ...aircraft mishap if not well managed and has the potential to cause fatalities and serious injuries of passengers and crew. Hence, there is value in better understanding the risks in visual inspection during aircraft maintenance. Purpose—This paper identifies the risks inherent in visual inspection tasks during aircraft engine maintenance and how it differs from aircraft operations. Method—A Bowtie analysis was performed, and potential hazards, threats, consequences, and barriers were identified based on semi-structured interviews with industry experts and researchers’ insights gained by observation of the inspection activities. Findings—The Bowtie diagram for visual inspection in engine maintenance identifies new consequences in the maintenance context. It provides a new understanding of the importance of certain controls in the workflow. Originality—This work adapts the Bowtie analysis to provide a risk assessment of the borescope inspection activity on aircraft maintenance tasks, which was otherwise not shown in the literature. The consequences for maintenance are also different compared to flight operations, in the way operational economics are included.
Airworthiness, as a field, encompasses the technical and non-technical activities required to design, certify, produce, maintain, and safely operate an aircraft throughout its lifespan. The evolving ...technology, science, and engineering methods and, most importantly, aviation regulation, offer new opportunities and create, new challenges for the aviation industry. This book assembles review and research articles across a variety of topics in the field of airworthiness: aircraft maintenance, safety management, human factors, cost analysis, structures, risk assessment, unmanned aerial vehicles and regulations. This selection of papers informs the industry practitioners and researchers on important issues.
In this paper we present a technique for tracking borescope tip pose in real-time. While borescopes are used regularly to inspect machinery for wear or damage, knowing the exact location of a ...borescope is difficult due to its flexibility. We present a technique for incremental borescope pose determination consisting of off-line feature extraction and on-line pose determination. The off-line feature extraction precomputes from a CAD model of the object the features visible in a selected set of views. These cover the region over which the borescope should travel. The on-line pose determination starts from a current pose estimate, determines the visible model features, and projects them into a two-dimensional image coordinate system. It then matches each to the current borescope video image (without explicitly extracting features from this image), and uses the differences between the predicted and matched feature positions in a least squares technique to iteratively refine the pose estimate. Our approach supports the mixed use of both matched feature positions and errors along the gradient within the pose determination. It handles radial lens distortions inherent in borescopes and executes at video frame rates regardless of CAD model size. The complete algorithm provides a continual indication of borescope tip pose.
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