Borescope Inspection Research Articles

Superpixel perception graph neural network for intelligent defect detection of aero-engine blade

Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. However, existing traditional technology which is borescope inspection is labor-intensive, time-consuming, and experience-dependent. To endow this technology with intelligence, a novel superpixel perception graph neural network (SPGNN) is proposed by utilizing a multi-stage graph convolutional network (MSGCN) for feature extraction and superpixel perception region proposal network (SPRPN) for region proposal. First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations. Then, MSGCN composed of several GCN blocks extracts graph structure features and performs graph information processing at graph level. Last but not least, the SPRPN is proposed to generate perceptual bounding boxes by fusing graph representation features and superpixel perception features. Therefore, the proposed SPGNN always implements feature extraction and information transmission at the graph level in the whole SPGNN pipeline, to alleviate the reduction of receptive field and information loss. To verify the effectiveness of SPGNN, we construct a simulated blade dataset with 3000 images. A public aluminum dataset is also used to validate the performances of different methods. The experimental results demonstrate that the proposed SPGNN has superior performance compared with the state-of-the-art methods.

Grouting inspection of PC bridge girder using Ultrasonic Pulse Echo Tomography and multiparameter evaluation procedure

Assessing the condition of grouting in post-tensioned tendons within Prestressed Concrete (PC) structures stands as a challenge in the field of civil engineering infrastructure maintenance. Current non-destructive testing (NDT) techniques employed for grouting assessment have certain constraints. For instance, the Impact-Echo method exhibits relatively low accuracy, while the X-Ray method proves to be costly and demands significant time and labor resources. Simultaneously, there exist promising alternative NDT techniques that offer potential for grouting inspection. In this study the authors used commercially available Ultrasonic Pulse Echo Tomography device with an array of dry-contact sensors and multiparameter method of evaluation. The evaluation method uses amplitude of ultrasonic pulse reflected from PC sheath, backwall and phase of reflected ultrasonic pulse. The experimental study on large-scale specimens confirmed that the evaluation method allows to achieve higher accuracy, as compared to existing methods of evaluation. The developed procedure was applied for inspection of PC bridge girder on an existing road bridge. The inspection was carried out on one of the spans of the bridge, which had 4 I- shaped PC girders. The results of Ultrasonic Pulse Echo Tomography were confirmed by means of drilling and borescope inspection method The results of both methods showed good correlation.

Wear depth estimation from single 2-D image based on shape from Shading and convolutional neural network hybrid model for in-situ wear assessment

Vision-based wear inspection is a prevalent technique in in-situ assessment, such as borescope inspection. Evaluating wear volume of the damage has relied on Shape From Shading (SFS) algorithms to reconstruct the 3-D topography from a single 2-D image. However, this approach faces limitations in accuracy due to data insufficiency and the impact of non-Lambertian reflections on a 2-D surface image. To address this issue, a depth estimation method is proposed, centered on an SFS-Convolutional Neural Network (CNN) hybrid model. First, in terms of the insufficient information of a 2-D image, a coarse-to-fine detection network has been developed to identify the damage region. Regarding the influence of the non-Lambert reflection of the worn surface, an SFS-CNN hybrid model is constructed incorporating an SFS-based guidance branch and a CNN branch based on a dual-attention mechanism. Specifically, the latter emphasizes the connection between the 2-D damage image and the depth information, disregarding non-Lambertian reflection, while the former extracts the initial depth information from the damage region for CNN learning. Consequently, the wear volume can be derived by accumulating the depth of the reconstructed 3-D worn region. To verify, several real worn surfaces have been used and examined using the proposed model and Laser Scanning Confocal Microscopy. Experimental results indicate that the proposed method can determine the depth of a worn surface from a single surface image, and the absolute relative error of the computed wear volume is less than 19 %, which satisfies the requirement of in-situ wear condition assessment.

S763 Utilization of a Novel Artificial Intelligence Algorithm for Assessment and Categorization of Endoscope Instrument Channel Defects With Borescope Inspection

S763 Utilization of a Novel Artificial Intelligence Algorithm for Assessment and Categorization of Endoscope Instrument Channel Defects With Borescope Inspection

Global Prior Transformer Network in Intelligent Borescope Inspection for Surface Damage Detection of Aeroengine Blade

Surface damage detection is vital for diagnosis and monitoring of aero-engine 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 aero-engine 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 paper. 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, shifted window strategy is used to conduct information exchange. The capacity of global modeling is benefit for capturing detail 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.

Investigation of the efficacy of an innovative endoscope drying and storage method in a simulated ERCP setting.

Background and study aims Drying after cleaning and disinfection is essential in the reprocessing of endoscopes since microorganisms can grow and form biofilms on wet surfaces. In this experimental non-clinical study, we investigated the efficacy of a novel, fast-drying method when reprocessing duodenoscopes. Methods During a series of 40 tests, three duodenoscopes were exposed to an artificial test soil containing supraphysiological loads of four types of gut microorganisms in a non-clinical ERCP simulation, followed by reprocessing and drying with the PlasmaTYPHOON. Cultures of the distal tip and working channel were acquired immediately after automated decontamination and after drying with the PlasmaTYPHOON. Cobalt chloride paper tests and borescope inspections were used to evaluate drying efficacy. Results Contamination of the working channels dropped from 86.4 % post-decontamination to 33.6 % post-drying, with 94 % of the positive post-drying samples belonging to one duodenoscope. This duodenoscope showed persistent contamination with P. aeruginosa in the working channel. The other two duodenoscopes only showed low levels of P. aeruginosa in post-decontamination channel samples, but not after drying. Cobalt chloride paper tests and borescope inspections revealed good drying efficacy. Conclusions Positive cultures for gut microorganisms were often found in wet endoscopes post-decontamination. The PlasmaTYPHOON is an effective fast-drying method capable of abolishing nearly all remaining microorganisms after decontamination provided no biofilm has developed, even when using a supraphysiological concentration of bacterial load. The clinical use of the PlasmaTYPHOON has the potential to reduce endoscope contamination, the use of wet contaminated endoscopes and therefore the risk of patient infection.

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.

Clinical Issues-November 2022.

Reuse of single-use bronchoscopes Key words: single use, bronchoscope, contamination, time limit, instructions for use (IFU). Endoscope processing room workflow Key words: one-room design, decontamination sink, droplets, clean storage, transport. Simethicone use during endoscopy Key words: adenosine triphosphate (ATP) testing, defoaming agent, water bottle, borescope inspection, residue. Implementing borescope inspection of flexible endoscopes Key words: visual inspection, cleaning verification, high-risk endoscopes, competency verification, prepurchase evaluation.

Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s

When performed by a person, aero-engine borescope inspection is easily influenced by individual experience and human factors that can lead to incorrect maintenance decisions, potentially resulting in serious disasters, as well as low efficiency. To address the absolute requirements of flight safety and improve efficiency to decrease maintenance costs, it is imperative to realize the intelligent detection of common aero-engine defects. YOLOv5 enables real-time detection of aero-engine defects with a high degree of accuracy. However, the performance of YOLOv5 is not optimal when detecting the same defects with multiple shapes. In this work, we introduce a deformable convolutional network into the structure of YOLOv5s to optimize its performance, overcome the disadvantage of the poor geometric transformability of convolutional neural networks, and enhance the adaptability of feature maps with large differences in the shape features. We also use a depth-wise separable convolution to improve the efficiency of multichannel convolution in extracting feature information from each channel at the same spatial position while reducing the increased computational effort due to the introduction of deformable convolution networks and use k-means clustering to optimize the size of anchor boxes. In the test results, mAP50 reached 83.8%. The detection accuracy of YOLOv5s for common aero-engine defects was effectively improved with only a 7.9% increase in calculation volume. Compared with the metrics of the original YOLOv5s, mAP@50 was improved by 1.9%, and mAP@50:95 was improved by 1.2%. This study highlights the wide application potential of depth science methods in achieving intelligent detection of aero-engine defects. In addition, this study emphasizes the integration of DDSC-YOLOv5s into borescope platforms for scaled-up engine defect detection, which should also be enhanced in the future.

A NOVEL ALTERNATIVE FOR DETECTING ENDOTRACHEAL TUBE BLOCK IN A RESOURCE-LIMITED SETTING DURING COVID-19 PANDEMIC

TYPE: Case Report TOPIC: Respiratory Care PURPOSE: A large number of critically ill Covid-19 patients requiring mechanical ventilation globally. Endotracheal tube block is a common complication of mechanically ventilated patients. Detecting an endotracheal tube block is a challenge in a resource-limited setting. Intubation poses a high risk of increases aerosol exposure to health care workers. METHODS: 25 mechanically ventilated patients with suspected ET tube block were considered after prior written consent. Teslong 3.9 mm Borescope(Cost- 200 USD) was used in the study. A borescope is an optical device used for inspection of difficult access places it consists of a camera at one end of a semi-rigid tube and a 4.5 inch HD display at the other end. The adaptor for the open suction system was used to pass the borescope into the endotracheal tube since its easy availability in resource-constrained settings. The borescope was passed through the endotracheal tube and inspection of the endotracheal tube was done. RESULTS: 18 patients had endotracheal tube block due to airway secretion and mucous plugging, 2 patients had endotracheal tube bites and 5 patients endotracheal tube was normal. The average time for visual inspection was 80 seconds in the 25 patients and no complication was observed during or post-procedure. CONCLUSIONS: This novel borescope inspection is an excellent and rapid tool for visual inspection for detection of endotracheal tube block in critically ill patients during the covid 19 pandemic. This affordable and novel approach makes it ideal for usage in a resource-limited setting. CLINICAL IMPLICATIONS: The technique is ideal for detecting ET block in a resource-limited setting DISCLOSURE: Nothing to declare. KEYWORD: ET block, Covid 19, recourse limited setting, critical care, respiratory care, training

Persistent contamination of a duodenoscope working channel in a non-clinical simulated ERCP setting.

Background To mitigate duodenoscope contamination, recent design enhancements have primarily focused on the distal tip. However, the working channels remain unchanged, which may be linked to biofilm formation. We assessed the persistence of microorganisms, indicative of biofilm formation, in reprocessed duodenoscopes in a non-clinical endoscopic retrograde cholangiopancreatography (ERCP) simulation setting. Methods Three new duodenoscopes were over-soiled in non-clinical ERCP simulations followed by reprocessing. After 40 tests, the Pseudomonas aeruginosa strain in the soil (Pa-type 1) was switched to a different P. aeruginosa strain (Pa-type 2) for 20 subsequent tests. Cultures of the tip and working channel were acquired after high level disinfection and overnight storage. Results One duodenoscope showed persistent growth of P. aeruginosa from the fifth test until the end of the study. Pa-type 1 remained present until the end of the study in the cultures of this duodenoscope, even after discontinuation of exposure to that specific strain. The other two duodenoscopes only showed incidental contamination. Conclusion Persistent contamination by Pa-type 1 was seen in one out of three duodenoscopes after exposure to supraphysiological levels of gut microorganisms. No clear explanation was found for this persistent contamination as exposure and handling were identical and no abnormalities of this particular duodenoscope were identified by borescope inspection.

Health and Durability of Protective and Thermal Barrier Coatings Monitored in Service by Visual Inspection

Protective and Thermal Barrier Coatings (TBC) applied on gas-turbine blades gradually degrade due to oxidation, aluminum depletion and impacts of environmental particles. Among various non-destructive coating testing methods (NDT), visual inspection can be undertaken regularly in service, but it provides little quantitative information, and only surface defects can be detected. This work aims at in-service monitoring of turbine blades with multilayer coatings applied by atmospheric plasma spraying (APS) in a few variants. They were validated during a series of accelerated mission tests of a retired military turbofan engine in a test cell together with five other technologies. The fifty-hour rainbow test focused on assessing coating durability. Between engine runs, 12 borescope inspections were conducted to monitor the health of the blades. Finally, the blades were disassembled and examined using computed tomography (CT) and metallographic methods. Throughout the testing, 31 newly-coated blades (66%) withstood the tests, producing results comparable to the reference blades. However, 16 blades suffered intolerable failures observed as increased roughness, gradual loss of the topcoat, spallation and minor foreign object damage. Visual inspection results were generally in agreement with subsequent laboratory tests.

Semantic Damage Segmentation for Aircraft Engine Borescope Videos

Due to extremely high temperatures, friction, and vibrations, aircraft engines tend to have various types of internal damage. To guarantee the safety of the aircraft, maintenance for aircraft engines is regularly performed by manual borescope inspection, which is time consuming and error prone. Existing studies adopted deep learning-based approaches to detect potential damage in borescope images to accelerate the process of engine maintenance. However, these approaches are designed for damage detection from static images and cannot be directly used to track damage in borescope videos due to the extremely high computational cost. To detect and track the damage in borescope videos in real time, we propose the deep fusion network (DFNET), which works along two parallel functional paths: i.e., the segmentation path and the spatial warping path. The segmentation path only runs on selected key frames to extract semantic features, which are then propagated to other frames through optical flows in the spatial warping path. The performance and efficiency of the DFNET are validated through extensive experiments using real borescope videos from a local air carrier.

Deep learning-based borescope image processing for aero-engine blade in-situ damage detection

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.

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.

Initial application of deep learning to borescope detection of endoscope working channel damage and residue.

Background and study aims Outbreaks of endoscopy-related infections have prompted evaluation for potential contributing factors. We and others have demonstrated the utility of borescope inspection of endoscope working channels to identify occult damage that may impact the adequacy of endoscope reprocessing. The time investment and training necessary for borescope inspection have been cited as barriers preventing implementation. We investigated the utility of artificial intelligence (AI) for streamlining and enhancing the value of borescope inspection of endoscope working channels. Methods We applied a deep learning AI approach to borescope inspection videos of the working channels of 20 endoscopes in use at our academic institution. We evaluated the sensitivity, accuracy, and reliability of this software for detection of endoscope working channel findings. Results Overall sensitivity for AI-based detection of borescope inspection findings identified by gold standard endoscopist inspection was 91.4 %. Labels were accurate for 67 % of these working channel findings and accuracy varied by endoscope segment. Read-to-read variability was noted to be minimal, with test-retest correlation value of 0.986. Endoscope type did not predict accuracy of the AI system ( P = 0.26). Conclusions Harnessing the power of AI for detection of endoscope working channel damage and residue could enable sterile processing department technicians to feasibly assess endoscopes for working channel damage and perform endoscope reprocessing surveillance. Endoscopes that accumulate an unacceptable level of damage may be flagged for further manual evaluation and consideration for manufacturer evaluation/repair.

Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model.

A Systematic Methodology for Developing Bowtie in Risk Assessment: Application to Borescope Inspection

Background—Bowtie analysis is a broadly used tool in risk management to identify root causes and consequences of hazards and show barriers that can prevent or mitigate the events to happen. Limitations of the method are reliance on judgement and an ad hoc development process. Purpose—Systematic approaches are needed to identify threats and consequences, and to ascertain mitigation and prevention barriers. Results—A new conceptual framework is introduced by combining the Bowtie method with the 6M structure of Ishikawa to categorise the threats, consequences and barriers. The method is developed for visual inspection of gas turbine components, for which an example is provided. Originality—Provision of a more systematic methodology has the potential to result in more comprehensive Bowtie risk assessments, with less chance of serious omissions. The method is expected to find application in the broader industry, and to support operators who are non-risk experts but have application-specific knowledge, when performing Bowtie risk assessment.

Bowtie Methodology for Risk Analysis of Visual Borescope Inspection during Aircraft Engine Maintenance

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.

Development of borescope inspection apparatus for reliability enhancement of gas turbine hot gas path parts

Development of borescope inspection apparatus for reliability enhancement of gas turbine hot gas path parts