Blade Inspection System

Tian, Chen

doi:10.4028/www.scientific.net/amm.423-426.2386

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…In aviation maintenance, several attempts have been made to either aid the operator or fully automate the inspection and repair process by introducing new technologies such as artificial intelligence in combination with robots [15][16][17][18][19] or drones [20,21] for defect detection of aircraft wings and fuselage structures [22][23][24][25][26][27][28], wing fuel tanks [29], tires [30] and composite parts [22,[31][32][33][34]. The inspection and serviceability of engine parts such as shafts [35,36], fan blades [26,37], compressor blades [38,39] and turbine blades [8,[40][41][42][43][44][45] is of particular interest as they are safety-critical, and thus unsurprisingly the most rejected parts during engine maintenance [46]. From a hardware perspective, the automation of inspection and repair processes is commonly done using robots.…”

Section: Advanced Technologiesmentioning

confidence: 99%

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

Aust¹,

Pons²

2022

Applied Sciences

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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.

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Section: Advanced Technologiesmentioning

confidence: 99%

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

Aust¹,

Pons²

2022

Applied Sciences

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“…Several airlines have shown increasing interest in automating visual inspection and tested several systems, including inspection drones for detection of lightning strikes, marking checks and paint quality assurance [27,28], robots with vacuum pads for thermographic crack detection [29] and visual inspection robots for fuselage structures [30]. Engine parts such as shafts [31,32], fan blades [20,33], compressor blades [9,34,35] and turbine blades [9,26,[36][37][38][39][40][41][42][43] are also of particular interest as all are safety-critical to flight operations.…”

Section: Automated Visual Inspection Systems (Avis)mentioning

confidence: 99%

“…Furthermore, most research focuses on defect detection on turbine blades rather than compressor blades. This encompasses mainly crack detection [9,33,37,38,42], as this is the most critical type of defect and the main source of failure. Nonetheless, other types of defects can lead to significant shortage of the part life cycle and propagate towards cracks leading to the same consequences.…”

Section: Gaps In the Body Of Knowledgementioning

confidence: 99%

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

et al. 2021

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Background—In the field of aviation, maintenance and inspections of engines are vitally important in ensuring the safe functionality of fault-free aircrafts. There is value in exploring automated defect detection systems that can assist in this process. Existing effort has mostly been directed at artificial intelligence, specifically neural networks. However, that approach is critically dependent on large datasets, which can be problematic to obtain. For more specialised cases where data are sparse, the image processing techniques have potential, but this is poorly represented in the literature. Aim—This research sought to develop methods (a) to automatically detect defects on the edges of engine blades (nicks, dents and tears) and (b) to support the decision-making of the inspector when providing a recommended maintenance action based on the engine manual. Findings—For a small sample test size of 60 blades, the combined system was able to detect and locate the defects with an accuracy of 83%. It quantified morphological features of defect size and location. False positive and false negative rates were 46% and 17% respectively based on ground truth. Originality—The work shows that image-processing approaches have potential value as a method for detecting defects in small data sets. The work also identifies which viewing perspectives are more favourable for automated detection, namely, those that are perpendicular to the blade surface.

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Blade Inspection System

Cited by 2 publications

References 3 publications

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

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

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

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