Object Dimension Estimation for Remote Visual Inspection in Borescope Systems

doi:10.3837/tiis.2019.08.019

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Therefore the reference geometry is re-sampled into a point cloud pc r . First, each point p i ∈ R 3 of both point clouds (measurement pc m and reference pc r ) within a distance d = 0.5 mm to the identified inlier features (actual film-cooling holes) are selected by means of a range search (10). Using the selected points…”

Section: Data Registrationmentioning

confidence: 99%

“…This allows defect segmentation and classification based on 2D image data using neural networks and support vector machines. The field of automated defect detection and decision support based on 2D image data has therefore already been researched [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Due to a strong miniaturization in the field of camera sensors and fiber optics, the first endoscopic 3D measuring devices were able to be developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Pose Estimation and Damage Characterization of Turbine Blades during Inspection Cycles and Component-Protective Disassembly Processes

Middendorf¹,

Blümel²,

Hinz³

et al. 2022

Sensors

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Inspection in confined spaces and difficult-to-access machines is a challenging quality assurance task and particularly difficult to quantify and automate. Using the example of aero engine inspection, an approach for the high-precision inspection of movable turbine blades in confined spaces will be demonstrated. To assess the condition and damages of turbine blades, a borescopic inspection approach in which the pose of the turbine blades is estimated on the basis of measured point clouds is presented. By means of a feature extraction approach, film-cooling holes are identified and used to pre-align the measured point clouds to a reference geometry. Based on the segmented features of the measurement and reference geometry a RANSAC-based feature matching is applied, and a multi-stage registration process is performed. Subsequently, an initial damage assessment of the turbine blades is derived, and engine disassembly decisions can be assisted by metric geometry deviations. During engine disassembly, the blade root is exposed to high disassembly forces, which can damage the blade root and is crucial for possible repair. To check for dismantling damage, a fast inspection of the blade root is executed using the borescopic sensor.

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Section: Data Registrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pose Estimation and Damage Characterization of Turbine Blades during Inspection Cycles and Component-Protective Disassembly Processes

Middendorf¹,

Blümel²,

Hinz³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…However, this is a manual and time-consuming task, since the inspector has to acquire an image first and then mark the contour points (start and end of defect) between which the distance shall be measured. The situation can be improved by providing a scaled measurement grid [60].…”

Section: Automated Defect Measurementmentioning

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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Object Dimension Estimation for Remote Visual Inspection in Borescope Systems

Cited by 2 publications

References 12 publications

Pose Estimation and Damage Characterization of Turbine Blades during Inspection Cycles and Component-Protective Disassembly Processes

Pose Estimation and Damage Characterization of Turbine Blades during Inspection Cycles and Component-Protective Disassembly Processes

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

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