Presentation + Paper
31 May 2022 Robust counting of engine blades in aircraft engine video endoscopy
Author Affiliations +
Abstract
To ensure the safe operation of aircraft, regular endoscopy of the engines is mandatory. Since the blade stages are particularly susceptible to defects, they must be inspected especially frequently. In the process, a worker must inspect each blade individually. All findings must be carefully documented and assigned to the respective blade. Since there are no individual markings to identify the blades, the operator must count all blades as they pass through the endoscope image. Although electric rotary devices with automatic blade counting are available for some engines, manual counting is often necessary. Simultaneously inspecting and counting blades is tedious and error-prone. We present a novel algorithm for automatic blade counting during jet engine inspection in this paper. The algorithm’s central part is a Pearson correlation of individual video frames as the blades pass before the endoscopes during turning. Adaptive thresholding of the correlation function is used to count the blades. Rotation direction and speed are determined using the Farneback method of optical flow. By using correlation instead of classical image features, the algorithm is highly robust to metallic reflections and smooth blade surfaces without significant image features. In addition, the algorithm is robust to different rotation speeds and directions. Compared to existing approaches, the algorithm is robust and universally applicable for counting engine blades on almost any engine without the need for customization.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Moritz v. Wrangel, Markus Kästner, and Eduard Reithmeier "Robust counting of engine blades in aircraft engine video endoscopy", Proc. SPIE 12098, Dimensional Optical Metrology and Inspection for Practical Applications XI, 120980C (31 May 2022); https://doi.org/10.1117/12.2618504
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KEYWORDS
Inspection

Video

Cameras

Correlation function

Detection and tracking algorithms

Optical flow

Endoscopes

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