Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4353
Publisher DOI: 10.1016/j.procir.2022.05.197
Title: Anomaly detection for industrial surface inspection : application in maintenance of aircraft components
Language: English
Authors: Kähler, Falko 
Schmedemann, Ole  
Schüppstuhl, Thorsten  
Keywords: optical inspectiont; anomaly detection; surface defects; machine vision
Issue Date: 26-May-2022
Publisher: Elsevier
Source: Procedia CIRP 107: 246-251 (2022)
Abstract (english): 
Surface defects on aircraft landing gear components represent a deviation from a normal state. Visual inspection is a safety-critical, but recurring task with automation aspiration through machine vision. Various rare occurring faults make acquisition of appropriate training data cumbersome, which represents a major challenge for artificial intelligence-based optical inspection. In this paper, we apply an anomaly detection approach based on a convolutional autoencoder for defect detection during inspection to encounter the challenge of lacking and biased training data. Results indicated the potential of this approach to assist the inspector, but improvements are required for a deployment.
Conference: 55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022 
URI: http://hdl.handle.net/11420/12730
DOI: 10.15480/882.4353
ISSN: 2212-8271
Journal: Procedia CIRP 
Institute: Flugzeug-Produktionstechnik M-23 
Document Type: Article
Project: Modulare sensorbasierte Befundung von Verkehrsflugzeugen 
Funded by: Bundesministerium für Wirtschaft und Klimaschutz (BMWK) 
More Funding information: Research was funded by the German Federal Ministry for Economics and Climate Action under the Program LuFo V-3.
Peer Reviewed: Yes
License: CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives) CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
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