Title: Anomaly Detection with Autoencoders as a Tool for Detecting Sensor Malfunctions
Authors: Liebert, Artur 
Weber, Wolfgang 
Reif, Sebastian
Zimmering, Bernd 
Niggemann, Oliver 
Language: eng
Keywords: dtec.bw
Issue Date: 1-Jan-2022
Document Type: Conference Object
Published in (Book): Proceedings - 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)
Conference: IEEE 5th International Conference on Industrial Cyber-Physical Systems, ICPS 2022
One possibility to extend the service life of engi-neering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distributed at the surface or within the structure to be monitored. Each sensor measures at least one physical quantity, the data of all sensors then have to be properly evaluated to derive the health state and to predict the remaining service life. Health issues may be detected by machine learning methods by looking for anomalous behaviour in sensor data. Hereby the problem is that malfunctions differ excessively in the representation of the data collected by sensors such that specialisation of methods on anomaly types is required. The current contribution suggests the simulation of sensor malfunction based on established criteria by creating different types of artificial anomalous data indicating different types of issues. Several proposed autoencoder approaches are verified for different anomaly representations, which are artificially introduced in a set of data. The final solutions are different autoencoder specialized on different types of simulated anomaly data, making the conclusions drawn from the measured data more reliable. As a case study, data of a numerical experiment of fibre pull-out are considered.
Organization Units (connected with the publication): DTEC.bw 
Informatik im Maschinenbau 
ISBN: 9781665497701
Publisher DOI: 10.1109/ICPS51978.2022.9816908
Appears in Collections:3 - Reported Publications

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