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Open AccessArticle

Collecting Labels for Rare Anomalies via Direct Human Feedback—An Industrial Application Study

1
Department of Electrical Engineering and Computer Science, University of Siegen, 57068 Siegen, Germany
2
Corporate Research, Robert Bosch GmbH, 71272 Renningen, Germany
*
Author to whom correspondence should be addressed.
Informatics 2019, 6(3), 38; https://doi.org/10.3390/informatics6030038
Received: 29 July 2019 / Revised: 23 August 2019 / Accepted: 29 August 2019 / Published: 2 September 2019
Many systems rely on the expertise from human operators, who have acquired their knowledge through practical experience over the course of many years. For the detection of anomalies in industrial settings, sensor units have been introduced to predict and classify such anomalous events, but these critically rely on annotated data for training. Lengthy data collection campaigns are needed, which tend to be combined with domain expert annotations of the data afterwards, resulting in costly and slow process. This work presents an alternative by studying live annotation of rare anomalous events in sensor streams in a real-world manufacturing setting by experienced human operators that can also observe the machinery itself. A prototype for visualization and in situ annotation of sensor signals is developed with embedded unsupervised anomaly detection algorithms to propose signals for annotation and which allows the operators to give feedback on the detection and classify anomalous events. This prototype allowed assembling a corpus of several weeks of sensor data measured in a real manufacturing surrounding and was annotated by domain experts as an evaluation basis for this study. The evaluation of live annotations reveals high user motivation after getting accustomed to the labeling prototype. After this initial period, clear anomalies with characteristic signal patterns are detected reliably in visualized envelope signals. More subtle signal deviations were less likely to be confirmed an anomaly due to either an insufficient visibility in envelope signals or the absence of characteristic signal patterns. View Full-Text
Keywords: human-machine interaction; anomaly detection; data annotation; industrial manufacturing human-machine interaction; anomaly detection; data annotation; industrial manufacturing
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Reich, C.; Mansour, A.; Van Laerhoven, K. Collecting Labels for Rare Anomalies via Direct Human Feedback—An Industrial Application Study. Informatics 2019, 6, 38.

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