Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems

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Published Jun 29, 2022
Stephanie Holly Robin Heel Denis Katic Leopold Schoeffl Andreas Stiftinger Peter Holzner Thomas Kaufmann Bernhard Haslhofer Daniel Schall Clemens Heitzinger Jana Kemnitz

Abstract

Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies on expert knowledge and thresholds. However, data is viewed isolated and complex, multivariate relationships are neglected. In this work, we present an autoencoder based end-to-end workflow for anomaly detection suitable for multivariate time series data in large industrial cooling systems, including explained fault localization and root cause analysis based on expert knowledge. We identify system failures using a threshold on the total reconstruction error (autoencoder reconstruction error including all sensor signals). For fault localization, we compute the individual reconstruction error (autoencoder reconstruction error for each sensor signal) allowing us to identify the signals that contribute most to the total reconstruction error. Expert knowledge is provided via look-up table enabling root-cause analysis and assignment to the affected subsystem. We demonstrated our findings in a cooling system unit including 34 sensors over a 8-months’ time period using 4-fold cross validation approaches and automatically created labels based on thresholds provided by domain experts. Using 4-fold cross validation, we reached a F1-score of 0.56, whereas the autoencoder results showed a higher consistency score (CS of 0.92) compared to the automatically created labels (CS of 0.62) – indicating that the
anomaly is recognized in a very stable manner. The automatically created labels, however, detected anomaly earlier. The main anomaly was found by the autoencoder and automatically created labels, and was also recorded in the log files. Further, the explained fault localization highlighted the most affected component for the main anomaly in a very consistent manner.

How to Cite

Holly, S. ., Heel, R. ., Katic, D., Schoeffl, L. ., Stiftinger, A. ., Holzner, P. ., Kaufmann, T. ., Haslhofer, B. ., Schall, D. ., Heitzinger, C. ., & Kemnitz, J. . (2022). Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems. PHM Society European Conference, 7(1), 200–210. https://doi.org/10.36001/phme.2022.v7i1.3349
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Keywords

Anomaly Detection, Explained Fault, multivariate time series data, expert knowledge, autoencoder reconstruction, cooling system

Section
Technical Papers