  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 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.
