Anomaly detection and root cause analysis using convolutional autoencoders: A real case study

Abstract

Anomaly detection is the process of identifying unusual patterns in data that may indicate a deviation from the expected norm. This paper proposes a semi-supervised deep learning solution to detect anomalies of a YANMAR energy device that produces heat and power utilizing an internal combustion engine supplied with natural gas. The main equipment of the analysis is a 20 $k{W}_e$ micro-cogeneration unit installed in the energy plant of a facility school. More in detail, the dataset considered in this work consists of 12 features temporally acquired every 15 min. The authors exploit a deep learning architecture, an autoencoder with 1-D convolutional layers to retain temporal correlations, trained to learn the normal behavior of the cogenerator and report unseen operations. In consideration of the fact that autoencoders tend to yield false positives, a Fast-Fourier-Transform-based technique has been applied to filter spurious detections and improve the algorithm’s robustness. As the last contribution, a naive methodology to address the root cause of the anomalies has been explained and its effectiveness has been proved in a real malfunctioning of the CHP.