Anomaly detection using a model-agnostic meta-learning-based variational auto-encoder for facility management

Abstract

In a smart building, various types of sensor data are generated and employed to control devices within the building. Detecting anomalies in these sensor data is critical for effective facility management because it can prevent device malfunction or failure. Although deep learning-based methods have been used to detect anomalies, effective model construction is challenging because abnormal data tend to be rare under real-world conditions. In image classification and load forecasting, model-agnostic meta-learning (MAML) has recently been proposed to alleviate this problem using common knowledge learned from various tasks. This paper proposes an MAML-based unsupervised anomaly detection method called MAVAE for time-series sensor data. The proposed method uses a variational auto-encoder (VAE) as an anomaly detection model and adapts the model to a new target task with few unlabeled anomaly data via MAML. To our knowledge, this is the first study to train a VAE using MAML with time-series data. Extensive experiments on public data reveals that the proposed method outperforms existing anomaly detection methods, achieving an average improvement in prediction performance of 45% compared to state-of-the-art methods. The robustness of the proposed method is also demonstrated by evaluating its performance with a different number of samples for MAML. Our code is available at github.com/17011813/MAVAE.