  This paper focuses on anomaly detection for multivariate time series data in
large-scale fluid handling plants with dynamic components, such as power
generation, water treatment, and chemical plants, where signals from various
physical phenomena are observed simultaneously. In these plants, the need for
anomaly detection techniques is increasing in order to reduce the cost of
operation and maintenance, in view of a decline in the number of skilled
engineers and a shortage of manpower. However, considering the complex behavior
of high-dimensional signals and the demand for interpretability, the techniques
constitute a major challenge. We introduce a Two-Stage AutoEncoder (TSAE) as an
anomaly detection method suitable for such plants. This is a simple autoencoder
architecture that makes anomaly detection more interpretable and more accurate,
in which based on the premise that plant signals can be separated into two
behaviors that have almost no correlation with each other, the signals are
separated into long-term and short-term components in a stepwise manner, and
the two components are trained independently to improve the inference
capability for normal signals. Through experiments on two publicly available
datasets of water treatment systems, we have confirmed the high detection
performance, the validity of the premise, and that the model behavior was as
intended, i.e., the technical effectiveness of TSAE.
