Unsupervised spatio-temporal state estimation for fine-grained anomaly diagnosis of cyber-physical systems - SUN Haili; HUANG Yan; HAN Lansheng; ZHOU Chunjie (Tongxin xuebao)
To reveal the spatio-temporal dependence and evolution mechanisms in cyber-physical system operational states, a fine-grained adaptive multivaviate time series anomaly diagnosis (MAD-Transformer) model was proposed for identifying and diagnosing anomalies in multivariate time series (MTS). MAD-Transformer first constructed temporal state matrixes to characterize and estimate the evolutionary patterns of system states along the time dimension. Secondly, to locate the anomalies, spatial state matrixes were constructed to capture the inter-sensor state correlation. Subsequently, a triple-branch sequence-temporal-spatial attention module was designed to simultaneously capture the sequential, temporal, and spatial dependencies among MTS. Afterwards, three associated alignment loss functions and a reconstruction loss were constructed to jointly optimize the model. The experimental results show that the MAD-Transformer can not only accurately detect and locate the anomaly, but also fine-grained diagnose the duration of the anomaly.