Coincident Learning for Unsupervised Anomaly Detection

Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. In this paper, we introduce a new method, called CoAD, for training anomaly detection models on unlabeled data, based on the expectation that anomalous behavior in one sub-system will produce coincident anomalies in downstream sub-systems and products. Given data split into two streams $s$ and $q$ (i.e., subsystem diagnostics and final product quality), we define an unsupervised metric, $\hat{F}_\beta$, out of analogy to the supervised classification $F_\beta$ statistic, which quantifies the performance of the independent anomaly detection algorithms on s and q based on their coincidence rate. We demonstrate our method in four cases: a synthetic time-series data set, a synthetic imaging data set generated from MNIST, a metal milling data set, and a data set taken from a particle accelerator.