Model-free Anomaly Detection for Dynamical Systems with Gaussian Processes

TL;DR

This paper introduces a model-free anomaly detection method for dynamical systems using Gaussian processes trained on nominal data, capable of online detection despite noise.

Contribution

It proposes a novel Gaussian process-based approach for anomaly detection that does not rely on explicit system models, suitable for noisy real-world data.

Findings

Effective detection of anomalies in two dynamical systems

Robustness to process and measurement noise demonstrated

Threshold-based method controls false positive rate

Abstract

In this paper we address the problem of detecting differences or anomalies in a dynamical system, based on historical data of nominal operations. This problem encompasses quality control, where newly manufactured systems are tested against desired nominal operations, and the detection of changes in the dynamics due to degradation or repairs. We propose a model free approach based on Gaussian processes (GPs). The idea is to train offline a GP based on nominal data, which is then deployed online to detect whether measurements of the system state are compatible with nominal operations or if they deviate. Detecting this deviation is made more challenging by the presence of process and measurement noise, which might obfuscate deviations in the dynamics. The detection then is based on a threshold that ensures a specific false positive rate. We showcase the promising performance of the proposed method with two systems, and highlight several interesting future research questions.