Hierarchical Anomaly Detection Using a Multioutput Gaussian Process

• Published in: IEEE transactions on automation science and engineering 2020-01, Vol.17 (1), p.261-272
• Main Authors: Cho, Woojin, Kim, Youngrae, Park, Jinkyoo
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Anomaly detection; Correlation; Data analysis; Decision making; Extreme value theory; Extreme value theory (EVT); Extreme values; Gaussian process; Gaussian process regression (GPR); Gaussian processes; Ground penetrating radar; hierarchical anomaly detection (HAD); Identification methods; Mahalanobis distance; Mechanical systems; Monitoring; multioutput Gaussian process; Outliers (statistics); Prediction models; Real time; Regression models; Regulators; Statistical analysis; Time series analysis; Trajectory; Trajectory measurement
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2019.2917887

This paper comprises a description of a data-driven approach to the real-time monitoring of a physical system. Specifically, a hierarchical anomaly detection algorithm that can identify both instantaneous pointwise anomalies and gradual trajectory anomalies is proposed. To detect anomalies, we first construct a multioutput Gaussian process regression (MOGPR) model that can predict, probabilistically, the outputs of the target system. Using the constructed prediction model, we then propose the statistical decision-making strategies to determine the abnormal operations of the target system by comparing its measured and the predicted responses. For pointwise anomaly detection, we regard a single measurement as abnormal if the difference between the measurement and the prediction exceeds the threshold based on an extreme value theory. For the trajectory anomaly detection, we consider a sequence of measurements abnormal if the Mahalanobis distance between the measured and predicted trajectories is highly improbable. The proposed monitoring strategy does both the pointwise and the trajectory anomaly detection in a single framework. The proposed strategy was applied to detecting abnormal operations of gas regulators. Validating with the actual gas regulator data demonstrated that it could identify the anomalies robustly and accurately. Note to Practitioners-This paper was motivated by the problem of a need to detect abnormal operations in industrial gas regulators, but it also applies to other industrial mechanical systems. Existing approaches to identifying abnormal operations in such systems are premised on rule-based methods that issue alarms if the measured response signal from a target system exceeds a certain fixed threshold. We propose an anomaly detection algorithm, constructed on the basis of a multioutput Gaussian process regression that takes into account the correlations of multiple-target time series data. In particular, the proposed method makes it possible to detect both pointwise anomalies (outlier) and trajectory anomalies (i.e., a systemic deviation between the measured and predicted target responses) in a single framework while adapting its prediction model with newly observed data. The proposed method was tested using data recorded from currently operating gas regulators. It was found that this method enabled one to detect the abnormal operations of the target system reliably. In the future research, we will accelerate the learning and adapt t