Automated model selection for multivariate anomaly detection in manufacturing systems

As machine learning is widely applied to improve the efficiency and effectiveness of manufacturing systems, the automated selection of appropriate algorithms and hyperparameters becomes increasingly important. This paper presents a model selection approach to multivariate anomaly detection for applications in manufacturing systems using a multi-output regression-based meta-learning method. The proposed method exploits the capabilities of meta-learning to explore and learn the intricate relationships within multivariate data sets in order to select the best anomaly detection model. It also facilitates the construction of an ensemble of algorithms with dynamically assigned weights based on their respective performance levels. In addition to the framework, new meta-features for the application domain are presented and evaluated. Experiments show the proposed method can be successfully applied to achieve significantly better results than benchmark approaches. This enables an automated selection of algorithms that can be used for enhanced anomaly detection under changing operating conditions.