Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data

摘要

This paper presents an application of dynamical linear models for anomaly detection and condition monitoring of ship machinery systems based onmultivariate sensor data. Various model alternatives are specified and fitted to a set of training data before they are applied to a test set. Sequential monitoringbased on statistical tests are applied to detect model breakdown as an indication of deviation from normal conditions. The framework is very flexible andallows for a range of different candidate models to be specified. In this paper, some of the estimated models perform rather poorly, but the best ones do quitewell in flagging anomalies in the data streams. Hence, it is demonstrated that dynamical linear models may be utilized for anomaly detection and conditionmonitoring with multivariate sensor data. However, identification of the best model structure is challenging and requires representative training data andcareful consideration in the model specification.