Anomaly Detection Using Dynamical Linear Models and Sequential Testing on a Marine Engine System



Published Oct 2, 2017
Erik Vanem Geir Olve Storvik


This paper presents a study on the use of Dynamical Linear Models for anomaly detection and condition monitoring of a marine engine system. Various sensors are installed at different places within the engine system and records essential parameters such as power output from the engine, engine speed, bearing temperatures and various other temperatures, speeds and pressures for selected engine components. The idea is to utilize the information in these sensor signals in order to monitor the condition of the engine. Such a condition monitoring system should include means of fault detection, diagnosis and prognostics, where robust anomaly detection is a prerequisite for reliable management of the system. Dynamical
Linear Models (DLM) constitute a flexible framework for modelling of sensor signals, where the sensor signals are modelled conditional on some latent states, and the model provides forecasts of the signals that can be compared to new sensor readings. Statistical sequential model testing will then be performed on the forecast errors and model breakdown can be an indication of deviation from normal conditions and possible
impending failures of the engine system. This will then call for further diagnostics and prognostics tasks to interpret the nature of the deviations. The Dynamical Linear Model framework can accommodate a range of candidate models. However, very complicated models in high dimensions may be computationally expensive to estimate and apply, so various pre-processing techniques are investigated in this paper to improve model performance, including simple regression models, cluster analysis and principal component transformation.

How to Cite

Vanem, E., & Storvik, G. O. (2017). Anomaly Detection Using Dynamical Linear Models and Sequential Testing on a Marine Engine System. Annual Conference of the PHM Society, 9(1).
Abstract 231 | PDF Downloads 153



Condition monitoring, Time series analysis, Marine applications, Fault detection, Data driven models

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