26–30 Jun 2022
Europe/Berlin timezone

Statistical Monitoring for Failure Detection of Royal Netherlands Navy Vessels

Not scheduled
20m
Process

Speaker

Alessandro Di Bucchianico (Eindhoven University of Technology)

Description

Within the PrimaVera (Predictive maintenance for Very effective asset management) project, we carried out a case study on monitoring procedures for failure detection of bearing in diesel engines of ocean-going patrol vessels. Monitoring is based on bearing temperature, since the two most important failure modes (abrasive wear and cavitation) cause an increase in these temperatures.
A regression model to correct the bearing temperatures for external factors was fitted using LASSO variable selection. Monitoring procedures have been developed based on predictive and recursive residuals. A hybrid method consisting of EWMA charts based on a combination of recursive and predictive residuals proved to be effective when applied to historical data, and has the additional feature of being self-starting.
Another effective method that proved to be useful is based on regression adjusted variables. This method is designed to detect when a bearing shows deviant behaviour from what is expected given the other bearings.

Keywords monitoring, regression control charts, contextual anomaly detection

Primary author

Alessandro Di Bucchianico (Eindhoven University of Technology)

Presentation materials

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