Using ship sensor data to achieve smart maintenance?
Creators
- 1. Netherlands Defence Academy
- 2. University of Twente, Faculty of Engineering Technology,
Description
In the present paper, the potential of using ship sensor data for achieving smart maintenance of naval ships is discussed. In modern ships, a lot of installations and equipment contain sensors that monitor and register the operation of these systems. This work will exemplify the value of the (long term) collection and analysis of sensor data. This will mainly focus on assessing the present performance or condition of systems and the detection of faults or failures, but also on the longer term ambition of predicting upcoming failures. The theoretical benefits of sensor data are checked against a real case study of a naval ship sea cooling water system. Different scenarios with increasing amounts of sensors are compared, and the importance of domain and system knowledge is discussed.
Files
INEC_2020_Paper_75.pdf
Files
(1.3 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:1a04e2af868a2d92992e028274c1b99d
|
1.3 MB | Preview Download |
Additional details
References
- Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W., & Ducoffe, M. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92(103678), 1-15.
- Karassik, I. J., Messina, J. P., Cooper, P., & Heald, C. C. (2001). Pump handbook (Vol. 3). New York: McGraw-Hill.
- NSWC. (2011). Handbook of Reliability Prediction Procedures for Mechanical Equipment.West Bethesda: Naval Surface Warfare Center.
- Rijsdijk, C. (2020). Data driven decision support; a maintenance case. In press.
- Rijsdijk, C., & Tinga, T. (2018). Enhanced data driven decision support. In C. Kulkarni, & T. Tinga (Ed.), Proceedings of the European Conference of the PHM Society. 4, p. 409. Utrecht: PHM Society.
- Tiddens,W., Braaksma, A., & Tinga, T. (2015). The adoption of prognostic technologies in maintenance decision making: a multiple case study. Procedia CIRP, 38, 171 – 176.
- Tinga, T. (2010). Application of physical failure models to enable usage and load based maintenance. Reliability engineering & system safety, 95(10), 1061-1075.
- Tinga, T., Wubben, J. P., Tiddens, W. W., Wortmann, J. C., & Gaalman, G. J. (2020). Dynamic Maintenance based on Functional Usage Profiles. Journal of quality in maintenance engineering, Accepted/In press, 1-15.
- Woldman, M., Tinga, T., van der Heide, E., & Masen, M. A. (2015). Abrasive wear based predictive maintenance for systems operating in sandy conditions. Wear, 338-339, 316-324.