Abstract
| Language | English |
|---|---|
| Title of host publication | Proceedings of the 2018 Smart Ship Technology Conference |
| Place of Publication | London |
| Publisher | Royal Institution of Naval Architects |
| Number of pages | 8 |
| Publication status | Published - 24 Jan 2018 |
| Event | Smart Ship Technology 2018 - London, United Kingdom Duration: 23 Jan 2018 → 24 Jan 2018 |
Conference
| Conference | Smart Ship Technology 2018 |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 23/01/18 → 24/01/18 |
Fingerprint
Keywords
- data clustering
- marine engine
- FMEA
- condition monitoring
- self organising maps
Cite this
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Implementing unsupervised learning algorithm for marine engine data clustering applications. / Raptodimos, Yiannis; Lazakis, Iraklis.
Proceedings of the 2018 Smart Ship Technology Conference. London : Royal Institution of Naval Architects, 2018.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Implementing unsupervised learning algorithm for marine engine data clustering applications
AU - Raptodimos, Yiannis
AU - Lazakis, Iraklis
PY - 2018/1/24
Y1 - 2018/1/24
N2 - Data preparation and processing is of great importance in a ship condition monitoring tool, as inaccurate and misinterpretation of data can significantly affect the condition monitoring accuracy and performance. Data for performance parameters related to the case study of a Panamax container ship main engine are clustered using an artificial neural network, the Self-Organizing Map (SOM). Neighbouring clusters are compared through a distance metric to examine the existence of data similarities. Additionally, the SOM has a supplementary functionality of identifying data clusters exceeding thresholds, consequently providing diagnostics connected to a Failure Mode and Effects Analysis (FMEA) for the main engine, providing valuable insight and information regarding potential faults. The SOM model is validated through actual data extracted from the case study. Moreover, simulated data representing data exceeding alarm levels for the engine fuel oil system demonstrate the capabilities of the SOM clustering process in combination with the associated FMEA results.
AB - Data preparation and processing is of great importance in a ship condition monitoring tool, as inaccurate and misinterpretation of data can significantly affect the condition monitoring accuracy and performance. Data for performance parameters related to the case study of a Panamax container ship main engine are clustered using an artificial neural network, the Self-Organizing Map (SOM). Neighbouring clusters are compared through a distance metric to examine the existence of data similarities. Additionally, the SOM has a supplementary functionality of identifying data clusters exceeding thresholds, consequently providing diagnostics connected to a Failure Mode and Effects Analysis (FMEA) for the main engine, providing valuable insight and information regarding potential faults. The SOM model is validated through actual data extracted from the case study. Moreover, simulated data representing data exceeding alarm levels for the engine fuel oil system demonstrate the capabilities of the SOM clustering process in combination with the associated FMEA results.
KW - data clustering
KW - marine engine
KW - FMEA
KW - condition monitoring
KW - self organising maps
UR - https://www.rina.org.uk/Smart_Ships2018.html
M3 - Conference contribution book
BT - Proceedings of the 2018 Smart Ship Technology Conference
PB - Royal Institution of Naval Architects
CY - London
ER -