Nikula, Riku-Pekka; Ruusunen, Mika; Keski-Rahkonen, Joni; Saarinen, Lars; Fagerholm, Fredrik. 2021. "Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements" Machines 9, no. 2: 39. https://doi.org/10.3390/machines9020039
Probabilistic condition monitoring of azimuth thrusters based on acceleration measurements
|Author:||Nikula, Riku-Pekka1; Ruusunen, Mika1; Keski-Rahkonen, Joni2;|
1Control Engineering, Environmental and Chemical Engineering, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland
2Kongsberg Maritime Finland Oy, P.O. Box 220, 26101 Rauma, Finland
|Online Access:||PDF Full Text (PDF, 8.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102255958
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2021-02-25
Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities. The versatile operating modes and the challenging marine environment create demand for flexible and practical condition monitoring solutions onboard. This study introduces a condition monitoring algorithm using acceleration and shaft speed data to detect anomalies that give information on the defects in the driveline components of the thrusters. Statistical features of vibration are predicted with linear regression models and the residuals are then monitored relative to multivariate normal distributions. The method includes an automated shaft speed selection approach that identifies the normal distributed operational areas from the training data based on the residuals. During monitoring, the squared Mahalanobis distance to the identified distributions is calculated in the defined shaft speed ranges, providing information on the thruster condition. The performance of the method was validated based on data from two operating thrusters and compared with reference classifiers. The results suggest that the method could detect changes in the condition of the thrusters during online monitoring. Moreover, it had high accuracy in the bearing condition related binary classification tests. In conclusion, the algorithm has practical properties that exhibit suitability for online application.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
214 Mechanical engineering
This research was funded by Business Finland during the Reboot IoT Factory program.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).