University of Oulu

Nikula R-P, Ruusunen M, Böhme SA. On Training Data Selection in Condition Monitoring Applications—Case Azimuth Thrusters. Applied Sciences. 2022; 12(8):4024.

On training data selection in condition monitoring applications : case azimuth thrusters

Saved in:
Author: Nikula, Riku-Pekka1; Ruusunen, Mika1; Böhme, Stephan André2
Organizations: 1Control Engineering, Environmental and Chemical Engineering, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland
2Kongsberg Maritime AS, P.O. Box 1522, N-6065 Ulsteinvik, Norway
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 8.9 MB)
Persistent link:
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2022-09-06


Machine learning techniques are commonly used in the vibration-based condition monitoring of rotating machines. However, few research studies have focused on model training from a practical viewpoint, namely, how to select representative training samples and operating areas for monitoring applications. We focus on these aspects by studying training sets with varying sizes and distributions, including their effects on the models to be identified. The analysis is based on acceleration and shaft speed data available from an azimuth thruster of a catamaran crane vessel. The considered machine learning algorithm was previously introduced in another study suggesting it could detect defects on the thruster driveline components. In this work, practical guidance is provided to facilitate its implementation, and furthermore, an adaptive method for training subset selection is proposed. Results show that the proposed method enabled the identification of usable training subsets in general, while the success of the previous approach was case-dependent. In addition, the use of Kolmogorov–Smirnov or Anderson–Darling tests for normal distribution, as a part of the method, enabled selections that covered the operating area broadly, while other tests were unfavorable in this regard. Overall, the study demonstrates that reconfigurable and automated model implementations could be achievable with minor effort.

see all

Series: Applied sciences
ISSN: 2076-3417
ISSN-E: 2076-3417
ISSN-L: 2076-3417
Volume: 12
Issue: 8
Article number: 4024
DOI: 10.3390/app12084024
Type of Publication: A1 Journal article – refereed
Field of Science: 218 Environmental engineering
Funding: This research was funded by Business Finland during Reboot IoT Factory project, grant number 4356/31/2019.
Copyright information: © 2022 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 (