(3)A computationally efficient method for identification of steady state in time series data from ship monitoring
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Øyvind Øksnes Dalheima,b,∗, Sverre Steena,b
a Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Otto Nielsens vei 10, Trondheim 7491, Norway
bRolls-Royce1 University Technology Centre (UTC) – Ship performance and cyber-physical systems, Trondheim, Norway
Received 21 October 2019; received in revised form 20 January 2020; accepted 23 January 2020
Available online 7 February 2020
Abstract
Most applied time series are non-stationary, or exhibit some kind of non-stationarity for at least parts of the time series. For time series
analyses or mathematical modeling purposes, the non-stationarities can be difficult to handle. Therefore, identification of stationary and
non-stationary behavior is of great practical interest in time series analysis. In this study a robust and computationally efficient method to
identify steady state parts of time series data is presented. The method is based on the class of deterministic trend models using a sliding
window, and is focused towards being easy to implement, efficient and practical in use and to preserve data completeness. To demonstrate the
performance of the steady state identifier, the method is applied on different sets of time series data from two ships equipped with systems
for in-service monitoring. The method is shown to be reliable and practical for identifying steady state parts of time series data, and can
serve as a practical preprocessing tool for time series data analysis.
© 2020 Shanghai Jiaotong University. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: Stationarity; Steady state; Signal extraction; Change point; In-service data; Ship monitoring.