Torque estimation in marine propulsion systems

Mikael Manngård*, Ivar Koene, Wictor Lund, Sampo Haikonen, Fredrik A. Fagerholm, Michał Wilczek, Konrad Mnich, Joni Keski-Rahkonen, Raine Viitala, Jerker Björkqvist, Hannu T. Toivonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

An augmented Kalman filter for torque estimation in marine propulsion-system drive trains is presented. Propeller and motor excitations and torque responses are estimated based on a dynamical model of the system and inboard shaft measurements. Input excitations affecting marine propulsion systems are signals whose statistical properties vary between finite time intervals. Hence, in this paper, excitations are characterized as quasi-stationary signals with bounded power spectral density. Given that upper bounds on the spectral densities are known prior to estimation, it is shown that a linear time-invariant input-and-state observer, minimizing the worst-case power of the estimation errors, can be synthesized by conventional Kalman-filtering techniques. Experiments have been conducted on a laboratory-scale test bench to assess the applicability of the proposed observer for use in marine propulsion systems. The test bench was built to emulate the behavior of a full-scale propulsion system operated in ice and other high load conditions. Estimation results from a full-size underwater mountable azimuthing thruster are also presented. Experiment results show that torque excitations and torque responses at all locations of interest on the engine-propeller drivetrain can be estimated with high accuracy based on a few indirect measurements at convenient locations on the motor shaft.

Original languageEnglish
Article number108969
Number of pages18
JournalMechanical Systems and Signal Processing
Volume172
DOIs
Publication statusPublished - 1 Jun 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Maritime systems
  • Optimal filtering
  • Simultaneous input and state estimation

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