Qasim Khadim, Yashar Shabbouei Hagh, Dezhi Jiang, Lauri Pyrhönen, Suraj Jaiswal, Victor Zhidchenko, Xinxin Yu, Emil Kurvinen, Heikki Handroos, Aki Mikkola, Experimental investigation into the state estimation of a forestry crane using the unscented Kalman filter and a multiphysics model, Mechanism and Machine Theory, Volume 189, 2023, 105405, ISSN 0094-114X, https://doi.org/10.1016/j.mechmachtheory.2023.105405
Experimental investigation into the state estimation of a forestry crane using the unscented Kalman filter and a multiphysics model
|Author:||Khadim, Qasim1; Hagh, Yashar Shabbouei2; Jiang, Dezhi3;|
1Laboratory of Machine and Vehicle Design, Department of Mechanical Engineering, University of Oulu, Oulu, Finland
2Laboratory of Intelligent Machines, Department of Mechanical Engineering, LUT University, Lappeenranta, Finland
3Laboratory of Machine Design, Department of Mechanical Engineering, LUT University, Lappeenranta, Finland
4Department of Mechanical and Manufacturing Engineering, University of Seville, Seville, Spain
|Online Access:||PDF Full Text (PDF, 4.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230921135086
|Publish Date:|| 2023-09-21
To increase productivity, reduce energy use, and minimize unplanned maintenance, manufacturers of heavy machinery must instrument their products. As explained in the literature, state and parameter estimators can successfully integrate machine sensor signals with simulation results from computational models. This leads to comparable or improved observations even when fewer sensors are being used. This study introduces a state observer based on the unscented Kalman filter for the coupled mechanical and hydraulic systems. The resulting reality-driven simulation procedure is applied to a hydraulically actuated forestry crane that has been instrumented to provide the necessary sensor information. This study analyzes the performance of state observer in four different scenarios and recommends an optimal sensor configuration for the application. Estimation accuracy of observer in the simulation of the mechanics and hydraulics components is evaluated using the percent normalized root mean square error (PN-RMSE) and 95% confidence interval.
Mechanism and machine theory
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
214 Mechanical engineering
216 Materials engineering
This work was supported by the Business Finland [4329/31/2019].
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0).