Department of Mechanical and Mechatronic Engineering

Permanent URI for this community

https://scholar.sun.ac.za/handle/10019.1/109

Browse

Browsing Department of Mechanical and Mechatronic Engineering by browse.metadata.advisor "Bienert, J."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    System identification and modal tracking on ship structures
    (Stellenbosch : Stellenbosch University, 2018-03) Soal, Keith Ian; Bekker, Annie; Bienert, J.; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH SUMMARY: are currently based mainly on dynamic response feedback. Navigators decide on how to operate the vessel based on how they feel it pitching, heaving, rolling and vibrating. The aim of this thesis is to investigate the idea of using system identification and modal tracking on polar vessels towards the development of a decision aiding system. System identification provides a powerful tool for building mathematical models of dynamic systems. An open source toolbox (openSID) for system identification using Stochastic Subspace Identification (SSI) was developed as a research and learning tool. Full scale measurements were performed on the research vessel Polarstern during an expedition to the Arctic. This is the first comprehensive data set including vibration responses and environmental parameters to span the entire operational profile of a research voyage to the Arctic. System identification successfully identified seven global modes in the bandwidth 2 - 10 Hz. Comparisons between different methods were used to cross validate results. A modal tracking algorithm was developed and relationships between identified modes and system inputs were observed. A novel method is developed to improve the uncertainty and sensitivity of system identification and tracking, based on a data driven statistical model and a Kalman filter. A key objective is to make experimental data maximally informative by using additional system inputs. The model was found to accurately re-create the training data set and was used to make predictions based on future system inputs. The Kalman filter estimates were observed to produce balanced and consistent results. These results demonstrate the potential of an ice force estimation and structural health monitoring system.