Developments in Hull Strength Monitoring

Developments in Hull Strength Monitoring

  • P. A. Thomson ;
  • Ph. D BMT SeaTech Ltd. ;
  • 발행 : 1996.01.01

초록

Recent Class requirements and IMO recommendations concerning Hull Strength Monitoring (HSM) have prompted an increasing number of shipowner to adopt monitoring systems on bulk carriers and tanker. Such systems are designed to give warning when stress levels and the frequency and magnitude of ship motions approach levels which require corrective action. When fitted these systems provide enhanced operational safety and efficiency. This paper describes a development beyond the standard BMT HSM system through the integration of stress, motion and radar-based sea state monitoring with powerful, on-board, artificial intelligence (AI) tools. The latter utilises conceptual clustering techniques as an aid to pattern recognition in stress, fatigue. motion and sea state data clusters. This, in turn, provides additional operational guidance for ship's staff. Feedback from applications of the standard BMT HSM and extended HSM systems on board the British Steel Bulk Shipping fleet is described.

키워드

참고문헌

  1. Guide for Hull Condition Monitoring System ABS
  2. Radar Evaluation Handbook Barton, D. K.(et al)
  3. Rules for Classification of Ships Surveillance System DnV. Hull
  4. Cluster Analysis Evertt, B
  5. Artificial Intelligence and Statistics Methods of Conceptual Clustering and their Relation to Numerical Taxonomy Fisher, D.P.Langley;W. Gale(ed)
  6. Machine Learning. Vol.2 Knowledge Acquisition via Incremental Conceptual Clustering Fisher, D.
  7. Artificial Intelligence v.40 Method of Incremental Concept Formulation Gennari, J.;Iangloy P;Fisher D.
  8. MARIN Report No. 410777-2-2E Installation of Measuring Equipment on Board M V "Nedlloy Africa", Final Report Jansson, F.A.J.;Huysmans, R.H.M.
  9. Associative Memory : A System Theoretical Approch Kohonen, T.
  10. Provisional Rules for the Classification of Hull Surveillances Systems Lloyds Register
  11. Radar Systems Lynn C;Shin, D.G.
  12. International Journal of Policy Analysis and Information System v.4 Knowledge Acquisition Through Conceptual Clustering: A Theoretical Framework and an Algorithm for Paritioning Data into Conjunctive Concepts Michalski, R.S.
  13. IEEE Transactions on pattern Analysis and Machine Intelligence v.5 Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy Michalski, R.S.;Stepp, R.E.
  14. Progress in Pattern recognition A Recent Advance in Data Analysis: Clusterint Objects into Classes Characterised by Conjunctive Michalski, R.S.;Stepp, R.E.;Diday, E.;L.N. Kannal(ed); A. Rosenfeld(ed)
  15. Operation Mannual for WAVEX Radar Data Capture System
  16. Radar Handbook Skolink, M.I.
  17. Techniques in Computational Learning Thornton, C.J.