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Neighbor Retrieval Visualizer for Monitoring Lifting Cranes

  • Paul HoneineEmail author
  • Samira Mouzoun
  • Mario Eltabach
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)

Abstract

Gear wear is hard to monitor in lifting cranes due to the difficulties to provide appropriate models of such complex systems with varying functioning modes. Statistical machine learning offers an elegant framework to circumvent these difficulties. This work explores recent advances in statistical machine learning to provide a data-driven model-free approach to monitor lifting cranes, by investigating a large number of indicators extracted from vibration signals. The principal contributions of this paper are twofold. Firstly, it explores the recently introduced Neighbor Retrieval Visualizer (NeRV) method for nonlinear information retrieval. The extracted information allows to construct a low-dimensional representation space that faithfully depicts the evolution of the system. Secondly, it proposes a simple and efficient detection method to detect abnormal evolution and abrupt changes of the system at hand, using the distance measure with neighborhood retrieval in the same spirit as NeRV. The relevance of the proposed methods, for visualizing the evolution and detecting abnormality, is demonstrated with experiments conducted on real data acquired on a lifting crane benchmark operating for almost two years with more than fifty indicators extracted from vibration signals.

Keywords

Nonlinear information retrieval Neighbor Retrieval Visualizer Dimensionality reduction Gear wear monitoring Monitoring lifting cranes Detect abnormal evolution Abrupt change detection 

Notes

Acknowledgements

This work is supported by the commission MLS (Manutention, Levage et Stockage) of CETIM.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LITIS, Université de Rouen NormandieSaint Etienne du RouvrayFrance
  2. 2.Centre Technique des Industries Mécaniques, CETIMSenlisFrance

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