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Application of logical analysis of data to machinery-related accident prevention based on scarce data

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  • Jocelyn, Sabrina
  • Chinniah, Yuvin
  • Ouali, Mohamed-Salah
  • Yacout, Soumaya

Abstract

This paper deals with the application of Logical Analysis of Data (LAD) to machinery-related occupational accidents, using belt-conveyor-related accidents as an example. LAD is a pattern recognition and classification approach. It exploits the advancement in information technology and computational power in order to characterize the phenomenon under study. The application of LAD to machinery-related accident prevention is innovative. Ideally, accidents do not occur regularly, and as a result, companies have little data about them. The first objective of this paper is to demonstrate the feasibility of using LAD as an algorithm to characterize a small sample of machinery-related accidents with an adequate average classification accuracy. The second is to show that LAD can be used for prevention of machinery-related accidents. The results indicate that LAD is able to characterize different types of accidents with an average classification accuracy of 72–74%, which is satisfactory when compared with other studies dealing with large amounts of data where such a level of accuracy is considered adequate. The paper shows that the quantitative information provided by LAD about the patterns generated can be used as a logical way to prioritize risk factors. This prioritization helps safety practitioners make decisions regarding safety measures for machines.

Suggested Citation

  • Jocelyn, Sabrina & Chinniah, Yuvin & Ouali, Mohamed-Salah & Yacout, Soumaya, 2017. "Application of logical analysis of data to machinery-related accident prevention based on scarce data," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 223-236.
    • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:223-236
    DOI: 10.1016/j.ress.2016.11.015
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    References listed on IDEAS

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    1. Peter Hammer & Tibérius Bonates, 2006. "Logical analysis of data—An overview: From combinatorial optimization to medical applications," Annals of Operations Research, Springer, vol. 148(1), pages 203-225, November.
    2. Sorin Alexe & Eugene Blackstone & Peter Hammer & Hemant Ishwaran & Michael Lauer & Claire Pothier Snader, 2003. "Coronary Risk Prediction by Logical Analysis of Data," Annals of Operations Research, Springer, vol. 119(1), pages 15-42, March.
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    4. Rivas, T. & Paz, M. & Martín, J.E. & Matías, J.M. & García, J.F. & Taboada, J., 2011. "Explaining and predicting workplace accidents using data-mining techniques," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 739-747.
    5. M. W. Brauner & N. Brauner & P. L. Hammer & I. Lozina & D. Valeyre, 2007. "Logical Analysis of Computed Tomography Data to Differentiate Entities of Idiopathic Interstitial Pneumonias," Springer Optimization and Its Applications, in: Panos M. Pardalos & Vladimir L. Boginski & Alkis Vazacopoulos (ed.), Data Mining in Biomedicine, pages 193-208, Springer.
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    Cited by:

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    2. Lejeune, Miguel & Lozin, Vadim & Lozina, Irina & Ragab, Ahmed & Yacout, Soumaya, 2019. "Recent advances in the theory and practice of Logical Analysis of Data," European Journal of Operational Research, Elsevier, vol. 275(1), pages 1-15.
    3. Kedong Yan & Hong Seo Ryoo, 2022. "Graph, clique and facet of boolean logical polytope," Journal of Global Optimization, Springer, vol. 82(4), pages 1015-1052, April.
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    5. Guo, Cui & Ryoo, Hong Seo, 2021. "On Pareto-Optimal Boolean Logical Patterns for Numerical Data," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    6. Kedong Yan & Dongjing Miao & Cui Guo & Chanying Huang, 2021. "Efficient feature selection for logical analysis of large-scale multi-class datasets," Journal of Combinatorial Optimization, Springer, vol. 42(1), pages 1-23, July.

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