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

Type de document

Études primaires

Année de publication

2017

Langue

Anglais

Titre de la revue

Reliability Engineering & System Safety

Première page

223

Dernière page

236

Résumé

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.

Mots-clés

Analyse des données, Data analysis, Convoyeur à courroie, Belt conveyor, Risque mécanique, Mechanical hazard, Documentation de la prévention, Safety and health documentation, Hiérarchie des mesures de prévention, Hierarchy of controls, Base de données, Data base

Numéro de projet IRSST

n/a

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