Estimation of probability of harm in safety of machinery using an investigation systemic approach and Logical Analysis of Data
| Autor: | Mohamed-Salah Ouali, Sabrina Jocelyn, Yuvin Chinniah |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Estimation
Data collection Computer science 05 social sciences 0211 other engineering and technologies Public Health Environmental and Occupational Health Context (language use) 02 engineering and technology computer.software_genre Task (project management) Moment (mathematics) Accident (fallacy) Logical analysis of data Harm 021105 building & construction 0501 psychology and cognitive sciences Data mining Safety Risk Reliability and Quality Safety Research computer 050107 human factors |
| Zdroj: | Safety Science. 105:32-45 |
| ISSN: | 0925-7535 |
| DOI: | 10.1016/j.ssci.2018.01.018 |
| Popis: | In safety of machinery, estimating the probability of occurrence of harm is a recurrent problem. This paper proposes and applies a new method to estimate that probability. Information regarding accidents involving machinery that is gathered and analyzed by experts is formatted based on a systemic-inspired model using the MELITO concept. Then, Logical Analysis of Data (LAD) is used to extract knowledge automatically to characterize accidents. MELITO describes the context in which the accident has occurred, gathering information about the moment (M), equipment (E), location (L), individual (I), task (T) and organization (O). LAD is a data mining algorithm that infers knowledge learning from a database. In this paper, a case study consisting of twenty-three fatal and serious accident reports involving belt conveyors is presented. Data on these accidents is classified according to MELITO. The inferred knowledge is presented in the form of interpretable patterns that characterize and distinguish fatalities from non-fatal harm. Each pattern consists of a Boolean equation from MELITO and covers a subset of accidents. Based on each pattern, the probability of the occurrence of harm related to a hazardous situation is estimated. Such probability is useful in monitoring risk behavior after the occurrence of a new accident, for instance. |
| Databáze: | OpenAIRE |
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