کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6974953 | 1453367 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimation of probability of harm in safety of machinery using an investigation systemic approach and Logical Analysis of Data
ترجمه فارسی عنوان
ارزیابی احتمال آسیب در ایمنی ماشین آلات با استفاده از رویکرد سیستماتیک تحقیق و تحلیل منطقی داده ها
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بهداشت و امنیت شیمی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Safety Science - Volume 105, June 2018, Pages 32-45
Journal: Safety Science - Volume 105, June 2018, Pages 32-45
نویسندگان
Sabrina Jocelyn, Mohamed-Salah Ouali, Yuvin Chinniah,