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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Machine learning approach for risk-based inspection screening assessment
ترجمه فارسی عنوان مقاله:
روش یادگیری ماشین برای ارزیابی غربالگری بازرسی مبتنی بر ریسک
منبع:
Sciencedirect - Elsevier - Reliability Engineering and System Safety, 185 (2019) 518-532: doi:10:1016/j:ress:2019:02:008
نویسنده:
Andika Rachman⁎, R.M. Chandima Ratnayake
چکیده انگلیسی:
Risk-based inspection (RBI) screening assessment is used to identify equipment that makes a significant contribution
to the systems total risk of failure (RoF), so that the RBI detailed assessment can focus on analyzing
higher-risk equipment. Due to its qualitative nature and high dependency on sound engineering judgment,
screening assessment is vulnerable to human biases and errors, and thus subject to output variability and
threatens the integrity of the assets. This paper attempts to tackle these challenges by utilizing a machine
learning approach to conduct screening assessment. A case study using a dataset of RBI assessment for oil and gas
production and processing units is provided, to illustrate the development of an intelligent system, based on a
machine learning model for performing RBI screening assessment. The best performing model achieves accuracy
and precision of 92.33% and 84.58%, respectively. A comparative analysis between the performance of the
intelligent system and the conventional assessment is performed to examine the benefits of applying the machine
learning approach in the RBI screening assessment. The result shows that the application of the machine learning
approach potentially improves the quality of the conventional RBI screening assessment output by reducing
output variability and increasing accuracy and precision.
Keywords: Risk-based inspection | Machine learning | Screening assessment | Knowledge transfer and reuse | Risk assessment
قیمت: رایگان
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