利用贝叶斯岭回归预测设备整体效能

Mjimer Imane, Es-Saâdia Aoula, E. H. Achouyab
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引用次数: 3

摘要

为了在市场上具有竞争力,每个公司都试图减少生产系统中的各种浪费,因此,公司试图制定用于衡量公司绩效的关键绩效指标,其中一个很好的指标是整体设备效率(OEE)。本研究旨在使用监督机器学习技术来预测OEE,以其处理重要规模数据的能力而闻名,监督学习技术将帮助我们处理数据并了解公司绩效的轨迹。在我们的例子中,我们使用了贝叶斯岭回归算法。应用结果表明,该技术的精度达到99%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Bayesian Ridge Regression to predict the Overall Equipment Effectiveness performance
To be competitive in the market, each company tries to decrease all kind of waste in the production system, for this reason, the company try to put in place key performance indicators used to measure the company performance, one of the good indicators used is the overall equipment effectiveness (OEE). This study aims to predict the OEE using the supervised machine learning techniques, known by their ability to treat an important size of data, the supervised learning techniques will help us to treat the data and know the trajectory of the company performance. In our case, we have used the Bayesian Ridge regression algorithm. The results of applying this technique show a very high accuracy higher than 99%.
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