数据分析和基于机器学习的实时生产建模

S. ChandraPrabha, S. Kantha Lakshmi
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引用次数: 0

摘要

本文主要关注使用线性回归和决策树算法进行数据分析和实时数据建模,这些算法可能会对生产数据进行革命性的预测。实际时间数据点包括温度、负荷、警告,在所有呈现的轴上都是依赖参数,这些参数取决于负荷等自主参数的变化。工业中非常需要监测和创新预测,因为反复出现的负荷变化会产生数据漂移,而维护方面可能会影响生产端,因为需要持续监测和控制,基于机器学习的方法在这些实时生产数据集上工作得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data analysis and machine learning-based modeling for real-time production
This article primarily focuses on data analysis and real time data modelling using linear regression and decision tree algorithm that might make revolutionary prediction on production data. Factual time data points include temperature, load, warning, on all the presented axis are the dependent parameters which be contingent on the changes in the autonomous paraments like load. Monitoring and innovative prediction is very much needed in industry as there are recurrent load changes that would create an data drift and in term of maintenance that could impact the production side as need of continues monitoring and control machine learning based approaches would work better on these real time production datasets.
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