Handling Data Imbalance in Predictive Maintenance for Machines using SMOTE-based Oversampling

S. Sridhar, Sowmya Sanagavarapu
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引用次数: 7

Abstract

The identification of failures and defects in industrial machines has proven to be a challenge to gauge their warranty and performance. Depreciation in industrial machines occurs due to several factors, most commonly- tool wear, strain, heat and power failure. In this paper, the development of machine learning algorithms for the prediction of machine failures is done. A synthesized dataset was used in the predictive maintenance model, that reflects real-time failures encountered in the industries. The class data imbalance hinders the performance of machine learning algorithms and this is handled by evaluating SMOTE-based oversampling techniques. By using SMOTE technique, a 7.83 % increase in the AUC score is observed, thereby improving the performance of the Random Forest classifier in distinguishing the instances of non-failure and machine failures.
基于smote的过采样处理机器预测维护中的数据不平衡
工业机器的故障和缺陷的识别已被证明是衡量其保修和性能的一个挑战。工业机器的折旧是由几个因素引起的,最常见的是刀具磨损、应变、热和电源故障。本文对机器故障预测的机器学习算法进行了研究。在预测维护模型中使用了一个综合数据集,该数据集反映了工业中遇到的实时故障。类数据不平衡阻碍了机器学习算法的性能,这是通过评估基于smote的过采样技术来处理的。通过使用SMOTE技术,观察到AUC得分提高了7.83%,从而提高了随机森林分类器在区分非故障和机器故障实例方面的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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