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首页> 外文会议>IFAC Symposium on Telematics Applications >Machine Condition Monitoring and Fault Diagnostics with Imbalanced Data Sets based on the KDD Process
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Machine Condition Monitoring and Fault Diagnostics with Imbalanced Data Sets based on the KDD Process

机译：基于KDD进程的Imbalanced数据集机器状态监控和故障诊断

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Machine condition monitoring is a maintenance strategy, which enables real-time diagnostics and prognostics of machine conditions. One major problem of machine fault diagnostics is that data presenting faulty behavior is significantly underrepresented resulting in poor classifier accuracy for the faulty classes. Since misclassification of faulty behavior results in unplanned machine breakdowns and thus economic loss, improved fault diagnostics classifiers handling data imbalance are of importance. This paper addresses the problem of binary and multi-class imbalanced data sets. Based on the structural KDD process for data mining, required steps for imbalanced data sets are defined. The KDD process is evaluated through a real-world industrial case including data sampling and the development of a support vector machine classifier.
机译：机器状态监控是一种维护策略，可实现机器条件的实时诊断和预后。机器故障诊断的一个主要问题是呈现错误行为的数据显着低于代表性，导致故障类的分类器精度差。由于错误行为的错误分类导致无计划的机器故障以及因此经济损失，因此改善的故障诊断分类器处理数据不平衡具有重要性。本文解决了二进制和多级不平衡数据集的问题。基于用于数据挖掘的结构KDD进程，定义了不平衡数据集的所需步骤。 KDD过程通过现实世界的工业案例进行评估，包括数据采样和支持向量机分类器的开发。

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