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An Overproduce-and-Choose Strategy to Create Classifier Ensembles with Tuned SVM Parameters Applied to Real-World Fault Diagnosis

机译:一种过剩的选择策略,该策略使用已调整的SVM参数创建分类器集合,用于实际故障诊断

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We present a supervised learning classification method for model-free fault detection and diagnosis, aiming to improve the maintenance quality of motor pumps installed on oil rigs. We investigate our generic fault diagnosis method on 2000 examples of real-world vibra-tional signals obtained from operational faulty industrial machines. The diagnostic system detects each considered fault in an input pattern using an ensemble of classifiers, which is composed of accurate classifiers that differ on their predictions as much as possible. The ensemble is built by first using complementary feature selection techniques to produce a set of candidate classifiers, and finally selecting an optimized subset of them to compose the ensemble. We propose a novel ensemble creation method based on feature selection. We work with Support Vector Machine (SVM) classifiers. As the performance of a SVM strictly depends on its hyperparameters, we also study whether and how varying the SVM hyperparameters might increase the ensemble accuracy. Our experiments show the usefulness of appropriately tuning the SVM hyperparameters in order to increase the ensemble diversity and accuracy.
机译:我们提出了一种用于无模型故障检测和诊断的监督学习分类方法,旨在提高安装在石油钻机上的电动泵的维护质量。我们将根据从运行中的故障工业机器获得的2000年实际振动信号示例研究通用故障诊断方法。诊断系统使用一组分类器来检测输入模式中的每个已考虑的故障,这些分类器由准确的分类器组成,这些分类器的预测可能会有所不同。首先通过使用互补特征选择技术来生成一组候选分类器,然后最后选择它们的优化子集以构成整体,从而构建整体。我们提出了一种基于特征选择的新型合奏创建方法。我们使用支持向量机(SVM)分类器。由于SVM的性能严格取决于其超参数,因此我们还研究了是否以及如何更改SVM超参数可能会提高整体精度。我们的实验表明,适当调整SVM超参数以增加整体多样性和准确性是有用的。

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