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Soft computing approach to fault diagnosis of centrifugal pump

机译：离心泵故障诊断的软计算方法

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摘要

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.
机译：从工业角度来看，旋转机械中的故障检测和隔离非常重要，因为它可以帮助进行维护活动并显着减少机器的停机时间，从而节省大量成本。已经发现传统方法不是很准确。为此目的，越来越多地采用基于软计算的方法。所提出的方法基于一种称为基因表达编程（GEP）的遗传编程技术。 GEP在某种程度上是基因编程家族的新成员。本文的主要目的是将所提出的基于进化计算的方法与其他模式分类方法（如支持向量机（SVM），Wavelet-GEP和近端支持向量机（PSVM））的分类准确性进行比较。为此，在离心泵上模拟了六个状态，即正常，轴承故障，叶轮故障，密封故障，叶轮和轴承故障，气蚀现象。决策树算法用于选择特征。将使用GEP获得的结果与基于Wavelet-GEP，支持向量机（SVM）和近端支持向量机（PSVM）的分类器的性能进行比较。可以看出，GEP和SVM均优于本研究中考虑的其他两个分类器（PSVM和Wavelet-GEP）。

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