Prediction of flow blockages and impending cavitation in centrifugal pumps using Support Vector Machine (SVM) algorithms based on vibration measurements
Prediction of flow blockages and impending cavitation in centrifugal pumps using Support Vector Machine (SVM) algorithms based on vibration measurements
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DOI：
10.1016/j.measurement.2018.07.092
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发表时间：
2018-12-01
期刊：
影响因子：
5.6
通讯作者：
Tiwari, Rajiv
中科院分区：
文献类型：
--
作者：
Panda, Asish Kumar;Rapur, Janani Shruti;Tiwari, Rajiv
本文主要研究基于振动的离心泵状态监测与故障诊断。考虑了两种相互关联的故障，即入口管中的流动堵塞和泵中即将形成的气泡。对于变速泵的故障诊断和分类，采用了一种称为支持向量机（SVM）的机器学习算法。离心泵安装在机器故障模拟器（MFS（TM））上，用于实验目的。两个三轴加速度计，一个在泵壳上，另一个在轴承座上，用于提取振动信号。在不同的流动堵塞（0%，16.7%，33.3%，50%和66.6%的堵塞）和气泡形成的开始（空化的开始）采取振动信号。从时域振动信号中提取多个统计特征，并将其输入SVM算法进行训练和测试。标准差本身被证明比该领域中的任何其他特征都要好，并且对于该应用。SVM参数，包括y和C是最佳选择。训练数据和测试数据的比例也得到了优化。二进制故障分类提供了更好的预测精度的所有堵塞的情况下，多类故障分类。当训练和测试在更高的转速下进行时，已经发现在多级故障分类（不同程度的堵塞）中的适度更高的预测精度。还观察到，在较高的旋转速度下，可以非常准确地预测即将发生的气泡形成。(C)2018爱思唯尔有限公司版权所有
The present work concentrates on the vibration based condition monitoring and fault diagnosis of centrifugal pumps. Two types of interrelated faults, i.e. flow blockages in the inlet pipe and impending bubble formation in the pump are considered. For the fault diagnosis and classification in the pump at varied speeds, a machine learning algorithm called, the Support Vector Machine (SVM) is utilized. Centrifugal pump is mounted on the Machine Fault Simulator (MFS (TM)) set-up for the purpose of experimentation. Two tri-axial accelerometers, one on the pump casing and another on the bearing housing, are used to extract the vibration signals. Vibration signatures are taken at different flow blockages (0%, 16.7%, 33.3%, 50% and 66.6% of blockage) and at the start of bubble formation (inception of cavitation). Several statistical features are extracted from time domain vibration signal and fed to the SVM algorithm for training and testing. Standard deviation alone proves to be better than any other feature in this domain, and for this application. SVM parameters, including y and C are optimally chosen. The ratio of training and testing data is also optimized. Binary fault classification offered better prediction accuracy for all blockage conditions over the multi-class fault classification. Moderately higher prediction accuracy in the multi-class fault classification (different level of blockages) has been found, when the training and the testing is done at higher rotational speeds. It has also been observed that the impending bubble formation could be very accurately predicted at higher rotational speeds. (C) 2018 Elsevier Ltd. All rights reserved.