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首页> 外文期刊>Journal of Dynamic Systems, Measurement, and Control >Multifault Diagnosis of Combined Hydraulic and Mechanical Centrifugal Pump Faults Using Continuous Wavelet Transform and Support Vector Machines
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Multifault Diagnosis of Combined Hydraulic and Mechanical Centrifugal Pump Faults Using Continuous Wavelet Transform and Support Vector Machines

机译：使用连续小波变换和支撑载体机组组合液压和机械离心泵故障的多利差诊断

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摘要

Centrifugal pumps (CPs) fail due to anomalies in fluid flow patterns and/or due to failure of mechanical subsystems in them. In this work, a technique built on the multiclass support vector machine (MSVM) is developed to identify multiple faults in the CP. In addition, the complex problem of fault combinations and their classification is dealt with in this work. The combination of features from motor line current sensors and accelerometers is used to train the algorithm. To take into account the transient as well as harmonic components of fault signatures, continuous wavelet transform (CWT) analysis is used. Thereafter, the most important information from the CWT coefficients is selected using the two proposed novel methods CWT-based on energy (BE)-MSVM and CWT-principal component analysis (PCA)-MSVM, which are BE as well as PCA, respectively. It is experimentally observed that faults in the CPs have a very strong association with its operating speed. Thus, in order to make the CP versatile in operation, it is important that the fault diagnosis methodology is also efficient at large speed range of CP operation. This work attempts to develop a fault classification methodology, which is independent of the CP operating speed.
机译：离心泵（CPS）由于流体流动模式中的异常和/或由于机械子系统的故障而导致。在这项工作中，开发了一种基于多条支持向量机（MSVM）的技术以识别CP中的多个故障。此外，在这项工作中处理了故障组合的复杂问题及其分类。电机线电流传感器和加速度计的特征的组合用于训练算法。要考虑瞬态以及故障签名的谐波分量，使用连续小波变换（CWT）分析。此后，使用基于能量（BE）-MSVM和CWT-主成分分析（PCA）-MSVM，使用两种提出的新型方法CWT系数来选择来自CWT系数的最重要信息。它是通过实验观察到CP中的故障与其操作速度非常强烈。因此，为了使CP多功能在操作中，重要的是，故障诊断方法在CP操作的大速度范围内也有效。这项工作试图开发出故障分类方法，其与CP运行速度无关。

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