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Fault diagnosis and severity analysis of rolling bearings using vibration image texture enhancement and multiclass support vector machines

机译：使用振动图像纹理增强和多标配矢量机的滚动轴承故障诊断和严重性分析

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摘要

Fault detection and diagnosis of its severity for machine health monitoring can be stated as a nested classification problem. For a faulty bearing, each fault location whether belonging to inner race, outer race or the ball can be seen as multiclass classification with three classes while the varying degree of severity in each class can be viewed as a sub classification task. The peculiar vibration patterns generated from the flaws in different bearing parts and with varying degree of distortion can be classified into various classes and subclasses for analysis of vibration signatures. This paper proposes a multiclass support vector machines (MSVMs) based fault classification approach for fault diagnosis of ball bearings. The one dimensional vibration signals are converted to two dimensional gray scale images resulting in textural patterns which are then enhanced using the wave atom transform. Features such as semivariance, skewness and entropy are extracted from the texture images and the MSVM is then trained using feature matrices generated from feature vectors. The MSVM is trained in two phases; in the first phase, the classifier categorizes the location of the fault and in the second phase the classifier does the diagnosis regarding the size of the fault at that particular location. Simulation results show that the proposed technique is highly robust in locating the fault and its severity. (C) 2021 Elsevier Ltd. All rights reserved.
机译：故障检测和诊断其机器健康监测的严重程度可以表示为嵌套的分类问题。对于造成故障的轴承，每个故障位置是否属于内部竞争，外部竞争或球可以被视为具有三个类的多字符分类，而每个类中的变化程度可以被视为子分类任务。从不同轴承部件中的缺陷和变化程度的缺陷产生的特殊振动图案可以分为各种等级和子类，以分析振动签名。本文提出了一种基于多牌的滚珠轴承故障诊断的故障分类方法。一维振动信号被转换为二维灰度图像，导致纹理图案，然后使用波原子变换增强。从纹理图像中提取诸如半变性，偏斜和熵的特征，然后使用从特征向量生成的特征矩阵训练MSVM。 MSVM在两个阶段接受培训;在第一阶段中，分类器对故障的位置和在第二阶段分类，分类器对该特定位置处的故障大小进行诊断。仿真结果表明，所提出的技术在定位故障及其严重程度方面是高度稳健的。 （c）2021 elestvier有限公司保留所有权利。

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