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Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines

机译：使用异构特征模型和多类支持向量机对轴承进行可靠的多重组合故障诊断

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This paper proposes a reliable multiple combined fault diagnosis scheme for bearings using heterogeneous feature models and an improved one-against-all multiclass support vector machines (OAA-MCSVM) classifier. Distinct feature extraction methods are simultaneously applied to an acoustic emission (AE) signal to extract unique fault features for diagnosing bearing defects. These fault features are composed of time domain, frequency domain statistical parameters, and complex envelope spectrum analysis. Generally, a high-dimensional feature vector is used to train the standard OAA-MCSVM classifier for diagnosis and identification of bearing defects. However, this classification method ignores individual classifier competence when results from multiple classes are agglomerated for the final decision, and therefore, yields undecided and overlapped feature spaces where classification accuracy is severely degraded. To solve this unreliability problem, this paper introduces a dynamic reliability measure (DReM) technique for individual support vector machines (SVMs) in the one-against-all (OAA) framework. This DReM accounts for the spatial variation of the classifier's performance by finding the local neighborhood of a test sample in the training samples space and defining a new decision function for the OAA-MCSVM. The efficacy of the proposed OAA-MCSVM classifier with DReM is tested for identifying single and multiple combined faults in low-speed bearings. The experimental results demonstrate that the proposed classifier technique is superior to three state-of-the-art algorithms, yielding 6.19-16.59% improvement in the average classification performance. (C) 2018 Elsevier Ltd. All rights reserved.
机译：本文提出了一种基于异构特征模型的可靠的轴承多重故障组合诊断方案，并提出了一种改进的反对所有多类支持向量机（OAA-MCSVM）分类器。同时将独特的特征提取方法应用于声发射（AE）信号，以提取独特的故障特征以诊断轴承缺陷。这些故障特征由时域，频域统计参数和复杂的包络频谱分析组成。通常，高维特征向量用于训练标准OAA-MCSVM分类器，以诊断和识别轴承缺陷。但是，当将多个类别的结果进行汇总以进行最终决策时，此分类方法会忽略各个分类器的能力，因此会产生未确定且重叠的特征空间，从而严重降低分类精度。为了解决这个不可靠的问题，本文介绍了一种针对单一支持（OAA）框架的单个支持向量机（SVM）的动态可靠性度量（DReM）技术。此DReM通过在训练样本空间中找到测试样本的局部邻域并为OAA-MCSVM定义新的决策函数来解决分类器性能的空间变化。测试了建议的带有DReM的OAA-MCSVM分类器的功效，以识别低速轴承中的单个和多个组合故障。实验结果表明，所提出的分类器技术优于三种最新算法，平均分类性能提高了6.19-16.59％。 （C）2018 Elsevier Ltd.保留所有权利。

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