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首页> 外文期刊>IEEE transactions on industrial informatics >Fault Diagnosis Method of Low-Speed Rolling Bearing Based on Acoustic Emission Signal and Subspace Embedded Feature Distribution Alignment
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Fault Diagnosis Method of Low-Speed Rolling Bearing Based on Acoustic Emission Signal and Subspace Embedded Feature Distribution Alignment

机译：基于声发射信号和子空间嵌入式特征分布对准的低速滚动轴承故障诊断方法

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摘要

Vibration signal always performs poorly in the fault diagnosis of low-speed rolling bearings. The fact that rolling bearings running under different speed conditions further increases the difficulty of fault diagnosis on low-speed bearing. To address the above problems, this article proposes a fault diagnosis method for low-speed rolling bearings based on acoustic emission (AE) signal and subspace embedded feature distribution alignment (SADA). First, the AE signal of low-speed rolling bearing is collected and the spectral dataset is constructed. Second, subspace alignment is used to align the basis vectors for both domains in order to prevent feature distortion. Then, a base classifier is trained to predict the pseudolabels of the target domain, which is used to quantitatively estimate the weight of the edge distribution and conditional distribution of the two domains for adaption. Finally, following the structural risk minimization (SRM) framework, a kernel function is constructed to establish the classifier f, which iteratively updates the pseudolabels in the target domain and obtains the coefficient matrix of the final framework to complete the identification task. The feasibility and effectiveness of the proposed method are verified by two AE datasets of low-speed rolling bearing.
机译：振动信号始终在低速滚动轴承的故障诊断中执行不良。滚动轴承在不同速度条件下运行的事实进一步提高了对低速轴承的故障诊断的难度。为了解决上述问题，本文提出了一种基于声发射（AE）信号和子空间嵌入特征分布对准（SADA）的低速滚动轴承的故障诊断方法。首先，收集低速滚动轴承的AE信号，并构造光谱数据集。其次，子空间对齐用于对齐两个域的基向量，以防止特征失真。然后，训练基本分类器以预测目标域的伪标签，其用于定量估计两个域的边缘分布的权重和用于适配的两个域的权重。最后，在结构风险最小化（SRM）框架之后，构造内核功能以建立分类器F，其迭代地更新目标域中的伪标签并获得最终框架的系数矩阵以完成识别任务。所提出的方法的可行性和有效性由低速滚动轴承的两个AE数据集验证。

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