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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理

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标 题： A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis
用于滚动轴承故障诊断的快速自适应多尺度形态分析方法
作 者： Changqing Shen
Qingbo He
Fanrang Kong
Peter W. Tse
关键词： Signal processing  [信号处理]
Bearing fault diagnosis  [ 轴承故障诊断]
Morphology  [ 形态学]
Varying-scale feature extractor  [ 多尺度特征提取器]
Vibration analysis  [ 振动分析]
出版年： 2013
期刊名： Proceedings of the Institution of Mechanical Engineers
机械工程师学会会刊
年卷期： 2013, vol.227, no.6
摘 要： The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

近年来，滚动轴承故障诊断的研究引起了广泛关注。这是因为轴承经常发生故障，可能导致设备意外停机。当轴承出现故障时，其振动频率谱中会呈现出周期性的脉冲信号。不同类型的轴承故障会导致脉冲出现在不同的周期间隔上。为了有效提取这些周期性脉冲，人们开发了多种技术以识别轴承的故障特征频率。本研究提出了一种自适应可变尺度的时间域形态分析方法。该方法可根据脉冲局部峰值定义结构元素的不同长度，适用于一维信号分析。该方法首先通过模拟脉冲信号进行了验证，随后又应用于包含内圈缺陷和外圈缺陷所导致的故障脉冲的真实轴承振动信号。结果表明，采用所提出的自适应变尺度形态分析方法，即使故障脉冲被噪声部分掩盖，也能准确识别轴承缺陷的原因。此外，与其他现有方法相比，该分析可作为高效的故障特征提取器，并能以极快的速度完成。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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