Multiple faults separation and identification of rolling bearings based on time-frequency spectrogram

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Structural Health Monitoring-An International Journal Pub Date : 2023-10-10 DOI:10.1177/14759217231197110
Ming Lv, Changfeng Yan, Jianxiong Kang, Jiadong Meng, Zonggang Wang, Shengqiang Li, Bin Liu
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Abstract

Rolling bearings play a crucial role as components in rotating machinery across various industrial fields. Bearing faults can potentially lead to severe accidents in operating machines. Therefore, condition monitoring and fault diagnosis of rolling bearings are essential for preventing equipment failures. Multiple faults are a common occurrence resulting from the prolonged operation of rolling bearings, and numerous research efforts have been made to study multiple faults in different components of the bearing. However, diagnosing multiple faults in a single component of the rolling bearing still remains a highly challenging task. In this paper, a multiple faults separation and identification method based on time-frequency (TF) spectrogram (TFS) is proposed for vibration signals of rolling bearings. Firstly, the fast path optimization method is improved to match the TFS of original vibration signals in bearing faults generated by short-time Fourier transform. Then multiple TF curves are extracted from the TFS by the proposed multiple transient component curves extraction method based on the improved fast path optimization method. With the fault characteristic period, a classification criterion is introduced to separate TF curves. Secondly, a TF masking method is constructed to retain the TF information closely related to fault components of vibration signals. Finally, the novel TF representation can be obtained to develop signal reconstruction, and multiple faults can be detected based on envelope analysis separately. The experiments from rolling bearings with multiple faults on raceways are used to verify the effectiveness of the proposed methods.
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基于时频谱的滚动轴承多故障分离与识别
滚动轴承在各个工业领域的旋转机械中起着至关重要的作用。轴承故障可能会导致机器操作中的严重事故。因此,滚动轴承的状态监测和故障诊断对于防止设备故障至关重要。多故障是滚动轴承长时间运行所导致的常见故障,人们对轴承不同部件的多故障进行了大量的研究。然而,诊断滚动轴承单个部件的多个故障仍然是一项极具挑战性的任务。针对滚动轴承振动信号,提出了一种基于时频谱(TFS)的多故障分离与识别方法。首先,改进快速路径优化方法,匹配由短时傅里叶变换生成的轴承故障原始振动信号的TFS;然后,采用基于改进快速路径优化方法的多瞬态分量曲线提取方法,从TFS中提取多条TF曲线。根据故障特征周期,引入分类准则对TF曲线进行分类。其次,构造TF掩蔽方法,保留与振动信号故障分量密切相关的TF信息;最后,利用新的TF表示进行信号重构,并在包络分析的基础上分别检测出多个故障。通过滚动轴承在滚道上的多故障实验,验证了所提方法的有效性。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
期刊最新文献
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