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Supervised by Ministry of Industry and Information Technology of The People''s Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Jinbao Zhang,Yongqiang Zhao,Lingxian Kong,Ming Liu.Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy[J].Journal of Harbin Institute Of Technology(New Series),2020,27(1):1-9.DOI:10.11916/j.issn.1005-9113.18110.
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Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy
Author NameAffiliation
Jinbao Zhang School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China 
Yongqiang Zhao School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China 
Lingxian Kong School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China 
Ming Liu School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China 
Abstract:
Bearings are crucial components in rotating machines, which have direct effects on industrial productivity and safety. To fast and accurately identify the operating condition of bearings, a novel method based on multi-scale permutation entropy (MPE) and morphology similarity distance (MSD) is proposed in this paper. Firstly, the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization. Then, the MSD was employed to measure the distance among test samples from different fault types and the reference samples, and achieved classification with the minimum MSD. Finally, the proposed method was verified with two experiments concerning artificially seeded damage bearings and run-to-failure bearings, respectively. Different categories were considered for the two experiments and high classification accuracies were obtained. The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.
Key words:  bearing fault diagnosis  multi-scale permutation entropy  morphology similarity distance
DOI:10.11916/j.issn.1005-9113.18110
CLC NUMBER:TP277
Fund:
Descriptions in Chinese:
  

基于多尺度排列熵和形态相似距离的滚动轴承故障诊断

张金豹,赵永强*,孔令贤,刘明

(哈尔滨工业大学 机电工程学院,哈尔滨 150001)

摘要:

轴承作为回转机械中的关键部件,直接影响到工业中的生产效率和人身安全。为准确便捷的识别轴承运行状态,本文提出一种基于多尺度排列熵和形态相似距离的新方法。首先,计算原始信号的多尺度排列熵,来表征信号在多个尺度上的复杂程度,然后组成特征向量,并进行标准化。接着,采用形态相似距离计算不同故障类型的样本和参考样本之间的距离,根据最近邻原则实现分类。最后,分别采用人工故障试验和全寿命试验两组实验数据对该方法进行验证。利用两组实验数据设计的不同组别中,分类精度都很高。实验结果表明该方法是一种行之有效的滚动轴承故障诊断方法。

关键词: 轴承故障诊断;多尺度排列熵;形态相似距离

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