Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (20): 197-201.DOI: 10.3778/j.issn.1002-8331.1903-0365

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Fusion Fault Diagnosis Using DPCA & Improved Evidence Theory

LI Guo, MA Chunyang, MA Jianxiao   

  1. 1.College of Machinical and Electrical Engineering, Nanyang Normal University, Nanyang, Henan 473061, China
    2.Automobile Engineering Department, Henan Economic Management School, Nanyang, Henan 473000, China
  • Online:2019-10-15 Published:2019-10-14



  1. 1.南阳师范学院 机电工程学院,河南 南阳 473061
    2.河南省经济管理学校 汽车工程系,河南 南阳 473000

Abstract: In order to comprehensively and reasonably utilize multi-source information of equipments to improve the accuracy of fault diagnosis, a method of fusion fault diagnosis is proposed based on Dynamic Principle Component Analysis(DPCA) and improved evidence theory. This method constitutes multi evidences to fault diagnosis at many levels by means of DPCA and revises the basic assignment probability according to the authoritative coefficients based on statistical errors. The method of time authoritative conversion of evidence and conflict weighted assignment are proposed to improve evidence combination rules. The experimental results show that the weighted fusion treatment of multi-source information evidences can reduce the conflicts based on single information, which can increase reliability by 50% and greatly reduce uncertainty. The results also are unaffected by the decline of evidence authority. So the method can effectively improve the accuracy of fault diagnosis.

Key words: dynamic principle component analysis, evidence theory, fault diagnosis, multi-source information fusion

摘要: 为了利用同一设备的多源特征信息提高故障诊断的准确性,提出了一种基于动态主元分析法(DPCA)和改进证据理论的融合式故障诊断方法。该方法利用DPCA在多个层面对设备故障特征诊断构成多证据体,基于统计误差的证据权威性系数修正基本指派概率,提出了证据的时间权威性换算和冲突度的加权分配方法,改进了证据组合规则。实验结果表明,多信息源证据体的加权融合处理能够明显降低单一信息源诊断间的冲突,在融合可信度提高50%左右的同时不确定性大大降低,并且随着证据权威性的下降,诊断结果基本未受影响,该方法可以有效提高故障诊断的准确率。

关键词: 动态主元分析, 证据理论, 故障诊断, 多信息融合