电子测量与仪器学报

杨秀芳,陈卓,王驰.基于隐 Markov 模型的齿轮箱故障识别方法研究[J].电子测量与仪器学报,2020,34(11):115-123

基于隐 Markov 模型的齿轮箱故障识别方法研究

Research on gearbox fault identification method based on hidden Markov model

DOI：

英文关键词:fault identification gearbox hidden Markov model full feature vector principal component feature vector

基金项目:国家自然科学基金面上项目(51775433)资助

作者	单位
杨秀芳	1.西安理工大学机械与精密仪器工程学院
陈卓	1.西安理工大学机械与精密仪器工程学院
王驰	1.西安理工大学机械与精密仪器工程学院

Author	Institution
Yang Xiufang	1.Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology
Chen Zhuo	1.Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology
Wang Chi	1.Faculty of Mechanical and Precision Instrument Engineering, Xi’an University of Technology

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中文摘要:

针对神经网络的识别一直停留在静态模式识别上的不足,釆用一种近年来发展较快的动态模式识别技术—隐马尔科夫模型分析齿轮箱振动信号。首先提取齿轮箱振动信号在时域、频域和时频域的统计特征,组成 34 维全特征矢量,训练了一组全特征-隐马尔科夫模型库;再通过主分量分析技术对全特征矢量进行降维处理,取其吸收信息量 98%以上的前 7 个主分量组成主分量特征矢量,训练了另外一组主分量-隐马尔科夫模型库。分别用两组独立的模型库进行齿轮箱故障识别。结果表明,全特征-隐马尔科夫模型库对齿轮正常、齿轮断齿的识别准确率为 97. 9%,对齿轮点蚀的识别准确率为 100%,程序运行耗时 22. 328 s,主分量-隐马尔科夫模型库对齿轮点蚀的和齿轮断齿识别准确率均达到 100%,程序运行耗时 4. 879 s。振动信号特征的降维处理没有降低故障的识别率,反而提升了故障识别准确率,同时大大提升了程序运行速度,这对机械系统故障诊断具有重要意义。

英文摘要:

Aiming at the shortcomings of neural network recognition in static pattern recognition, a dynamic pattern recognition technology developed in recent years—hidden Markov model is used to analyze gearbox vibration signals. First, the statistical characteristics of the gearbox vibration signals in the time domain, frequency domain and time-frequency domain are extracted to form a 34-dimensional full feature vector. Trained a set of full feature-hidden Markov model libraries;then, through the principal component analysis technology, the full feature vector is reduced in dimension, and the first 7 principal components whose absorption information is more than 98% constitute the principal component feature vector. Another set of principal component-hidden Markov model library was trained. Two sets of independent model libraries are used for gearbox fault identification. The results show that the full feature-hidden Markov model library has 97. 9% accuracy for the identification of normal gears and gear broken tooth and 100% for gear pitting. The program takes 22. 328 s. The recognition accuracy of component-hidden Markov model library for gear pitting and gear tooth failure is 100%. The program takes 4. 879 s. Therefore, the dimensionality reduction processing of the vibration signal feature does not reduce accuracy of fault identification, but improves the accuracy of fault recognition, and greatly increases the speed of the program. This is of great significance for fault diagnosis of mechanical systems.

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