Fault identification of bearings based on bispectrum distribution of ARMA model and FCM method

作者：Xu, Hong-Bo; Chen, Guo-Hua^*; Wang, Xin-Hua

来源：Journal of South China University of Technology(Natural Science Edition), 2012, 40(7): 78-82+89.

DOI：10.3969/j.issn.1000-565X.2012.07.014

• ARMA model
• Auto regressive moving average modeling
• Bispectrum
• Characteristics of vibrations
• Distribution features
• Empirical Mode Decomposition
• Fault identifications
• Fuzzy C mean

According to the nonlinear and non-Guassian characteristics of vibration signals of rolling bearings, a novel fault identification method based on the bispectrum distribution feature of auto-regressive moving average (ARMA) model and on the cluster analysis of fuzzy c-means (FCM) method is proposed. In this method, first, original vibration signals are modulated via the empirical mode decomposition (EMD), and an ARMA model of principal signal components is established. Then, a bispectrum estimation of the ARMA model is implemented. Finally, the binary images extracted from the bispectrum distribution are taken as the feature vectors and are used to construct a classifier of the class templates and the smallest-distance templates via the FCM clustering, thus implementing the fault identification successfully. Application results in the fault diagnosis of rolling bearings demonstrate that the proposed method is effective because it can accurately determine the actual conditions of rolling bearings.

• 出版日期2012-7
• 单位广州市特种机电设备检测研究院; 华南理工大学