基于系统信息融合的滚动轴承故障模式识别

Rolling Bearing Fault Pattern Recognition Based on Fusing Random,Gray and Fuzzy Information

基于滚动轴承故障模式识别的随机性、灰色性和模糊性特征,从信息融合的角度出发,提出了一种融合框架。首先针对这三方面的信息分别从小波域、幅域和频域构造特征向量;然后借助于D-S证据理论,在基于概率统计的隐马尔科夫模型的诊断结果基础之上,进一步融合从系统灰色性和模糊性观点出发所得的诊断信息,从而实现滚动轴承故障模式的多角度信息融合识别;最后,利用该融合框架对实测滚动轴承故障数据进行了识别。结果表明,基于系统随机性、灰色性和模糊性信息融合的识别方法较基于系统单一性信息的识别方法能够进一步提高模式分类的正确率。

Based on the characteristics of random,gray and fuzzy information in the process of rolling bearing fault pattern recognition,a new fuse frame is presented by the view of information fusion.The characteristic vectors are calculated from wavelet,amplitude and frequency domain information,and the evidence theory is used.The diagnose result is gained by Hidden Markov Mode.Then,the recognition results from the view of gray and fuzzy are obtained.Results are fused so that rolling bearing fault pattern can be identified from multi-angle of view information.The fuse frame is applied to rolling bearing real test vibration data.The result shows that the class validity probability can be remarkably more advanced from multi-angle of random,gray and fuzzy information than single information.