基于自适应CYCBD和1.5维谱的滚动轴承故障特征提取方法

Fault features extraction method of rolling bearings based on adaptive CYCBD and 1.5-dimensional spectrum

在强背景噪声环境下,滚动轴承的故障特征信号难以得到分离,针对这一问题,提出了一种基于自适应最大二阶循环平稳盲卷积(CYCBD)和1.5维谱的滚动轴承故障特征提取方法。首先,对滚动轴承故障振动信号进行了傅里叶变换,得到了其信号的频谱结构,再以加权谐波和为优化指标,将信号频谱范围内的所有频率作为候选频率进行了搜索,确定加权谐波和最大处的频率为最优循环频率;然后,使用经过参数优化后的CYCBD对信号进行了滤波,并结合1.5维谱方法对滤波信号进行了处理;最后,为了进一步验证该方法在提取轴承故障特征方面的有效性,采用包络分析方法对实测信号进行了分析,获得了滤波信号的频谱特征。研究结果表明:经基于自适应CYCBD和1.5维谱方法滤波后,信号的香农熵为0.094,显著低于CYCBD和经验模态分解(EMD)方法;而且在信号的包络谱中,出现了清晰的故障特征频率及其倍频谱线,说明该方法具有较好的噪声抑制能力,并且能够有效地提取轴承振动信号中的故障脉冲成分。

Aiming at the problem of difficult to separate the fault characteristic signals of rolling bearing in a strong background noise environment,a fault feature extraction method of rolling bearing based on adaptive maximum second-order cyclostationary blind convolution(CYCBD)and 1.5-dimensional spectrum was proposed.Firstly,Fourier transform was performed on the vibration signal to obtain the spectral structure of the signal;taking the proposed weighted harmonic sum as the optimization index,all frequencies in the signal spectrum range were used as candidate frequencies to search,and the frequency at the maximum weighted harmonic sum was determined as optimal cycle frequency.Then,the CYCBD with optimized parameters was used to filter the signal,and the 1.5-dimensional spectrum method was used to process the filtered signal.Finally,in order to further verify the effectiveness of the method in extracting bearing fault characteristics,the measured signal was analyzed by envelope analysis method,and the spectral characteristics of the filtered signal were obtained.The experimental results show that after filtering by the method,the Shannon entropy of the signal is 0.094,significantly lower than CYCBD and the EMD method.In addition,clear fault characteristic frequency and multiplier appear in the envelope spectrum of the signal,which indicate that the method has good noise suppression ability and can effectively extract fault pulse components from bearing vibration signals.