Rolling bearing fault feature extraction method based on feature mode decomposition and adaptive window length sliding window noise reduction
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摘要:
针对航空发动机滚动轴承故障信号受背景噪声影响导致故障信号特征微弱的难题，提出了基于灰狼算法优化特征模态分解与自适应窗长滑窗降噪的滚动轴承故障特征提取方法。采用特征模态分解方法对振动信号进行分解，再依据峭度-相关系数筛选准则对信号分量进行筛选重构；为了解决使用特征模态分解方法时输入参数需要人为筛选的弊端，选择重构信号的信息熵作为目标函数，应用灰狼算法对输入参数进行寻优，确定模态分量数和滤波器尺寸；然后，利用自适应窗长滑窗降噪方法对重构信号进行二次降噪和故障特征增强，输出降噪信号；对降噪信号进行包络解调，提取故障特征。分别使用仿真信号、美国凯斯西储大学数据集、航空发动机中介轴承实验台数据和主轴承实验台数据对提出方法的有效性进行验证。实验结果表明：特征模态分解与滑窗降噪方法的结合可以有效滤除振动信号中的干扰噪声成分，故障信号的信息熵降低了大约40%，使得包络谱图中的故障特征频率及其2～4倍频幅值明显，相较于其他现有的轴承故障诊断方法，具有更好的降噪效果和更强的故障特征提取能力。
Abstract:In view of the problem that the fault signal of aero-engine rolling bearing is affected by background noise, which leads to weak characteristics of the fault signal, a fault feature extraction method of rolling bearing based on grey wolf algorithm optimization feature mode decomposition and adaptive window length sliding window noise reduction was proposed. The vibration signal was decomposed by feature mode decomposition method, and then the signal components were filtered and reconstructed according to the kurtosis correlation coefficient selection criterion. To solve the problem that the input parameters need to be manually filtered with the feature mode decomposition method, the information entropy of the reconstructed signal was selected as the objective function, and the grey wolf algorithm was used to optimize the input parameters to determine the number of modal components and the size of the filter. Then, the noise reduction method of adaptive window length sliding window was used to perform secondary noise reduction and fault feature enhancement on the reconstructed signal, and the noise reduction signal was output. The noise reduction signal was used to extract fault features via envelope demodulation. The effectiveness of the proposed method was verified by using the simulation signal, the data set of Case Western Reserve University, the data of the aeroengine intermediate bearing testbed and the data of the main bearing testbed. The results showed that the combination of feature mode decomposition and sliding window noise reduction method can effectively filter out the interference noise components. The information entropy decreased by approximately 40%, making the fault characteristic frequency and its 2—4 times amplitude in the envelope spectrum more prominent. Compared with other existing bearing fault diagnosis methods, it had better noise reduction effect and stronger fault feature extraction ability.
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图 20 中介轴承外圈故障包络谱图
Figure 20. Intershaft bearing outer ring fault envelope spectrum diagram
图 25 中介轴承内圈故障包络谱图
Figure 25. Intershaft bearing inner ring fault envelope spectrum diagram
图 36 滑窗降噪后信号时域图
Figure 36. Signal time domain diagram after sliding window noise reduction
图 37 滑窗降噪后信号包络谱图
Figure 37. Signal envelope spectrum after sliding window noise reduction
表 2 实验轴承参数
Table 2. Test bearing parameters
几何参数 数值 轴承节径/mm 125 滚珠直径/mm 8 接触角/（°） 0 滚珠数量/个 34 
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表 3 主轴承参数
Table 3. Main bearing parameters
几何参数 数值 轴承节径/mm 167.5375 滚珠直径/mm 22.225 接触角/（°） 32～48 滚珠数量/个 20
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