Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

中文翻译：

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基于负熵的灰狼优化器的多目标信息频带选择在滚动轴承故障诊断中的应用。

在包络分析中，对于滚动轴承的局部故障检测，信息频带（IFB）的选择是一项艰巨的任务。在以前的研究中，通常使用单个指标（例如峰度等）进行指导自动选择。但是，在某些情况下，很难完全描绘并平衡重复性瞬变的脉冲性和循环平稳性引起的故障特征。为解决这一问题，本文提出了一种新型的负熵诱导的多目标优化小波滤波器。小波参数由具有两个独立目标函数的Gray Wolf优化器确定，即最大化平方包络和平方包络谱的负熵以分别捕获冲动和循环平稳性。后来，利用平均负熵来从获得的帕累托集合中识别出IFB，IFR不受其他解决方案的支配，以平衡脉冲和循环平稳特征并消除背景噪声。为了验证所提出方法的性能，应用了两种情况下带有轻微轴承故障的真实振动信号实例，结果证明了其在某些快速和最佳滤波方法上的有效性。此外，还通过状态监视数据集来测试其跟踪IFB的稳定性。为了验证所提出方法的性能，应用了两种情况下带有轻微轴承故障的真实振动信号，以证明该方法在某些快速和最佳滤波方法上的有效性。此外，还通过状态监视数据集来测试其跟踪IFB的稳定性。为了验证所提出方法的性能，应用了两种情况下带有轻微轴承故障的真实振动信号，以证明该方法在某些快速和最佳滤波方法上的有效性。此外，还通过状态监视数据集来测试其跟踪IFB的稳定性。