Demodulation analysis is one of the most effective methods for bearing fault diagnosis. However, in practical applications, the interferences from ambient noises or other rotating components may create great challenges to demodulation analysis and thus decrease its effectiveness. Generally, a selection procedure for the most informative frequency band (IFB) is usually implemented in advance to extract the fault features that are hidden by the interferences. The fast kurtogram (FK) has been utilized as a benchmark for the IFB selection. Although designed to identify the most impulsive part of the signal, the FK is inevitably affected by the fault-irrelevant impulsive and cyclostationary interferences due to the dual sensitiveness to the impulsiveness and cyclostationarity of the kurtosis, and thus it may produce a misleading band for demodulation. To address this issue, a novel and robust IFB selection method based on the fault energy of correntropy (named FECgram) is proposed in this paper to replace the FK, through which the IFB can capture the fault symptom without being influenced by the fault-irrelevant impulsive and cyclostationary interferences. The superiority of the FECgram in combination with the squared envelope spectrum (SES) is validated on both simulation data and three different challenging experimental datasets.

解调分析是轴承故障诊断的最有效方法之一。但是，在实际应用中，来自环境噪声或其他旋转组件的干扰可能会对解调分析带来巨大挑战，从而降低其有效性。通常，通常提前执行信息量最大的频段（IFB）的选择程序，以提取被干扰隐藏的故障特征。快速峰图（FK）已被用作选择IFB的基准。尽管FK旨在识别信号的最脉冲部分，但由于对峰度的脉冲和循环平稳性具有双重敏感性，因此FK不可避免地受到与故障无关的脉冲和循环平稳干扰的影响，因此可能会产生误导性的解调频带。为了解决这个问题，本文提出了一种基于熵的故障能量的新颖，鲁棒的IFB选择方法（称为FECgram）来代替FK，IFB可以通过IFB捕获故障症状而不受与故障无关的影响。脉冲和循环平稳干扰。FECgram与平方包络谱（SES）相结合的优越性已在仿真数据和三个不同的具有挑战性的实验数据集上得到验证。