Bearings are important and easily damaged parts in equipment. Collecting and analyzing the vibration signals of damaged bearings is a common fault diagnosis method. The vibration signal of the damaged bearing under constant speed conditions contains periodic pulses, and may also contain equipment operating vibration information, environmental noise or occasional pulses. In order to extract the periodic pulse information in the signal and weaken the influence of the interference signal, this paper proposes a periodic pulse detection indicator and calls it Harmonic spectral kurtosis (HSK). HSK can extract the harmonic information in the envelope spectrum, quantify the periodic pulses in the signal, and suppress the influence of interference such as random pulse. In order to expand the applications of this indicator, a tower-shaped boundary distribution diagram called Harmogram similar to Fast Kurtogram was established. Harmogram with HSK not only optimizes the spectral segmentation method, but also obtains a better center frequency and bandwidth. The filtered frequency bands can contain more periodic pulse information, and the proposed method has greater advantages in decomposing noisy signals. The simulation signal shows that the proposed method is accurate and effective. The data of bearing inner ring, outer ring and compound faults prove that the method can be applied to bearing fault diagnosis.

轴承是设备中重要且易损坏的部件。收集和分析损坏轴承的振动信号是一种常见的故障诊断方法。损坏轴承在恒速条件下的振动信号中含有周期性脉冲，也可能含有设备运行振动信息、环境噪声或偶发脉冲。为了提取信号中的周期性脉冲信息，减弱干扰信号的影响，本文提出了一种周期性脉冲检测指标，称为谐波谱峰度(HSK)。HSK可以提取包络谱中的谐波信息，量化信号中的周期性脉冲，抑制随机脉冲等干扰的影响。为了扩大该指标的应用，建立了类似于Fast Kurtogram的塔形边界分布图，称为Harmogram。Harmogram with HSK 不仅优化了频谱分割方法，而且获得了更好的中心频率和带宽。滤波后的频带可以包含更多的周期性脉冲信息，该方法在分解噪声信号方面具有更大的优势。仿真信号表明所提出的方法是准确有效的。轴承内圈、外圈和复合故障的数据证明该方法可用于轴承故障诊断。滤波后的频带可以包含更多的周期性脉冲信息，该方法在分解噪声信号方面具有更大的优势。仿真信号表明所提出的方法是准确有效的。轴承内圈、外圈和复合故障的数据证明该方法可用于轴承故障诊断。滤波后的频带可以包含更多的周期性脉冲信息，该方法在分解噪声信号方面具有更大的优势。仿真信号表明所提出的方法是准确有效的。轴承内圈、外圈和复合故障的数据证明该方法可用于轴承故障诊断。