基于鲸鱼优化算法的自适应共振解调轴承故障诊断方法

针对共振解调方法需要选取包含较多故障信息的共振频带这一特点，提出了一种基于鲸鱼优化算法的自适应共振解调轴承故障诊断方法。首先，使用鲸鱼优化算法，以峭度和包络谱峭度构造的复合峭度作为优化目标，对带通滤波器参数中心频率和带宽进行自适应选择。然后，使用寻优得到的最优中心频率和带宽对轴承故障信号进行滤波分析。最后，对滤波后信号进行包络解调处理，提取出故障特征频率，判断轴承故障类型。通过对仿真信号和轴承内、外圈故障信号的分析诊断，可以证明该方法能够满足共振解调方法中对共振频带选取的要求，完成轴承信号故障频率的提取，是一种有效的轴承故障诊断方法。

Fault Diagnosis Method of Adaptive Resonance Demodulation Bearing Based on Whale Optimization Algorithm

Aiming at the characteristic that the resonance demodulation method needs to select the resonance frequency band containing more fault information, an adaptive resonance demodulation bearing fault diagnosis method based on the whale optimization algorithm is proposed. First of all, use the whale optimization algorithm for optimization. Taking the composite kurtosis as the optimization target, the parameters of the band-pass filter are adaptively selected. Then, use the optimal center frequency and bandwidth obtained by Whale optimization algorithm to filter and analyze the bearing fault signal. Finally, the envelope demodulation processing is performed on the filtered signal, and the fault characteristic frequency is extracted to judge the bearing fault type. Through the analysis of the simulation signal and the fault signal of the inner and outer rings of the bearing, it can be proved that the method can meet the requirements for the selection of the resonance frequency band in the resonance demodulation method, and complete the extraction of the fault frequency of the bearing signal.