基于倒谱预白化和随机共振的轴承故障增强检测

轴承损伤引起的冲击受到离散频率分量和噪声干扰，使轴承故障检测面临困难。结合基于倒谱编辑(Cepstrum editing procedure, CEP)的信号预白化和随机共振(Stochastic resonance, SR)微弱信号检测技术，提出一种轴承故障增强检测的新方法。信号预白化能够提升轴承振动信号的冲击特性，产生包含白噪声和轴承局部故障信号的白化信号。在未知最优共振频带的情况下，对白化后的轴承振动信号进行包络分析，增强故障特征分量的同时引入了较多噪声。通过随机共振的归一化尺度变换，将轴承包络信号作为检测模型的输入，增强轴承故障特征频率分量。提出将轴承故障特征频率处的局部谱峭度和局部信噪比作为对照指标。实测正常和外环植入故障轴承的诊断结果表明，提出的方法优于基于谱峭度优化的包络分析和单纯的信号预白化方法。

Enhanced Detection of Bearing Faults Based on Signal Cepstrum Pre-whitening and Stochastic Resonance

The impulse generated by bearing damage is interfered by discrete frequencies and much noise. The interference brings troubles to bearing faults detection. A new enhanced detection method of bearing faults is proposed based on combination of pre-whitening technology using cepstrum editing procedure (CEP) and weak signal detection based on stochastic resonance (SR) theory. Signal pre-whitening could be of advantage in enhancing the impulsiveness of the bearing signals, and the eventual result is a white signal, which contains both noise and the components resulting from localized bearing defects. The vibration signal pre-whitened is enveloped without the knowledge of optimal frequency band of bearing. The bearing faults character components could be enhanced, while bring more noise interference. The input of SR model is envelope signal of bearing. By normalized scale transform, bearing characteristic frequencies are enhanced. The local spectrum kurtosis and local signal-to-noise ratio of bearing faults characteristic components are proposed as indicators for comparison. The detection results of normal and outer race fault planted bearing show that the method proposed is better than the envelope analysis based on optimum spectrum kurtosis and signal pre-whitening only.