【摘要】 针对发动机异响故障信号呈非平稳时变特征并伴随有强烈的背景噪声,提出一种基于小波细节系数自相关性分析的分层阈值降噪法,该方法对信号进行离散小波变换,将信号分解为近似系数和细节系数,求出各层细节系数的自相关序列,根据序列是否呈白噪声自相关特性确定该层阈值。信号经过分层阈值降噪后,再进行连续小波变换,画出时频图,结合时域特征和频域特征确定故障类别。试验研究首先以模拟的信号模型为例,再针对实际的活塞敲缸响和曲轴轴承响两种常见异响故障进行比较分析,结果表明,分层阈值降噪法可以提高信噪比,恢复较高频率的有用信号,小波时频图可以清晰地呈现故障信号的时域和频域特征,为诊断提供一种切实可行的策略。更多还原

【Abstract】 The engine abnormal sound signal is proved to be non-stationary and carries intense background noise. In view of these characteristics, a denoising method using multilevel threshold based on the analysis of the autocorrelation of detailed coefficients is proposed. This method use discrete wavelet transform technique to decompose the signal into approximations and details. The autocorrelation sequences of the detailed coefficients are determined. According to whether the sequence reflects the autocorrelation of white noise, the threshold is determined. The time-frequency map of the denoised signal is then drawn through continuous wavelet transform. By combining the features of time domain and frequency domain, the faults can be classified. In the experimental research a simulated signal model is introduced as an example. Piston cylinder knocking and crank bearing knock. Which stand for familiar engine abnormal sounds are compared and analyzed. The result proves that the signal to noise ratio is increased and the higher- frequency useful signal is recovered. The time-frequency map can display the features corresponding to time and frequency of fault signal which offers a practical strategy for diagnosis.更多还原