基于随机共振预处理的振动故障特征提取研究

为有效降低噪声对机械故障特征提取结果干扰，提高故障特征集分类性能，提出基于随机共振(SR)预处理的故障特征提取方法。用随机共振方法对振动信号预处理，提高输出信号信噪比，增强信号频率特性；将随机共振输出信号用于特征集提取。为验证随机共振对信号预处理效果，分别提取基于时域、频域及时频域分析的故障特征集用于故障诊断；用转子试验数据对该方法所取特征集进行检验。结果表明，经随机共振处理后提取的各特征集与原始数据提取的特征集相比，均表现出较好分类性能，且其诊断结果的确定性较原始特征好，有望应用于工程实际。

Application research of the vibration fault feature extraction based on stochastic resonance pretreatment

In order to reduce the interference of noise on aeroengine mechanical fault diagnosis result and improve the classification performance of fault feature set, an integrated fault feature set extraction method based on stochastic resonance(SR) was proposed. First, the stochastic resonance was applied to pretreat the vibration signal, thus improving the Signal to Noise Ratio(SNR) and enhancing the frequency characteristics of the output. Then, the fault feature set was extracted from the output signals of SR system. The fault feature sets based on time domain analysis, frequency domain analysis and time-frequency domain analysis was proposed respectively to test the treatment effect of SR method. And the rotor test data was used to test the extracted feature sets. The results indicate that the fault feature set extracted from the output signals of SR system showed the better classification performance and the diagnosis result had the higher stability than the feature set extracted from the original signals.