齿轮故障识别的密度峰值聚类欠定盲源分离算法

Underdetermined blind source separation based on density peak clustering for gear fault identification

为提高盲源分离算法在振动源数目估计问题中的噪声鲁棒性,提出了一种基于密度峰值聚类的欠定盲源分离算法。对预处理后的信号提取单源点,通过密度峰值聚类对单源点进行聚类得到混合矩阵的估计值。通过基于压缩感知模型对源信号进行重构,得到分离信号。为验证所提算法分离准确性和噪声鲁棒性,用所提算法对不同信噪比下的仿真信号进行分离,结果显示:在信噪比不低于4 dB时,所提算法均可以准确分离出源信号,算法的准确性和鲁棒性得到验证。设计旋转部件故障诊断试验台对所提算法在实际应用中的有效性进行验证,对实测复合故障振动信号进行分离,试验结果表明该算法成功分离出观测信号中的锥齿轮和行星齿轮单一故障特征,有助于工程中旋转部件故障诊断。

In order to improve the noise robustness of blind source separation algorithm in estimating the number of vibration sources, an underdetermined blind source separation method was proposed based on density peak clustering. Single source points were extracted from signals preprocessed, and then the mixed matrix was estimated by clustering single source points with density peak clustering. The separated signals were obtained by reconstructing the source signals based on the compressed sensing model. In order to verify the separation accuracy and noise robustness of the proposed algorithm, the simulation signals under different signal-to-noise ratios were separated by the proposed algorithm. The results showed that when the signal-to-noise ratios was not less than 4 dB,the proposed algorithm can accurately separate the source signals,and the accuracy and robustness of the algorithm were verified. A rotating component fault diagnosis test-bench was designed to verify the effectiveness of the proposed algorithm in practical application, and the measured composite fault vibration signal was processed. The test results showed that the algorithm successfully separated the single fault characteristics of bevel gear and planetary gear from the observed signal, contributing to the fault diagnosis of rotating components in engineering.