针对齿轮故障难以识别的问题,提出了一种用于齿轮异常状态识别的自适应噪声补偿聚合经验模态分解方法。利用光纤布拉格光栅(FBG)传感器提取齿轮的振动信号,通过自适应补偿高斯白噪声使振动信号频谱均匀化,以消除经验模态算法分解产生的模态混叠现象。利用相关系数和峭度值组成综合评价指标来选择有效分量,并提取其特征,采用支持向量机对齿轮故障进行识别与分类。实验结果表明:所提方法能有效地识别齿轮的不同状态(正常、轻度磨损、重度磨损、点蚀、裂纹以及断齿等),识别正确率均在90%以上。

In this study, we propose a gear fault identification method based on adaptive-noise complementary ensemble empirical mode decomposition to solve the problem associated with the identification of gear faults. Initially, we used a fiber Bragg grating to extract the gear vibration signals, and uniformized the spectrum of vibration signal by adaptively adding Gaussian white noise to eliminate the mode mixing caused by the empirical modal algorithm. Subsequently, we used the correlation coefficient and the kurtosis value to obtain comprehensive evaluation indexes for selecting the effective components and extracting the features of the effective components. Finally, we used a support vector machine to identify the gear faults. The experimental results denote that the proposed method can be used to effectively identify the states of gears, including normal, mild-wear, severe-wear, pitting, cracks, broken teeth. Furthermore, the gear state identification accuracy is more than 90%.