针对齿轮振动信号非线性和非平稳的特点，提出一种基于改进希尔伯特-黄变换与马氏距离相结合的故障诊断方法。首先，利用自适应白噪声的完备经验模态分解将齿轮振动信号分解成一系列固有模态函数，并采用敏感固有模态函数判别算法判断出对故障信息敏感的模态函数；其次，通过对敏感固有模态分量的局部希尔伯特瞬时能量谱的分析，得出信号能量随时间变化的精确表达；最后，以不同故障信号局部希尔伯特瞬时能量谱的最大峰值作为特征向量，采用马氏距离对齿轮故障进行状态识别。试验结果表明：该方法可有效识提取齿轮故障特征，实现不同故障状态识别。
Abstract
In view of nonlinear and non-stationary characteristics of gear vibration signals, a fault diagnosis method based on improved Hilbert-Huang transform and Mahalanobis distance is proposed. The gear vibration signals are decomposed by complete ensemble empirical mode decomposition with adaptive noise, the intrinsic mode functions are obtained and sensitive intrinsic mode functions are selected by the sensitivity evaluation method. Then, the local Hilbert instantaneous energy spectrum of the sensitive intrinsic mode components is analyzed, and the fault information can be extracted form the distribution of the energy of the gear vibration signal with the change of time. Finally, the maximum peak value of the local Hilbert instantaneous energy spectrum as the fault features and using Mahalanobis distance method for judging the gear fault. Experimental results show that the method can effectively extract gear fault features and use it in different fault identification.
Key words
gear /
complete ensemble empirical mode decomposition with adaptive noise /
instantaneous energy spectrum /
Mahalanobis distance /
fault diagnosis
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