【摘要】 由于旋转电机滚动轴承振动信号存在不平稳、非线性的特征，传统时频分析法、小波分解法存在在信号分解过程中能量泄露、自适应能力差的问题，经验模态分解（EMD）法存在模态混叠等问题。提出一种基于噪声自适应完备总体平均经验模态分解方法（CEEMDAN），利用具有麻雀捕食预警机制的粒子群算法（SPSO）优化极限学习机神经网络（ELM）的CEEMDAN-SPSO-ELM算法。利用所提方法对滚动轴承单一与多种损伤故障进行分析诊断，结果表明，所提算法具有有效性及诊断准确性。更多还原

【Abstract】 In view of the unstable and nonlinear characteristics of the rolling bearing vibration signal of rotating electrical machines, the traditional time-frequency analysis method and wavelet packet decomposition method have energy leakage and poor adaptive ability in the signal decomposition process, and the EMD decomposition method has modal aliasing and other problems.In order to improve the fault diagnosis accuracy of rolling bearings, CEEMDAN combined with energy moment method is proposed to extract the original vibration signal features. The weight and offset of ELM hidden layer are optimized by SPSO algorithm, and the CEEMDAN-SPSO-ELM method is used to analyze and diagnose single and multiple damage faults of rolling bearings.The effectiveness of the algorithm and the improvement of diagnosis accuracy are verified by comparative experiments.更多还原