Meng Rui, Zhang Junpeng, Chen Ming, Chen Liangliang
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243002, China.
AHUT Engineering Research Institute, Anhui University of Technology, Maanshan 243002, China.
Entropy (Basel). 2025 Jul 24;27(8):782. doi: 10.3390/e27080782.
As critical components of planetary gearboxes, gears directly affect mechanical system reliability when faults occur. Traditional feature extraction methods exhibit limitations in accurately identifying fault characteristics and achieving satisfactory diagnostic accuracy. This research is concerned with the gear of the planetary gearbox and proposes a new approach termed multi-scale wavelet packet energy entropy (MSWPEE) for extracting gear fault features. The signal is split into sub-signals at three different scale factors. Following decomposition and reconstruction using the wavelet packet algorithm, the wavelet packet energy entropy for each node is computed under different operating conditions. A feature vector is formed by combining the wavelet packet energy entropy at different scale factors. Furthermore, this study proposes a method combining multi-scale wavelet packet energy entropy with extreme learning machine (MSWPEE-ELM). The experimental findings validate the precision of this approach in extracting features and diagnosing faults in sun gears with varying degrees of tooth breakage severity.
作为行星齿轮箱的关键部件，齿轮出现故障时会直接影响机械系统的可靠性。传统的特征提取方法在准确识别故障特征和实现令人满意的诊断精度方面存在局限性。本研究针对行星齿轮箱的齿轮，提出了一种称为多尺度小波包能量熵（MSWPEE）的新方法来提取齿轮故障特征。信号在三个不同的尺度因子下被分解为子信号。使用小波包算法进行分解和重构后，在不同运行条件下计算每个节点的小波包能量熵。通过组合不同尺度因子下的小波包能量熵形成特征向量。此外，本研究提出了一种将多尺度小波包能量熵与极限学习机相结合的方法（MSWPEE-ELM）。实验结果验证了该方法在提取具有不同程度断齿严重程度的太阳轮特征和诊断故障方面的精度。
