Xia Xin, Wang Xiaolu
School of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China.
Information Construction Center, Suqian University, Suqian 223800, China.
Entropy (Basel). 2025 May 10;27(5):512. doi: 10.3390/e27050512.
Efficient extraction and classification of fault features remain critical challenges in planetary gearbox fault diagnosis. A fault diagnosis framework is proposed that integrates hierarchical refined composite multiscale fuzzy entropy (HRCMFE) for feature extraction and a gray wolf optimization (GWO)-optimized least squares support vector machine (LSSVM) for classification. Firstly, the HRCMFE is developed for feature extraction, which combines the segmentation advantage of hierarchical entropy (HE) and the computational stability advantage of refined composite multiscale fuzzy entropy (RCMFE). Secondly, the hyperparameters of LSSVM are optimized by GWO using a proposed fitness function. Finally, fault diagnosis of the planetary gearbox is achieved by the optimized LSSVM using the HRCMFE-extracted features. Simulation and experimental study results indicate that the proposed method demonstrates superior effectiveness in both feature discriminability and diagnosis accuracy.
在行星齿轮箱故障诊断中，故障特征的高效提取和分类仍然是关键挑战。提出了一种故障诊断框架，该框架集成了用于特征提取的分层细化复合多尺度模糊熵（HRCMFE）和用于分类的灰狼优化（GWO）优化的最小二乘支持向量机（LSSVM）。首先，开发了HRCMFE用于特征提取，它结合了分层熵（HE）的分割优势和细化复合多尺度模糊熵（RCMFE）的计算稳定性优势。其次，使用提出的适应度函数通过GWO对LSSVM的超参数进行优化。最后，利用HRCMFE提取的特征通过优化的LSSVM实现行星齿轮箱的故障诊断。仿真和实验研究结果表明，所提方法在特征可区分性和诊断准确性方面均表现出卓越的有效性。
