SDAE network hyperparameters.

Motor rolling bearing is a fundamental component of industrial production, and its vibration signal extraction and fault diagnosis are challenging because of the effect of operating characteristics and external noise. This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic mutation-Sine Cosine Algorithm is utilized to optimize the hyperparameters of the stacked denoising auto-encoders network and construct a fault diagnosis model. The CWRU open bearing dataset is used to comprehensively evaluate the performance of the method, and the experimental results will be compared to show that the method proposed in this paper can effectively extract signal features in the situation of strong noise, while ensuring a high prediction accuracy, and has stronger adaptability and noise resistance compared with other methods.

电机滚动轴承（Motor rolling bearing）是工业生产的核心基础部件，受运行特性及外部噪声的影响，其振动信号提取与故障诊断始终是颇具挑战性的研究课题。本研究首先提出一种基于蜣螂优化算法（Dung Beetle Optimization Algorithm）的自适应变分模态分解（Adaptive Variational Mode Decomposition）方法，用于信号分解与特征提取。与此同时，构建了一种基于塔尼莫特系数（Tanimoto Coefficient）、排列熵（Permutation Entropy）与峭度（Kurtosis）的复合优化指标函数，并将其作为分解过程的适应度函数（fitness function），以提升该方法的灵活性与鲁棒性。随后将该方法与复合多尺度排列熵（Composite Multiscale Permutation Entropy）相结合，完成特征提取并构建特征向量。最后，采用改进惯性权重柯西混沌变异正弦余弦算法（Cauchy Chaotic Mutation-Sine Cosine Algorithm）对堆叠去噪自编码器网络（Stacked Denoising Auto-Encoders Network）的超参数进行优化，以此构建故障诊断模型。本研究采用西储大学公开轴承数据集（CWRU Open Bearing Dataset）对所提方法的性能进行全面评估，并通过对比实验结果验证：相较于其他同类方法，本文所提方法可在强噪声环境下有效提取信号特征，同时保障较高的预测精度，具备更优异的适应性与抗噪性能。