【摘要】 变转速工况下，轴承故障信号具有显著的非平稳性和低信噪比特征，传统基于静态字典的稀疏表示方法多基于固定工况设计静态字典，难以适应转速波动引起的信号非平稳性，导致故障特征提取精度显著下降。针对这一问题，提出了一种Rényi熵驱动的结构字典学习方法(RE-ASDLA),旨在提升字典学习方法在非平稳信号工况下的适应性与诊断精度。该方法基于变转速下轴承故障形态特点，构造精准描述故障瞬态冲击形态并具有时变响应能力的过完备结构字典，突破传统分析中对信号片段截断处理的限制，能够从强噪声背景中精准提取稀疏的故障瞬态特征成分。在字典更新过程中，联合最小化重构误差和Rényi熵度量指标，自适应优化字典结构参数，增强对信号时变特征的响应能力，并构建时频-稀疏协同的故障诊断流程。通过2组线性/非线性变速仿真信号、1组渥太华公开轴承数据和1组实车采集数据开展实验验证。结果表明在低信噪比环境下，RE-ASDLA有效克服了强背景噪声和时变故障特征的干扰，准确重构变转速故障特征，在不同的变速模式下均能准确重构故障特征，验证了该方法的有效性，提升了字典学习在变速工况下的适应性。RE-ASDLA与快速路径优化方法和基于时间重分配的同步压缩变换相比，从信号重构精准度、时频聚集性、故障拟合效果体现了RE-ASDLA的优越性。更多还原

【Abstract】 Under variable-speed conditions, bearing fault signals exhibit significant non-stationarity and low signal-to-noise ratio(SNR). Traditional sparse representation methods based on static dictionaries are typically designed for fixed-speed scenarios, making them poorly suited to adapt to speed-induced signal variations, which leads to a notable decline in fault feature extraction accuracy. To address this problem, this paper proposes a Rényi entropy-driven adaptive structural dictionary learning algorithm(RE-ASDLA), aiming to enhance the adaptability and diagnostic accuracy of dictionary learning under non-stationary conditions. The method constructs an overcomplete structural dictionary with time-varying responsiveness, tailored to the transient impact patterns of bearing faults under variable speeds. It overcomes the limitations of conventional segment-based analysis and enables precise extraction of sparse transient features from strong noise backgrounds. During the dictionary update process, a joint optimization of reconstruction error and Rényi entropy is performed to adaptively refine dictionary parameters, enhancing sensitivity to time-varying features and establishing a time-frequency-sparsity collaborative diagnostic framework. Experimental validation is carried out using two sets of linear and nonlinear variable-speed simulation signals, one publicly available Ottawa bearing dataset, and one set of real onboard measurements. The results demonstrate that RE-ASDLA effectively suppresses background noise and reconstructs variable-speed fault features with high accuracy, even under low-SNR conditions. It maintains robust performance across different speed profiles and significantly enhances the adaptability of dictionary learning for variable-speed scenarios. Compared with fast path optimization and time-reassigned synchrosqueezing transform methods, RE-ASDLA shows superior performance in terms of reconstruction accuracy, time-frequency concentration, and fault feature representation.更多还原