Integrated-Dispersion Manifold Distance: A New Distribution Discrepancy Metric for Machine Fault Transfer Diagnosis Under Time-Varying Conditions
针对时变条件下数据分布不稳定导致现有分布差异度量失效的问题，提出集成-色散流形距离（IDMD），通过最大熵局部分布选择和集成格拉斯曼流形测地线测量，增强动态数据结构的差异表征能力，在行星齿轮箱和风力涡轮机轴承实验中验证了有效性。
The distribution discrepancy metrics are the core foundation of achieving domain confusion. Therefore, they mainly determine the performance of deep transfer diagnosis models. However, their effectiveness relies on the stability of data local distributions, making them unsuitable for cross-domain machine diagnosis tasks under continuous time-varying conditions. Hence, a new integrated-dispersion manifold distance (IDMD) is proposed to enhance the discrepancy representation capability in dynamic data structures. The maximum entropy-based local distribution (MELD) selection mechanism is designed to represent the global distribution information of time-varying monitoring signals adaptively. Furthermore, the ensemble Grassmann manifold geodesic (EGMG) measurement is constructed to characterize the intrinsic distribution discrepancy information due to complex nonlinear structures of high-dimensional data. The proposed IDMD distribution discrepancy metric is validated against two fault transfer diagnosis experiments under time-varying conditions, including laboratory planetary gearboxes and actual wind turbine bearings. The experimental results demonstrate its effectiveness and advantage over the existing advanced methods.