MADAN: a multi-angle domain adversarial network for robust cross-condition rolling bearing fault diagnosis

Abstract Rolling bearing fault diagnosis under varying operating conditions remains challenging due to significant domain shifts in vibration‐signal distributions. To address this, we propose a multi‐angle domain adversarial network (MADAN) that unifies multi‐angle perception and multi‐view utilization within an adversarial adaptation framework. First, a dual‐branch feature extractor captures both time‐domain and frequency‐domain representations via multi‐scale convolutions, augmented by channel and temporal attention, and fuses them into a concise 512-dimensional embedding. Second, bidirectionally complementary discriminators impose ‘source vs. non-source’ and ‘target vs. non-target’ adversarial tasks, yielding finer‐grained domain confusion. Third, a structurally complementary dual-head classifier—comprising a locally robust, high-dropout head and a globally oriented, low-dropout head—provides diversified decision boundaries, further regularized by an inter-head consistency loss. Extensive experiments on the PU and mechanical comprehensive diagnostic simulation platform bearing datasets demonstrate that MADAN consistently outperforms other models, achieving superior transferability and classification accuracy across diverse operating scenarios.