A Learnable FIR and DPEE‐Based Selective SSM–Conv Framework for Cross‐Condition Bearing Fault Diagnosis

ABSTRACT Robust bearing fault diagnosis in electric motor‐driven electromechanical systems remains challenging under varying operating conditions, where changes in speed, load, and torque induce substantial distribution shifts in vibration signals. This study presents an end‐to‐end deep learning framework that integrates learnable multi‐band FIR decomposition, a Dynamic Phase Event Encoder (DPEE), and a Selective state space model–convolutional (SSM–Conv) hybrid backbone within a unified differentiable architecture. Unlike conventional approaches that mainly rely on amplitude‐ or spectrum‐oriented representations, the proposed method introduces a phase‐driven intermediate representation designed to capture both localized fault‐related irregularities and longer‐range temporal dependencies. To reflect more realistic monitoring conditions, evaluation was performed using a condition‐based leave‐one‐operating‐condition‐out protocol, ensuring strict separation between training and test conditions and reducing condition‐level data leakage. On the Case Western Reserve University (CWRU) dataset, the framework achieved consistently strong performance across unseen load conditions, while on the more challenging Paderborn dataset, the results varied depending on the severity of the condition shift. Ablation studies further supported the contribution of both the learnable FIR decomposition and the DPEE module. Additional analyses were also conducted to assess robustness and transferability. Under additive white Gaussian noise, the model remained comparatively stable at mild‐to‐moderate SNR levels but showed noticeable degradation under severe noise. Cross‐dataset experiments between CWRU and Paderborn, including target‐domain fine‐tuning with 5%, 10%, and 20% labeled target data, indicated that limited target supervision can substantially improve adaptation. Overall, the results suggest that the proposed framework is a promising approach for reliability‐oriented bearing condition monitoring under variable operating regimes.