Variable Working Condition Fault Diagnosis Method for Rotating Machinery Based on Dual-Task Cognitive Cost Sensitivity

Accurate fault diagnosis of rotating machinery in complex environments and under changing operating conditions remains a key challenge in industrial systems. In this paper, we propose a novel fault diagnosis algorithm named dual-task cognitive cost sensitivity (DCCS), designed for high-accuracy diagnosis of rotary bearing faults and small-sample scenarios under variable working conditions. The method integrates four modules: CNN for local feature extraction, LSTM for temporal features, Softmax for classification, and a DCCS-based hyperparameter optimization module. A dual-task learning objective is formulated by combining losses from both full-condition and few-shot variable-condition datasets, with adaptive cost-sensitive weighting to balance learning focus. The integration of cognitive cost sensitivity with transfer learning enhances the model’s adaptability, allowing it to flexibly generalize across different operating conditions. Experiments on the CWRU dataset demonstrate that the method achieves 99.33% accuracy within fewer training epochs and shows strong robustness to noise. Compared with mainstream optimization methods, DCCS offers higher efficiency with reduced computation time. In cross-condition diagnosis, it improves accuracy by up to 10.94 percentage points over the original Alpha Evolution algorithm, effectively addressing the challenge of limited samples in varying environments.