2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2025)

To address the challenges of fault diagnosis in wind turbine drivetrains under variable speed conditions, this paper proposes a novel method called Dempster-Shafer (DS) evidence theory-based order spectrum sparse representation classification (DS-OSSRC). By integrating multi-sensor data, the proposed approach combines order spectrum analysis and sparse representation classification to extract discriminative speed-invariant features for classifier-free intelligent diagnosis. A decision-level fusion strategy based on DS evidence theory is proposed to effectively resolve the conflicts among individual channel outputs, enhancing diagnostic accuracy and robustness. Experimental validation on a wind turbine drivetrain dataset demonstrates that the proposed method achieves 99.52% accuracy under varying working conditions and significantly outperforms single-sensor-based models and two other fusion strategies, especially in noisy environments. The proposed DS-OSSRC method offers a computationally efficient and reliable solution for cross-condition transfer fault diagnosis.

fault diagnosis,order spectrum analysis,sparse representation classification,Dempster-Shafer evidence theory,variable working conditions