Feature Extraction Method Based on Ensemble Empirical Mode Decomposition and Curve Quadratic Code - Peng, Gang; Chen, Tao; Yang, Jiaqi (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
Fault diagnosis, as an important part of machinery and equipment health management, plays a vital role in improving the service life of machinery and equipment and in reducing the safety risks of machinery use. Feature extraction directly affects the effectiveness of data-driven fault-diagnosis methods. To improve the accuracy of fault type diagnosis, this study proposes a novel feature extraction method, ensemble empirical mode decomposition–curve quadratic code, by combining ensemble empirical mode decomposition and curve quadratic code to collect rotor vibration signals from rotating machinery where faults occur and uses the method for feature extraction, which can obtain higher-order code with richer feature information. Experiments demonstrate that the proposed feature extraction method maintains an average fault diagnosis rate above 90% across a buffer-coefficient range of 0.05–0.25, with a peak of 92% at 0.15, and effectively improves the correct diagnosis rate of fault types.