Weak Feature Extraction Method for Bearing Faults Under Low-Speed Heavy-Duty Conditions

Abstract

Large electromechanical equipment typically operates under low-speed, heavy-duty conditions, significantly increasing the likelihood of bearing failures. The reduced speed diminishes the frequency of fault impacts per unit time, rendering them prone to being obscured within intricate vibration signals. To address this challenge, a novel fault weak feature extraction method is proposed for low-speed, heavy-duty operations. Initially, Improved Variational Modal Decomposition (IVMD) is employed to efficiently isolate the primary signal components from diverse signals and reconstruct them. Subsequently, a Stacking Autoencoder (SAE) is leveraged to extract higher-order features from each reconstructed signal spectrum, markedly enhancing the discriminative power between distinct signals. Lastly, Random Forest (RF) is utilized to establish a precise mapping between these higher-order features and fault category labels, enabling effective diagnosis of bearing health status. This integrated method effectively tackles the difficulties of weak fault feature extraction and classification under low-speed, heavy-duty conditions. Experimental data validates the method’s accuracy, achieving a remarkable 97% success rate.

Keywords

• Low-speed heavy-duty
• improved VMD
• stacking autoencoder
• random forest
• health monitoring