Adaptive blind deconvolution via convolutional neural networks for early fault detection in degraded gears under different speeds

Abstract

Early fault detection of degraded gears at different speeds is both essential and challenging. Adaptive blind deconvolution methods have shown considerable promise for extracting fault characteristics from complex vibration signals. Their performance depends on accurate cyclic frequency estimation and optimal filter length selection. However, this estimation often fails due to gear meshing shock interference and early weak fault characteristics. Additionally, determining the filter length relies on additional metrics with inefficient search strategies, thereby limiting the overall reliability and efficiency. To address these issues, an adaptive blind deconvolution via convolutional neural network (ABDCNN) is proposed. First, we employ an envelope harmonic product spectrum guided by gear frequency-domain features to reduce interference from noise and meshing shocks, enabling precise estimation of the target cyclic frequency. Then, an attention mechanism is integrated into the convolutional neural network to jointly optimize filter coefficients and length estimation, thereby improving computational efficiency. Simulations and gear contact fatigue experiments demonstrate that ABDCNN enables more efficient detection of early faults across different speeds while maintaining strong interpretability.