Dual-Channel Acoustic Temporal–Spectral Representation and Noise-Perturbation Learning for Mining Conveyor Fault Diagnosis

Abstract

This study develops a robust acoustic fault diagnosis framework for mining conveyor idlers, addressing the challenge of detecting early-stage mechanical degradation in noisy and imbalanced industrial environments. A dual-channel temporal--spectral representation is constructed by combining multi-scale log-Mel spectrograms and raw waveforms to capture complementary spectral patterns and fine-grained temporal dynamics. A Dual-Stream Cross-Attention Convolutional Recurrent Neural Network (DSCA-CRNN) is proposed to model cross-stream dependencies and enhance feature fusion. Noise-perturbation augmentation and a triplet-based contrastive objective are employed to enrich minority fault samples and improve embedding discriminability. Experiments on a self-collected conveyor auscultation dataset with 2,495 segments across three health states show that DSCA-CRNN achieves an overall accuracy of 0.95 and a macro-F1 score of 0.90, outperforming representative machine learning and deep learning baselines. Severe-fault recognition reaches an F1-score of 0.80. Ablation studies and PCA visualization confirm the effectiveness of recurrent temporal modeling, cross-attention fusion, noise-perturbation learning, and contrastive representation shaping. The proposed auscultation-based framework provides a practical and deployable solution for safety-oriented conveyor condition monitoring.