Multi-granularity spectrogram-based method for idler abnormal condition detection

DANG Yingying; CAO Xiangang; ZHANG Xinyuan; LI Xiangyu; MAO Yiwen; FAN Hongwei; DONG Ming; WAN Xiang; DUAN Yong

doi:10.13272/j.issn.1671-251x.2025120056

Abstract：Under complex underground operating conditions,mechanical noise generated by belt friction and coal flow impacts,airflow-induced disturbance noise,and coupled noise from multiple devices are superimposed,causing fault-related acoustic signatures of idlers to be easily masked by environmental noise.Meanwhile,the acquisition of abnormal idler samples is difficult and annotation costs are high,making traditional supervised learning-based idler abnormal condition detection methods hard to generalize effectively.To address these issues,an unsupervised idler abnormal condition detection method based on Multi-Granularity Attention Autoencoder(MG-AAE)was proposed,which used only normal-condition idler sounds for model training and required no fault labels.A multi-granularity composite acoustic feature composed of Mel spectrograms and Mel-Frequency Cepstral Coefficients(MFCCs)was constructed to jointly capture energy contours and fine-grained acoustic signatures.A Gaussian Difference Pyramid(GDP)and a Multi-Head Attention(MHA)mechanism were introduced into the encoder to perform multi-scale modeling and adaptive weighted fusion,thereby suppressing steady background noise and highlighting key fault-related frequency bands.A multi-dimensional reconstruction mean-square error was used as the anomaly criterion to achieve automatic identification of idler abnormal conditions.Experimental results showed that,when trained using only normal samples,the MG-AAE model demonstrated excellent performance in cross-device and real-world operating conditions.Evaluation on four typical device categories in the MIMII dataset showed that,under a strong noise condition of 0 dB,the average area under curve(AUC)and local AUC(pAUC).f the MG-AAE model reached 84.2％and 70.4％,respectively,representing improvements of 7.3％and 5.6％over the Autoencoder model.On real idler data,the AUC reached 95.47％,and the reconstruction error of abnormal samples was approximately 1.40 times that of normal samples.These results indicate that the proposed method has good cross-device generalization and a low false alarm rate,and provides effective technical support for abnormal condition detection of idlers in coal mine belt conveyor systems.