工矿自动化2026，Vol.52Issue(8)：15-25,11.DOI:10.13272/j.issn.1671-251x.2026080015
带式输送机托辊轴承健康状态识别方法
Health state identification method for belt conveyor idler bearings
摘要
Abstract
Intelligent health state identification of belt conveyor idler bearings faces challenges including insufficient degradation characterization by a single modality,poor complementarity and consistency between modalities,and difficulty extracting features that link health state classes to continuous degradation.To address these challenges,a bearing health state identification method based on acoustic-vibration fusion for Health Indicator(HI)construction and Multi-Granularity Ordinal Contrastive Learning(MGOCL)was proposed.Time-and frequency-domain features were extracted from preprocessed vibration and acoustic signals,and degradation-sensitive features were selected based on correlation,monotonicity,and robustness.An acoustic-vibration fusion method for HI construction based on Mamba-Bidirectional Cross Attention(Mamba-BiCA)was developed.Dual-branch Mamba encoders captured long-term temporal dependencies in acoustic and vibration features throughout bearing degradation.Bidirectional cross attention enhanced interactions between the two modalities,and physical constraints were incorporated to construct HIs for five health states:healthy,good,fair,degraded,and failed.MGOCL combined discrete state supervision with continuous degradation constraints to preserve interclass separability,the ordering of health states,and intraclass degradation continuity in the learned representations.Health states were identified using k-nearest neighbor classification.Experimental results showed that the method achieved identification accuracies of 0.972,0.918,and 0.965 on subsets G1,G2,and G4 of the XJTU public dataset,respectively,with an inference latency of only 6.183 ms.On a self-built dataset collected using a belt conveyor simulation test rig,the identification accuracy reached 0.947,outperforming comparison models including XGBoost,MLP,and MSCCNN.关键词
带式输送机/托辊轴承/健康状态识别/声振融合/健康指标构建Key words
belt conveyor/idler bearing/health state identification/acoustic-vibration fusion/Health Indicator construction分类
矿业与冶金引用本文复制引用
田露松,曹现刚,张鑫媛,段雍,黄利平,杨鑫,韩珂,马骁扬,马鑫杰..带式输送机托辊轴承健康状态识别方法[J].工矿自动化,2026,52(8):15-25,11.基金项目
国家自然科学基金面上项目(52274158) （52274158）
国家自然科学基金青年项目(52504174) （52504174）
陕西省科技计划项目(2025BSHSDZZ359) （2025BSHSDZZ359）
陕西省科技地方专项项目(25JC073). （25JC073）
