机械与电子2026，Vol.44Issue(8)：26-34,42,10.
基于SSA-VMD-CNN-BiLSTM的张力机轴承故障分级诊断
Multi-level Fault Diagnosis of Tensioner Bearings Based on SSA-VMD-CNN-BiLSTM
摘要
Abstract
To address the challenges of the weak fault signatures,strong non-stationarity,and difficul-ty in multi-class fault classification for bearings in new-energy tensioners operating under low-speed heavy-load,frequent start-stop,and tension fluctuation conditions,this paper proposes a hybrid fault di-agnosis model that integrates Sparrow Search Algorithm(SSA)-optimized Variational Mode Decomposi-tion(VMD),Convolutional Neural Network(CNN),and Bidirectional Long Short-Term Memory(BiL-STM),denoted as SSA-VMD-CNN-BiLSTM.In the proposed model,the sum of permutation entro-py of the intrinsic mode functions(IMFs)is adopted as the fitness function,and the SSA is introduced to adaptively optimize the number of decomposition modes K and penalty factor α in VMD,thereby yielding IMFs with superior time-frequency concentration and fault sensitivity.The optimized IMF components are then fed into a CNN to extract local deep features,followed by a BiLSTM network to model the bidirec-tional temporal dependencies of fault feature sequences,enabling the classification of four states of the gen-erator spindle bearing in new-energy tensioners:normal state,inner race fault,outer race fault,and rolling element fault.Experimental results show that using permutation entropy as the SSA fitness function yields the best diagnostic performance.The proposed model achieves a test-set classification accuracy of 98.33%,a Macro-F1 value of 98.31%,and an average running time of 11.04 s,outperforming comparison models,including the CNN-BiLSTM and SSA-VMD-CNN-LSTM.Multi-metricevaluation,confusion matrix analysis,statistical significance tests,and ablation experiments further demonstrate that the SSA-VMD,CNN,and BiLSTM modules respectively enhance fault-sensitive component representation,local feature extraction,and bidirectional temporal modeling capability.关键词
张力机/轴承/故障诊断/麻雀搜索算法/双向长短期记忆网络Key words
tensioner/bearing/fault diagnosis/sparrow search algorithm/bidirectional long short-term memory network分类
机械制造引用本文复制引用
王骅,周俊宏,李文泽,王海燕,吴伟智,杨先..基于SSA-VMD-CNN-BiLSTM的张力机轴承故障分级诊断[J].机械与电子,2026,44(8):26-34,42,10.基金项目
广东电网有限责任公司科技项目(031300KC23120022(GDKJXM20231447)) （031300KC23120022(GDKJXM20231447）
