关键词: 柔性薄壁轴承, 故障诊断, 牛顿-拉夫逊优化算法, 双向长短期记忆网络, Transformer模型, 时变滤波经验模态分解

Abstract: To address the challenges of insufficient feature extraction and fine-grained fault identification in existing diagnostic methods for flexible thin-walled bearings,it proposes a fault diagnosis approach based on a Newton-Raphson-Based Optimizer (NRBO) algorithm-optimized Transformer encoder and Bi-directional Long Short-Term Memory (BiLSTM) decoder. First,vibration signals are decomposed using Time-Varying Filter Empirical Mode Decomposition (TVFEMD),with its time-varying filtering bandwidth and B-spline order optimized by NRBO algorithm. Principal Intrinsic Mode Function (IMF) are selected via cross-correlation coefficients,and their time-frequency features are combined with time-domain and frequency-domain characteristics to build a multi-domain dataset. The NRBO algorithm further optimizes the model′s initial learning rate,BiLSTM hidden layer nodes,and Transformer regularization coefficients. Experimental results show a 99.60 % accuracy,outperforming other fine-grained diagnostic models. It provides a novel solution for intelligent fault diagnosis of flexible thin-walled bearings and references for intelligent health management in related fields.

Key words: flexible thin-walled bearing, fault diagnosis, Newton-Raphson-Based Optimizer (NRBO) algorithm, Bi-directional Long Short-Term Memory (BiLSTM) network, Transformer models, Time-Varying Filter Empirical Mode Decomposition (TVFEMD)