Bearing Fault Diagnosis Based on Dual-domain Anti-noise Coding and Gated Recurrent Transformer
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摘要: 针对强噪声非平稳环境下滚动轴承故障信号关键特征易被淹没而导致诊断性能下降的难题,提出一种双域抗噪编码与协同注意力混合解码模型.首先,该模型构建一个双域抗噪编码器,其时域残差收缩分支可自适应学习阈值以抑制干扰,同时波域可微分小波卷积分支用以捕获多尺度频率结构,二者共同实现鲁棒的多域特征表示;其次,模型设计双域协同注意力模块,通过双向交互与门控调节实现时域、波域特征的动态协同与自适应融合,进而提升高噪声下的特征融合能力;最后,开发门控循环Transformer(Gated recurrent transformer, GRT)解码器组件,将Transformer自注意力机制与GRU循环门控机制深度融合,在统一的特征空间内同步实现全局建模与局部时序依赖提取的高效平衡.基于凯斯西储大学与帕德博恩大学轴承数据集的实验表明,该模型在标准工况下准确率达到100%,且在强噪声下仍保持高准确率,充分体现了其优越的抗噪性与鲁棒性.
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关键词:
- 故障诊断 /
- 双域特征提取 /
- 协同注意力机制 /
- 门控循环Transformer
Abstract: To address the challenge where key features of rolling bearing fault signals are easily masked in strong noise and non-stationary environments, leading to degraded diagnostic performance, this paper proposes a dual-domain anti-noise encoder with co-attention hybrid decoder. First, the model constructs a dual-domain anti-noise encoder: its time-domain residual shrinkage branch adaptively learns thresholds to suppress interference, while the wavelet-domain differentiable wavelet convolution branch captures multi-scale frequency structures, jointly achieving a robust multi-domain feature representation. Second, a dual-domain co-attention module is designed to realize dynamic coordination and adaptive fusion of time-domain and wavelet-domain features via bidirectional interaction and gated regulation, enhancing feature fusion capability under high noise. Finally, a gated recurrent Transformer decoder component is developed, deeply integrating the Transformer＇ s self-attention mechanism with the GRU＇ s recurrent gated mechanism. This achieves an efficient balance between global modeling and local temporal dependency extraction within a unified feature space. Experiments on the Case Western Reserve University and Paderborn University bearing dataset demonstrate that the proposed model achieves 100% accuracy under standard conditions and maintains high accuracy even under strong noise, validating its superior anti-noise performance and robustness. -
图 6 原始信号与−10 dB信噪比下的信号对比((a) 内圈故障;(b) 外圈故障;(c) 滚动体故障;(d) 正常状态)
Fig. 6 Signal comparison(original vs −10 dB SNR)((a) Inner race fault; (b) Outer race fault; (c) Rolling element fault; (d) Normal state)
表 1 模型参数
Table 1 Model parameters
层名称 滤波
器数卷积核大小/
步长单元数 输入
大小输出
大小激活
函数卷积层1 50 20/2 — 1×2000 50×991 tanh 小波变换1 — — — 50×991 100×495 — 卷积层2 30 10/2 — 100×495 30×243 tanh 小波变换2 — — — 30×243 60×121 — 卷积层3 50 6/1 — 60×121 50×116 tanh 自适应池化1 — — — 50×116 1×128 — 残差收缩块1 64 7/2 — 1×2000 64× 1000 ReLU 残差收缩块2 128 7/2 — 64× 1000 128×500 ReLU 残差收缩块3 128 7/1 — 128×500 128×500 ReLU 自适应池化2 — — — 128×500 1×128 — 协同注意力 — — 4 2×1×128 1×128 — 门控循环
Transformer1— — 4 1×128 1×128 — 门控循环
Transformer2— — 4 1×128 1×128 — 全局池化 — — 10 1×128 1×128 — Dropout层 — — — 1×128 1×128 — 全连接层 — — — 1×128 1×10 Softmax 
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表 2 故障样本信息
Table 2 Fault sample information
轴承状态 标签 故障(英寸) 样本数(个) 数据长度 负载区 滚动体故障 0 0.007 240 2000 — 滚动体故障 1 0.014 240 2000 — 滚动体故障 2 0.021 240 2000 — 内圈故障 3 0.007 240 2000 — 内圈故障 4 0.014 240 2000 — 内圈故障 5 0.021 240 2000 — 正常状态 6 — 240 2000 — 外圈故障 7 0.007 240 2000 6:00 外圈故障 8 0.014 240 2000 6:00 外圈故障 9 0.021 240 2000 6:00
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表 3 特征评价指标
Table 3 Feature evaluation metrics
特征类型 类间距离 类内距离 分离度 轮廓系数 原始特征 8.62 35.84 0.24 −0.19 学习特征 16.73 1.60 10.48 0.86
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表 4 消融实验准确率对比(%)
Table 4 Accuracy comparison in ablation experiment(%)
模型 准确率 A0 61.37 A1-T 80.43 A1-W 78.92 A2 88.19 A3-T2W 92.24 A3-W2T 91.62 A3-BI 93.60 A4 95.51 A5 96.80
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表 5 对比实验结果
Table 5 Comparative analysis
模型 参数量 训练时间(s) −10 dB −8 dB −4 dB 0 dB 4 dB 1D-CNN 117722 43.51 61.37% 70.23% 78.72% 82.11% 83.80% ResNet 111402 52.07 72.48% 80.66% 91.35% 93.92% 94.85% Dconformer 706371 213.00 79.94% 87.21% 97.88% 98.84% 99.06% MSD_CNN 303837 90.82 86.37% 90.32% 98.44% 99.03% 99.17% DDAE-CAHD Net 1104890 234.93 96.80% 98.75% 99.58% 100.00% 100.00%
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表 6 故障样本信息
Table 6 Fault sample information
类别 状态描述 故障位置 损伤组合 损伤程度 样本数 数据长度 1 健康 - - - 500 2000 2 疲劳:点蚀 外圈 单一损伤 1 500 2000 3 塑性变形:压痕 外圈 单一损伤 1 500 2000 4 塑性变形:压痕 内圈/外圈 多重损伤 2 500 2000 5 疲劳:点蚀 内圈 单一损伤 3 500 2000 6 疲劳:点蚀 内圈 单一损伤 1 500 2000
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表 7 对比实验结果
Table 7 Comparative analysis results
模型 −10 dB −8 dB −4 dB 0 dB 4 dB 1D-CNN 61.29% 69.80% 82.27% 89.75% 89.99% ResNet 69.58% 76.92% 87.09% 92.01% 92.19% Dconformer 77.37% 80.81% 88.93% 94.79% 95.00% MSD_CNN 81.77% 85.25% 92.71% 97.59% 97.64% DDAE-CAHD Net 93.96% 95.06% 98.57% 99.31% 99.40%
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