轻量模型LD-RPMNet实现铁路道岔电机近传感器故障诊断
LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines
- 融合Transformer与卷积网络，用多尺度深度可分离卷积和广播自注意力提升特征提取效率
- 参数量和计算复杂度降低50%，诊断准确率达98.86%，较之前提升近3%
- 适合资源受限的工业现场部署，尤其适用于铁路道岔电机实时故障检测
近传感器诊断在工业中日益普及。本文提出一种轻量级模型LD-RPMNet，融合Transformer与卷积神经网络，结合局部与全局特征提取，优化计算效率以适配实际铁路应用场景。该模型引入多尺度深度可分离卷积（MDSC）模块，将跨通道卷积分解为逐点与深度卷积，并采用多尺度核增强特征提取；同时引入广播自注意力（BSA）机制，简化复杂矩阵运算，提升计算效率。基于道岔电机运行时采集的声音信号实验结果表明，优化后的模型参数量和计算复杂度降低50%，诊断准确率提升近3%，最终达到98.86%。这验证了近传感器故障诊断在铁路道岔电机中的可行性。
原文摘要 · Abstract (English)
Near-sensor diagnosis has become increasingly prevalent in industry. This study proposes a lightweight model named LD-RPMNet that integrates Transformers and Convolutional Neural Networks, leveraging both local and global feature extraction to optimize computational efficiency for a practical railway application. The LD-RPMNet introduces a Multi-scale Depthwise Separable Convolution (MDSC) module, which decomposes cross-channel convolutions into pointwise and depthwise convolutions while employing multi-scale kernels to enhance feature extraction. Meanwhile, a Broadcast Self-Attention (BSA) mechanism is incorporated to simplify complex matrix multiplications and improve computational efficiency. Experimental results based on collected sound signals during the operation of railway point machines demonstrate that the optimized model reduces parameter count and computational complexity by 50% while improving diagnostic accuracy by nearly 3%, ultimately achieving an accuracy of 98.86%. This demonstrates the possibility of near-sensor fault diagnosis applications in railway point machines.
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