The noise within train is a paradox; while harmful to passenger health, it is useful to operators as it provides insights into the working status of vehicles and tracks. Recently, methods for identifying defects based on interior noise signals are emerging, among which representation learning is the foundation for deep neural network models to understand the key information and structure of the data. To provide foundational data for track fault detection, a representation learning framework for interior noise, named the interior noise representation framework, is introduced. The method includes: (i) using wavelet transform to represent the original noise signal and designing a soft and hard denoising module for dataset denoising; (ii) deep residual convolutional denoising variational autoencoder (VAE) module performs representation learning with a VAE and deep residual convolutional neural networks, enabling richer data augmentation for sparsely labeled samples by manipulating the embedding space; (iii) deep embedding clustering submodule balances the representation of reconstruction and clustering features through the joint optimization of these aspects, categorizing metro noise into three distinct classes and effectively discriminating significantly different features. The experimental results show that, compared to traditional mechanism-based models for characterizing interior noise, this approach offers a data-driven general analysis framework, providing a foundational model for downstream tasks.

中文翻译：

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基于变分自编码器和深度嵌入聚类的地铁车内噪声自监督表示学习


火车内的噪音是一个悖论；虽然对乘客健康有害，但对操作员来说很有用，因为它可以深入了解车辆和轨道的工作状态。近年来，基于内部噪声信号识别缺陷的方法不断涌现，其中表示学习是深度神经网络模型理解数据关键信息和结构的基础。为了给轨道故障检测提供基础数据，引入了一种内部噪声表示学习框架，称为内部噪声表示框架。该方法包括：(i)利用小波变换表示原始噪声信号，设计软硬去噪模块对数据集去噪。 (ii) 深度残差卷积去噪变分自动编码器（VAE）模块使用 VAE 和深度残差卷积神经网络执行表示学习，通过操纵嵌入空间为稀疏标记的样本提供更丰富的数据增强； （iii）深度嵌入聚类子模块通过这些方面的联合优化来平衡重建和聚类特征的表示，将地铁噪声分为三个不同的类别，并有效地区分显着不同的特征。实验结果表明，与传统的基于机制的内部噪声表征模型相比，该方法提供了数据驱动的通用分析框架，为下游任务提供了基础模型。