构建了含音视频信号的工业故障数据集，支持多模态分析。
Toward Multimodal Industrial Fault Analysis: A Single-Speed Chain Conveyor Dataset with Audio and Vibration Signals
- 采集单速传送带的音频与振动信号，覆盖多种工况。
- 包含正常状态及四类故障，支持无监督与有监督任务评估。
- 提供统一基线，适合研究多模态融合与鲁棒性故障检测。
我们提出一个面向生产线上系统级故障检测的多模态工业故障分析数据集，来自单速链式传送带（SSCC）系统。数据集包含三路音频和四路振动信号，涵盖正常运行及四种典型故障类型，在不同速度、负载下采集，并在真实工厂噪声环境下复现。数据集设计用于通道级分析与多模态融合研究。我们制定了标准评估协议：仅用正常样本训练的无监督故障检测，以及在不同工况与故障类型间平衡划分的有监督分类。同时提供统一的通道级kNN基线，用于公平比较特征表示质量，无需特定任务训练。该数据集为鲁棒多模态工业故障分析提供了实用且可扩展的基准。
原文摘要 · Abstract (English)
We introduce a multimodal industrial fault analysis dataset collected from a single-speed chain conveyor (SSCC) system, targeting system-level fault detection in production lines. The dataset consists of multimodal signals, including three audio and four vibration channels. It covers normal operation and four representative fault types under multiple speeds, loads, and both clean and realistic factory-noise conditions reproduced on-site. It is explicitly designed to support channel-wise analysis and multimodal fusion research. We establish standardized evaluation protocols for unsupervised fault detection with normal-only training and supervised fault classification with balanced dataset splits across different operating conditions and fault types. A unified channel-wise kNN baseline is provided to enable fair comparison of representation quality without task-specific training. The dataset offers a practical and extensible benchmark for robust multimodal industrial fault analysis.
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