Abstract
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.
Abstract (translated)
我们介绍了一个从单速链式输送机（SSCC）系统收集的多模态工业故障分析数据集，旨在用于生产线中的系统级故障检测。该数据集包含多模态信号，包括三个音频通道和四个振动通道。它涵盖了正常操作以及在多种速度、负载条件下，并且在干净环境及真实的工厂噪音环境下再现的四种典型的故障类型。此数据集明确设计用于支持逐通道分析及多模态融合研究。 我们为无监督故障检测（仅使用正常情况作为训练数据）和有监督故障分类（跨不同操作条件和故障类型的平衡数据集分割），建立了标准化的评估协议。此外，还提供了一个统一的逐通道kNN基线方法，以便在不进行特定任务训练的情况下公平比较表示质量。 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测试。
URL
https://arxiv.org/abs/2603.07130
