Toward Multimodal Industrial Fault Analysis: A Single-Speed Chain Conveyor Dataset with Audio and Vibration Signals

2026-03-07

AI Summary

This work addresses the challenge of robust system-level fault detection for industrial single-speed chain conveyors under diverse operating conditions and realistic noise environments. To this end, we present a real-world-oriented multimodal fault dataset that simultaneously captures multichannel audio and vibration signals across normal operation and four representative fault types. Data were recorded under varying conveyor speeds, load levels, and factory-replicated ambient noise conditions. The dataset supports channel-wise analysis, multimodal fusion, unsupervised anomaly detection, and supervised classification. It also provides standardized data splits and a unified channel-level kNN baseline, establishing a reliable and extensible benchmark platform for evaluating the generalization capabilities of fault detection algorithms.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• multimodal
• industrial fault analysis
• chain conveyor
• audio and vibration signals
• fault detection

Innovation

• multimodal fusion
• industrial fault detection
• unsupervised anomaly detection
• channel-wise analysis
• benchmark dataset