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

Zhang Chen, Yucong Zhang, Xiaoxiao Miao, Ming Li

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.

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

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.
Paper Structure (14 sections, 3 figures, 3 tables)

This paper contains 14 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dataset Overview
  4. Machine System Description
  5. Modalities and Sensors
  6. Noise Modeling and Injection
  7. Fault Types and Annotation
  8. Dataset Organization and Signal Modalities
  9. Experimental Setup
  10. Task Design and Data Partitioning
  11. Evaluation Protocols
  12. Results and Analysis
  13. Conclusion
  14. Generative AI Usage Disclosure

Figures (3)

  • Figure 1: Physical setup of the single-speed chain conveyer experimental platform.
  • Figure 2: Overall system layout and sensor configuration.
  • Figure 3: An evaluation pipeline on the SSCC dataset. For AD task, the distance to the nearest neighbor is used as the anomaly score. For FD task, the prediction is determined by majority vote among k nearest neighbors' labels.