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Factory-Based Vibration Data for Bearing-Fault Detection

Author

Listed:
  • Adam Lundström

    (Department of Computer and Electrical Engineering, Mid Sweden University, 85170 Sundsvall, Sweden
    Svenska Cellulosa Aktiebolaget, SCA, 85188 Sundsvall, Sweden)

  • Mattias O’Nils

    (Department of Computer and Electrical Engineering, Mid Sweden University, 85170 Sundsvall, Sweden)

Abstract

The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of data to perform well. This is a reason why publicly available data are important, and there currently exist several open datasets that contain different conditions and faults. However, one challenge is that almost all of these data come from a laboratory setting, where conditions might differ from those found in an industrial environment where the methods are intended to be used. This also means that there may be characteristics of the industrial data that are important to take into account. Therefore, this study describes a completely new dataset for bearing faults from a pulp mill. The analysis of the data shows that the faults vary significantly in terms of fault development, rotation speed, and the amplitude of the vibration signal. It also suggests that methods built for this environment need to consider that no historical examples of faults in the target domain exist and that external events can occur that are not related to any condition of the bearing.

Suggested Citation

  • Adam Lundström & Mattias O’Nils, 2023. "Factory-Based Vibration Data for Bearing-Fault Detection," Data, MDPI, vol. 8(7), pages 1-9, June.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:7:p:115-:d:1181205
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    References listed on IDEAS

    as
    1. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
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