BEARING-FDD: An early detection and diagnosis tool for bearing faults in rotating machinery (bibtex)

by Luis Magadán, Cristóbal Ruiz-Cárcel, Juan Carlos Granda, Francisco José Suárez, Alonso Menéndez-González and Andrew Starr

Abstract:

This paper presents the design and implementation of a web tool offering an innovative method for detecting, diagnosing and classifying bearing faults in rotating machinery under limited data conditions, providing explainability and interpretability of the results obtained. The tool uses a machine learning model to detect and diagnose bearing faults. A monotonic smoothed stacked autoencoder builds a health indicator without requiring feature extraction, making the tool useful without the need for specialized staff. The tool generates explainability and interpretability reports with a correlation analysis between the health indicator and well-known engineering features and easily interpretable details on the diagnosed faults. The tool includes the option to use preloaded state-of-the-art datasets, while also allowing users to upload their own datasets to analyze vibration data from real industrial equipment.

Reference:

BEARING-FDD: An early detection and diagnosis tool for bearing faults in rotating machinery (Luis Magadán, Cristóbal Ruiz-Cárcel, Juan Carlos Granda, Francisco José Suárez, Alonso Menéndez-González and Andrew Starr), In Software Impacts, volume , 2025.

Bibtex Entry:

@Article{magadan2025si,
  author  = {Luis Magadán and Cristóbal Ruiz-Cárcel and Juan Carlos Granda and Francisco José Suárez and Alonso Menéndez-González and Andrew Starr},
  journal = {Software Impacts},
  title   = {BEARING-FDD: An early detection and diagnosis tool for bearing faults in rotating machinery},
  volume       = {},
  number       = {},
  pages        = {},
  abstract     = {This paper presents the design and implementation of a web tool offering an innovative method for detecting, diagnosing and classifying bearing faults in rotating machinery under limited data conditions, providing explainability and interpretability of the results obtained. The tool uses a machine learning model to detect and diagnose bearing faults. A monotonic smoothed stacked autoencoder builds a health indicator without requiring feature extraction, making the tool useful without the need for specialized staff. The tool generates explainability and interpretability reports with a correlation analysis between the health indicator and well-known engineering features and easily interpretable details on the diagnosed faults. The tool includes the option to use preloaded state-of-the-art datasets, while also allowing users to upload their own datasets to analyze vibration data from real industrial equipment.},
  author+an    = {3=highlight},
  year    = {2025},
  date         = {2025},
  doi     = {},
  keywords = {Fault Diagnosis, Fault Classification, Rotating Machinery, Interpretable AI, Explainable AI},
  issn    = {2665-9638},
  jcr     = {1.2 -- Q4 [2024]},
  file    = {revistas/magadan2025si.pdf}
}