BEARING-FDD: an early detection and diagnosis tool for bearing faults in rotating machinery - Magadán, Luis; Ruiz-Cárcel, Cristobal; Granda, Juan C.; Suárez, Francisco J.; Menéndez-González, A; Starr, Andrew G. (Elsevier, 2026)
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.
Refers to: Explainable and interpretable bearing fault classification and diagnosis under limited data, https://dspace.lib.cranfield.ac.uk/handle/1826/23234
Keywords: 46 Information and Computing Sciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; 4.1 Discovery and preclinical testing of markers and technologies; 4609 Information systems; 4612 Software engineering; Fault diagnosis; Fault classification; Rotating machinery; Interpretable AI; Explainable AI