Proceedings of the 54th International Congress and Exposition on Noise Control Engineering

EARLY BEARINGS FAULT DETECTION USING ACOUSTIC SIGNALS AND MACHINE LEARNING ALGORITHMS

Published in 25/09/2025 - ISBN: 978-65-272-1573-8

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Paper Title
EARLY BEARINGS FAULT DETECTION USING ACOUSTIC SIGNALS AND MACHINE LEARNING ALGORITHMS
Authors
  • Letícia Lorena Conceição Araújo
  • Israel Jorge Cardenas Nunez
  • Julia Bertelli Duarte
  • Marcus Antonio Viana Duarte
Modality
Onsite-Oral
Subject area
03.1. Computational Methods & AI in Vibro-Acoustics
Publishing Date
25/09/2025
Country of Publishing
Brazil | Brasil
Language of Publishing
en-US
Paper Page
https://www.even3.com.br/anais/international-congress-exposition-noise-control-engineering/1074057-early-bearings-fault-detection-using-acoustic-signals-and-machine-learning-algorithms
ISBN
978-65-272-1573-8
Keywords
Bearings Fault Detection, Artificial Intelligence (AI), Sound-based monitoring,
Summary
Bearings are critical components in rotating machinery, and early fault detection is essential to prevent costly failures and ensure operational efficiency in industrial machines. Undetected bearing faults can lead to severe equipment damage, unscheduled stops, increased downtime, and safety risks. While traditional methods of monitoring, particularly vibration analysis, have been effective in identifying early-stage bearing issues, challenges such as fixation difficulties and sensitivity to frequency response persist. Acoustic sensors offer an interesting advantage by capturing sound emissions from the entire machine rather than just specific areas, thus providing broader analysis and allowing also non-contact measurements that alleviate fixation concerns. This research aims to harness recent advancements in artificial intelligence (AI) to develop innovative fault detection methodologies utilizing sound signals. The study investigates the effectiveness of sound-based monitoring in conjunction with Random Forest algorithms for the early detection of bearing faults, employing a SHAP Tree Explainer to enhance explainability. By utilizing machine learning techniques, this approach successfully identifies patterns and anomalies in acoustic data that may signal impending failures, indicating its potential as a dependable tool for predictive maintenance within the context of Industry 4.0.
Title of the Event
Inter-Noise 2025
City of the Event
São Paulo
Title of the Proceedings of the event
Proceedings of the 54th International Congress and Exposition on Noise Control Engineering
Name of the Publisher
Even3
Means of Dissemination
Meio Digital

How to cite

ARAÚJO, Letícia Lorena Conceição et al.. EARLY BEARINGS FAULT DETECTION USING ACOUSTIC SIGNALS AND MACHINE LEARNING ALGORITHMS.. In: Proceedings of the 54th International Congress and Exposition on Noise Control Engineering. Anais...Sao Paulo(SP) WTC Events Center, 2025. Available in: https//www.even3.com.br/anais/international-congress-exposition-noise-control-engineering/1074057-EARLY-BEARINGS-FAULT-DETECTION-USING-ACOUSTIC-SIGNALS-AND-MACHINE-LEARNING-ALGORITHMS. Access in: 03/04/2026

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