Predictive Vehicle Maintenance: Application of Machine Learning Techniques for Acoustic Analysis of Failures
Keywords:
Predictive vehicle maintenance, Industry 4.0, Machine Learning, Sound analysisAbstract
The Industry 4.0 has brought changes to the automotive sector, especially in vehicle maintenance, allowing the transition from reactive methods to predictive and personalized maintenance. Therefore, this work investigates the application of audio analysis for the diagnosis of anomalies in internal combustion engines. The proposed approach used a dataset of 537 audio files, from which features such as Mel-Frequency Cepstral Coefficients (MFCCs), Zero-Crossing Rate (ZCR), and Spectral Centroid were extracted to classify engine sounds with the Random Forest algorithm. The model demonstrated high effectiveness, achieving an accuracy of 99% in differentiating healthy engines from engines with ignition faults. Thus, it is concluded that audio analysis,
combined with machine learning, is a promising strategy for automotive
diagnostics.
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