Abstract:

Gearbox fault diagnosis plays a crucial role in ensuring the reliable and efficient operation of machinery. Traditional fault diagnosis methods often rely on manual inspection and experience-based techniques, which can be time-consuming, subjective, and limited in their ability to detect subtle faults. In recent years, machine learning techniques have emerged as a promising approach for gearbox fault diagnosis, offering the potential for automated and accurate fault detection. Comprehensive review of the application of machine learning algorithms for gearbox fault diagnosis. Additionally, the paper discusses the challenges and considerations associated with gearbox fault diagnosis using machine learning. These challenges include data acquisition and preprocessing, feature selection, model training, and the interpretability of the results. Machine learning techniques offer significant potential for gearbox fault diagnosis, providing automated and accurate detection of faults. Further research and development in this area can contribute to the advancement of predictive maintenance strategies enhancing the efficiency of machinery in various industrial applications

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