Researchers are exploring the use of acoustic signals and machine learning techniques for fault diagnosis in various mechanical systems, including electric motors and aircraft components. Different approaches are being investigated, such as the use of Mel spectrograms, convolutional neural networks, and deep learning models like LSTM and InceptionV3. The development of these methods is being supported by the creation of large-scale acoustic datasets and the integration of feature engineering and classification frameworks.