being utilized. It focuses on the difficulties that are brought about by faults and breakdowns in wind turbines, which are essential components of mechanical systems, notably in the context of wind power generation. For the purpose of maintaining the secure and dependable operation of large-scale wind farms, the passage emphasizes the need of effective problem detection and diagnosis. The authors demonstrate the application of advanced machine learning techniques in the mechanical domain by developing an efficient deep learning solution that makes use of a convolutional neural network (CNN). This solution is developed in order to address the problem that has been presented. In addition, the section discusses defect diagnostic methods and models that may be used to a variety of components, including bearings, pumps, and power transformers. This highlights the adaptability of machine learning approaches across a variety of mechanical systems. A performance comparison is used in this passage to evaluate the accuracy of these models in fault diagnosis. This evaluation is in line with the purpose of evaluating and comparing various algorithms for fault detection and diagnosis. In addition to this, the criticism that is presented in the text reveals areas that should be improved upon, highlighting the necessity of developing Extreme Learning (EL) models that are less dependent on explicit feature selection. In general, the work that is presented in the passage makes a direct contribution to the development of machine learning techniques for the purpose of fault detection and diagnosis inside mechanical systems.