Machine Learning-Based Fault Diagnosis for Rotating Machinery in Industrial Settings

Using a methodical approach that includes data preprocessing, feature engineering, model selection, and performance assessment, this study investigates the use of this research. We use a fictitious dataset to show how useful Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are for defect diagnosis in rotating equipment. Using concepts from vibration analysis, feature extraction may be used to collect crucial data about potential problems. The results show promise for both models in industrial defect detection, with high values for accuracy, precision, recall, F1-score, and ROC-AUC. These results have real-world implications because they provide potential for businesses to save costs, improve efficiency, and safeguard workers. To ensure the continuous development and appropriate deployment of machine learning in industrial settings, future approaches include real-world data validation, multi-sensor integration, explain ability, and ethical issues.