FAULT DETECTION AND CATEGORIZATION USING AN ADVANCED MACHINE LEARNING TECHNIQUE FOR INDUSTRIAL ROTATIONAL MACHINERY

Abstract

The difficulty of fault identification as well as categorization in industrial rotating machinery is fixed by this study, which introduces a revolutionary Dandelion Optimized CatBoost (DO-CB) technique. The suggested framework makes use of the CB algorithm, which is enhanced by the DO method. The first step in the suggested DO-CB approach is gathering sensor data from rotating gear to record different operational settings. To ensure robustness, the recommended approach is developed on identified data and includes a variety of fault scenarios. Additionally, the Python tool used for identifying faults and classification is the basis for the implementation of the DO-CB approach. The experimental findings show how well the suggested method works to precisely identify and classify problems in industrial rotating gear. In comparison to benchmark defect detection techniques, the suggested DO-CB approach performs better, demonstrating its capacity to manage intricate patterns and fluctuations in the data.