Enhancing Efficiency in Aerospace Manufacturing through AI Agent Based Bearing Diagnostics System

Industrial bearings are critical components in aerospace manufacturing, where their failures can result in costly downtime. Traditional fault diagnosis typically depends on time-consuming on-site inspections conducted by specialized engineers. This research introduces an innovative Artificial Intelligence application that functions as a virtual maintenance technician, empowering on-site personnel to perform preliminary diagnoses. By reducing the dependence on specialized engineers, this technology aims to minimize downtime. The AI system leverages large language models to guide the inspection process, answer queries from a comprehensive knowledge base, analyze defect images, and generate detailed reports with actionable recommendations. Multiple deep learning algorithms operate as backend APIs to support this functionality. This study details the architectural design of the expert system and provides a real-time simulation of its workflow, demonstrating its potential to enhance operational efficiency in aerospace and industrial manufacturing.