Artificial Intelligence in Reliability of Engineering Design-an Overview

Abstract

This study explores the transformative role of artificial intelligence (AI) in enhancing reliability within engineering systems. It highlights various applications of AI, including predictive maintenance, real-time monitoring, and advanced data analytics, which significantly contribute to reducing system failures and improving overall performance. By leveraging historical data and current sensor inputs, AI enables engineers to predict equipment failures before they occur, optimizing maintenance schedules and extending the life of critical systems. The research also discusses AI's capabilities in data acquisition and pattern recognition, which facilitate better understanding of failure modes and inform design decisions that enhance reliability. Furthermore, the study examines the impact of AI-driven simulations and modeling on engineering design processes, allowing for virtual testing of designs under various conditions and minimizing costly iterations. The implications of AI in automated quality control and decision support systems are also addressed, underscoring its potential to improve product quality and operational efficiency. Ultimately, the findings suggest that the integration of AI technologies across engineering disciplines can lead to more robust, efficient, and sustainable systems, paving the way for future innovations in reliability engineering.