AI and Machine Learning for Intelligent Control & Fault Diagnosis

Abstract

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of intelligent control systems and fault diagnosis. Modern industrial systems, robotics, power systems, and transportation networks are becoming increasingly complex, making conventional control and fault detection methods less effective. AI and ML techniques provide adaptive, predictive, and real-time solutions, improving system efficiency, safety, and reliability. This paper reviews the current state of-the-art AI and ML approaches applied to intelligent control and fault diagnosis, highlighting their advantages, limitations, and future research directions. Various methods, including neural networks, fuzzy logic, support vector machines, and deep learning, are discussed in detail. The study also includes examples of industrial applications, comparative analysis, and future perspectives.

KEYWORDS: Artificial intelligence, Machine learning, Intelligent control, Fault diagnosis, Neural networks, Predictive maintenance, Industrial automation