Prepared by: Asst. Lecturer Al-Batool Abdulmahdi Saleh The rapid development and increasing complexity of digital systems have made fault detection using traditional methods more difficult. Artificial intelligence has emerged as an effective solution for improving the accuracy and speed of fault detection and for predicting faults before they occur. This article aims to review the concept of fault detection in digital systems and clarify the role of artificial intelligence techniques in enhancing the reliability of these systems, while presenting the most prominent practical applications and future challenges. Digital systems are a fundamental part of modern infrastructures and are used in various fields such as computers, communications, industrial systems, and medical systems. As these systems become more complex, faults become harder to detect and manage. Therefore, researchers have turned to artificial intelligence techniques due to their ability to learn, perform self-analysis of data, and detect abnormal patterns. Concept of Fault Detection in Digital Systems Fault detection is the process of identifying errors or deviations that affect the normal performance of a digital system. These faults may result from: • Hardware faults • Software faults • Communication or signal faults The primary objective of fault detection is to reduce downtime and improve system reliability. Role of Artificial Intelligence in Fault Detection . Machine Learning Machine learning is used to analyze system data and extract normal operating patterns, which are then compared with current behavior to detect any deviations. Examples of commonly used algorithms include: • Artificial neural networks • Decision trees • Clustering algorithms . Deep Learning Deep learning is characterized by its high capability to process complex and large-scale data, such as sensor signals and time-series data. It is widely used in advanced digital systems for automatic fault detection without direct human intervention. Expert Systems Expert systems rely on predefined knowledge bases and stored human expertise. They are used to diagnose faults based on known scenarios, especially in industrial systems. Applications of Artificial Intelligence in Fault Detection • Digital industrial systems: Monitoring production lines and detecting faults at an early stage. • Embedded systems: Enhancing the reliability of microcontrollers and digital circuits. • Computer networks: Detecting faults and attacks that affect network performance. • Digital medical systems: Monitoring medical devices and ensuring their safe operation. Benefits of Using Artificial Intelligence Artificial intelligence contributes to: • Reducing fault detection time. • Improving diagnostic accuracy compared to traditional methods. • Predicting faults before they occur (predictive maintenance). • Reducing operational costs and increasing reliability. Future Challenges Despite its significant advantages, artificial intelligence applications face several challenges, including: • The need for large and accurate training datasets. • The complexity of AI models and the difficulty of interpreting their decisions. • High computational requirements in real-time systems. Artificial intelligence has proven its effectiveness in fault detection in digital systems and in improving their performance and reliability. With the continued development of machine learning and deep learning techniques, these systems are expected to become more intelligent and autonomous in the future, contributing to reduced faults and enhanced efficiency of modern digital systems.