ANALYSIS OF RESEARCH WORK ON EXPERT SYSTEMS
Keywords:
Expert systems, electrical equipment, artificial intelligence, predictive maintenance, fault diagnosis, machine learning, smart grid, industrial automation, electrical engineering, intelligent systems.Abstract
This paper analyzes recent developments and trends in expert systems for electrical equipment, focusing on research conducted from 2010 to 2020. The study examines the implementation of artificial intelligence-based expert systems in diagnostics, maintenance, and control of electrical equipment across various sectors including energy, industry, and transportation. Statistical analysis shows significant growth in research output, with scientific publications increasing by 150% and international conferences growing from 30 to 80 annually. The research demonstrates that expert systems have achieved 90% accuracy in fault detection while reducing diagnostic time by 50% and maintenance costs by 25%. The paper also discusses technological advances in data analytics, machine learning, and cloud computing, along with their integration into expert systems. Geographic analysis reveals leadership from the United States (35% of publications), followed by significant contributions from European and Asian institutions. The study identifies current challenges, including standardization issues and cybersecurity concerns, while highlighting future directions in self-learning systems and IoT integration.
