IMPROVING THE ANALYTICAL APPROACH TO RELIABILITY AND RISKS IN ENGINEERING SYSTEM USING INTELLIGENT BASED SUPER CAPACITOR

Engineering

Reliability and risk management are critical aspects of engineering systems, where failures can result in significant economic losses, safety hazards, and environmental impacts. Traditional analytical approaches often fall short in addressing the complexities and uncertainties inherent in modern engineering applications. This study explores the potential of intelligent-based supercapacitors as a transformative solution to enhance the reliability and risk analysis of engineering systems. By integrating the advanced energy storage capabilities of super capacitors with artificial intelligence techniques, this approach enables real-time monitoring, predictive maintenance, and adaptive risk mitigation. The research highlights the limitations of conventional methods and demonstrates how intelligent-based super capacitors can overcome these challenges by providing improved accuracy, efficiency, and scalability. Key focus areas include the application of machine learning algorithms for failure prediction, optimization of energy usage, and enhanced system resilience under varying operational conditions. The findings underscore the importance of adopting innovative technologies to meet the growing demands for reliable and safe engineering systems. This study contributes to the development of a robust framework for reliability and risk assessment, offering significant implications for critical sectors such as power systems, transportation, and industrial automation. The proposed approach lays the foundation for future advancements in intelligent engineering solutions, ensuring sustainable and resilient infrastructure. The conventional Cyber security Vulnerabilities causes of failure in the analytical approach to reliability and risks in engineering system was 10%. However, when an Intelligent based super capacitor was imbibed in the system, it drastically reduced it to8.7%. finally, the percentage improvement in the analytical approach to reliability and risks in engineering system was1.3%

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