Predictive Analytics and Computational Intelligence for Proactive System Resilience Engineering
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I3P102Keywords:
Predictive Analytics, Computational Intelligence, System Resilience, Machine Learning, Fuzzy Logic, Genetic Algorithm, Proactive Maintenance, Cyber-Physical Systems, Resilience EngineeringAbstract
In the modern age of digital transformation, sophisticated engineered systems, including cyber-physical infrastructures or smart manufacturing ecologies, require resilience mechanisms that are robust enough to predict and respond to disruptions. Predictive Analytics and Computational Intelligence (CI) have become formidable paradigms to develop proactive resiliency architectures, which break the past resilience designs based on reactive maintenance and fault-tolerant mechanisms. In this paper, the author investigates how proactive system resilience can be engineered in a synergistic context by jointly integrating data-driven predictive modeling, artificial intelligence, and computational optimization. Predictive analytics uses multivariate time series, anomaly detection models and machine learning based forecasting to predict system failure, and computational intelligence to make adaptive decisions in the presence of any uncertainty via evolutionary computation, use of fuzzy logic and neural network-based reasoning. The suggested methodology, proposes a predictive resilience model hybrid framework based on a combination of predictive models (via deep recurrent neural network) and computational intelligence modules (via genetic algorithms and fuzzy logic controllers). This integration allows the ongoing prediction of risks, self-healing, and optimization of the performance of the system even in the conditions of changing environmental and operational stresses. Another new Resilience Index (RI) formulation emerging in the research is the capacity of systems in dynamic environments to adapt. Empirical studies have shown that predictive-intelligent models have lowered down time by 37, fault detection accuracy by 28 and operational stability by 41 points compared with conventional resiliency models. The current work dispenses a scalable, data-centric paradigm of proactive resilience engineering and provides a roadmap to resilient smart systems, which have the ability to maintain performance in an autonomous manner. The results imply broadly to other industries such as critical infrastructure, aerospace, medicine, and autonomous cyber-physical systems
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