Self-Directed Digital Frameworks for Intelligent Fault Correction via Adaptive Decision Models and Strong System Stability

Arjun Mehta; Sneha Iyer; Rahul Khanna

Authors

Arjun Mehta Department of Computer Science & Artificial Intelligence, Indian Institute of Technology Delhi (IIT Delhi), New Delhi, India
Sneha Iyer School of Artificial Intelligence and Data Science, Indian Institute of Technology Bombay (IIT Bombay), Mumbai, Maharashtra, India
Rahul Khanna Department of Machine Learning and Intelligent Systems, Indian Institute of Science (IISc), Bengaluru, Karnataka, India

Keywords:

Self-healing systems, adaptive decision models, system resilience, fault correction

Abstract

Modern critical infrastructures, including power grids, cloud systems, and cyber-physical networks, are increasingly exposed to complex and cascading faults that cannot be effectively handled using static rule-based recovery mechanisms. These systems require adaptive, self-directed digital frameworks capable of autonomous fault detection, diagnosis, and correction while maintaining high levels of operational stability. This paper proposes a conceptual and analytical framework for intelligent fault correction using adaptive decision models inspired by resilience theory, system vulnerability analysis, and behavioral robustness principles drawn from interdisciplinary studies on resilience in both engineered and human systems (Stanković et al., 2023; Mahzarnia et al., 2020; Rice & Liu, 2016).

The proposed framework integrates adaptive decision-making layers with real-time system monitoring to enable self-directed recovery actions under dynamic and uncertain conditions. The model leverages principles from resilience quantification in power systems, where system robustness is evaluated through fragility curves, redundancy mapping, and recovery trajectories (Panteli et al., 2016). Additionally, graph-theoretic vulnerability analysis is incorporated to identify critical failure nodes and optimize corrective responses (Biswas et al., 2020).

The study further extends traditional resilience engineering by embedding behavioral resilience analogies, emphasizing adaptability, recovery capacity, and stress tolerance as transferable system properties (Pietrzak et al., 2009; Green et al., 2010). By mapping these properties into computational decision models, the framework enables systems to dynamically adjust fault correction strategies in real time.

The results of this conceptual synthesis demonstrate that self-directed digital frameworks significantly enhance system stability, reduce recovery latency, and improve fault isolation accuracy compared to static and semi-static approaches. The findings highlight the importance of adaptive intelligence in next-generation resilient infrastructures and establish a foundation for future research in autonomous system stabilization.

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