International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
www.irjet.net
Self-Healing Power Backup Systems: AI-Driven Digital Twin with Autonomous Actuation for Enterprise IT Environments Ramnath Rajendran Lead Design Lab Test Engineer Eaton Corporation, United States of America -----------------------------------------------------------------------***-------------------------------------------------------------------------Abstract Power backup systems are mission-critical for ensuring uninterrupted operations in enterprise IT environments, where even short disruptions can cause significant downtime and data loss. Current industry solutions, such as those enabled by platforms like Eaton’s Brightlayer and similar predictive analytics frameworks, leverage artificial intelligence (AI) and digital twin technologies to monitor infrastructure, predict failures, and optimize maintenance schedules. While these approaches enhance visibility and proactive decision-making, they remain primarily advisory in nature providing predictions and recommendations but relying on human intervention for corrective actions. This paper proposes an advanced framework that extends beyond predictive monitoring by introducing a self-healing digital twin integrated with an actuation layer. In this model, the digital twin not only simulates and predicts potential system failures but also triggers autonomous corrective actions such as dynamic load redistribution, automated UPS/generator switching, and adaptive battery management. By closing the loop between prediction and action, the framework evolves from reactive and advisory systems to a fully autonomous, self-healing infrastructure. Through this approach, enterprise IT environments can achieve higher resilience, reduced downtime, extended asset life, and improved operational efficiency. The proposed framework represents a shift from today’s predictive maintenance paradigm toward autonomous resilience, bridging a critical gap in the current state of power backup management. Keywords: Self-Healing Systems, Digital Twin, Autonomous Actuation, Power Backup, Enterprise Resilience
1. Introduction Why Self-Healing is the Next Step The reliable supply of power is the foundational requirement for modern Enterprise Information Technology (IT) environments, particularly for data centers and mission-critical operations. Even brief interruptions to power or degraded performance of power infrastructure assets can result in catastrophic data loss, regulatory penalties, and significant financial impact (Jones et al., 2022). Consequently, sophisticated Power Backup Systems (PBS), comprising Uninterruptible Power Supplies (UPS), generators, and associated battery and switching gear, are standard operational necessities. Limitations of Current Monitoring and Rule-Based Automation The current state of the art in PBS management has advanced significantly, moving away from purely reactive maintenance to sophisticated predictive frameworks. Industry leaders, such as Eaton with its Brightlayer platform, leverage Artificial Intelligence (AI) and machine learning to analyze real-time telemetry data. These systems excel at predictive maintenance (PdM), providing early warnings of component degradation (e.g., battery health, capacitor failure) and offering detailed insights into operational efficiency (Eaton, 2023). Furthermore, many systems incorporate basic rule-based automation, which triggers pre-defined switching sequences in response to known fault conditions, such as utility grid failure. However, a fundamental limitation persists: these systems are primarily advisory and predictive (Smith & Chen, 2021). They generate high-fidelity alerts and recommendations for example, "Battery set A is predicted to fail within 30 days" or "Current load is above optimal PUE" but the decisive corrective action remains dependent on human operators. A significant portion of the system response requires manual intervention for triage, validation, and implementation of complex corrections, such as dynamic load shifting or proactive asset isolation (Gupta et al., 2020).
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