Year: 2025
Type: article
Abstract: Predictive maintenance powered by artificial intelligence has revolutionized website reliability and operational efficiency across industries. This transformative approach integrates advanced machine learning algorithms, anomaly detection systems, and sophisticated data collection architectures to prevent failures before they occur. The implementation of AI-driven maintenance strategies has demonstrated significant improvements in equipment longevity, resource optimization, and cost reduction while enhancing system availability and performance. Through behavioral analysis and vulnerability prevention mechanisms, these systems strengthen security measures and enable proactive threat detection. The evolution of predictive maintenance incorporates emerging technologies such as reinforcement learning, federated learning, and automated incident response capabilities, setting new standards for maintenance practices. By following comprehensive implementation guidelines and best practices in data collection, model development, and operational integration, organizations can successfully transition from reactive to proactive maintenance approaches, ensuring optimal system performance and reliability while substantially reducing operational costs and minimizing unplanned downtime.
Author: Shailesh Kumar Agrahari
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407594894