MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641099836 A) filed by Srikanth Palutla on August 18, 2026, for Adaptive Ai-Based Reliability, Anomaly Detection, And Predictive Maintenance Framework For Connected Systems.
Inventor includes Srikanth Palutla.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: ABSTRACT [505] The adaptive AI-based reliability framework introduces an innovative multi-layered monitoring architecture for connected systems that integrates real-time anomaly detection protocols with predictive maintenance intelligence, facilitating continuous health assessment, dynamic failure-risk estimation, and robust operational confirmation while maintaining seamless integration across distributed connected environments and operational accuracy for consistent industrial and enterprise applications. [510] The comprehensive reliability framework employs adaptive machine learning algorithms and intuitive sensor-data protocols, utilizing embedded computational processing arrays and energy-efficient inference engines to ensure timely fault identification, enhanced system understanding, and optimal maintenance scheduling while maintaining continuous performance monitoring capabilities across heterogeneous connected devices. [515] The integrated methodology combines multi- dimensional telemetry analysis techniques with artificial intelligence-driven pattern recognition systems, leveraging variable-precision sensor signals and multi-factor degradation indicators to optimize maintenance scheduling and diagnostic workflows for maximum predictive accuracy and minimal false-alarm uncertainty during critical operational applications. [520] The novel responsive reliability architecture features engineered high-precision anomaly detection components with specialized asset-fingerprinting protocols, enabling complex multi-stage system verification while ensuring monitoring consistency and performance optimization across various connected instruments and industrial assets without compromising system reliability. [525] The innovative design incorporates strategic validation mechanisms for enhanced fault identification and operational security, utilizing optimized multi-function inference systems and adaptive monitoring technology to ensure legitimate maintenance triggering while maintaining functionality across diverse industrial environments and deployment scenarios. [530] Implementation methodology emphasizes scalable system integration and efficient diagnostic sequences, implementing interactive monitoring measures and pattern recognition algorithms to achieve superior degradation determination, enhanced fault identification, and unauthorized-access prevention while ensuring technological simplicity during continuous operational monitoring. [535] The system demonstrates exceptional adaptability through comprehensive integration of reliability identification protocols and intelligent monitoring technologies, validating its effectiveness across various connected-asset configurations and industrial scenarios while maintaining consistent monitoring performance and operational efficiency under diverse conditions. [540] The developed framework enables sustainable and reliable operation of connected systems through streamlined, AI-powered monitoring, providing significant advantages over traditional maintenance approaches through variable validation mechanisms, adaptive identification protocols, and improved maintenance scheduling while maintaining superior detection accuracy during critical operational procedures.
Disclaimer: Curated by HT Syndication.
