AI-Driven IoT Sensor System for Smart Infrastructure Monitoring and Predictive Maintenance

Abstract

The present invention provides an AI-driven IoT sensor system designed for smart infrastructure monitoring and predictive maintenance. The system integrates a network of IoT-enabled sensors deployed across critical infrastructure components to capture real-time data on parameters such as vibration, temperature, pressure, structural strain, and environmental conditions. The collected data is transmitted to a cloud-based or edge-computing platform, where advanced artificial intelligence and machine learning algorithms analyze the information to detect anomalies, predict potential failures, and assess maintenance requirements. The system offers features such as early fault detection, automated maintenance scheduling, and optimization of resource allocation, significantly reducing downtime and maintenance costs. With its scalable architecture, the solution adapts to diverse infrastructures, including bridges, buildings, industrial plants, and energy grids. This innovative approach ensures enhanced operational reliability, improved safety, and extended lifecycle for infrastructure assets, promoting sustainability and cost efficiency in infrastructure management.

Specification

Description:The present invention provides an AI-driven IoT sensor system designed for smart infrastructure monitoring and predictive maintenance. The system integrates a network of IoT-enabled sensors deployed across critical infrastructure components to capture real-time data on parameters such as vibration, temperature, pressure, structural strain, and environmental conditions. The collected data is transmitted to a cloud-based or edge-computing platform, where advanced artificial intelligence and machine learning algorithms analyze the information to detect anomalies, predict potential failures, and assess maintenance requirements. The system offers features such as early fault detection, automated maintenance scheduling, and optimization of resource allocation, significantly reducing downtime and maintenance costs. With its scalable architecture, the solution adapts to diverse infrastructures, including bridges, buildings, industrial plants, and energy grids. This innovative approach ensures enhanced operational reliability, improved safety, and an extended lifecycle for infrastructure assets, promoting sustainability and cost efficiency in infrastructure management. , Claims:1. A smart infrastructure monitoring system comprising a network of IoT sensors, a cloud-based platform, and AI algorithms to predict failures and schedule proactive maintenance.
2. The system of claim 1, wherein the IoT sensors collect data related to at least one of the following parameters: vibration, temperature, humidity, strain, and pressure.
3. The system of claim 2, wherein the AI algorithms include an anomaly detection model and a predictive maintenance model.
4. The system of claim 2, wherein edge devices perform preliminary data processing to reduce latency and bandwidth usage.
5. A method for predictive maintenance of infrastructure using IoT sensors and AI models, comprising the steps of data collection, edge processing, transmission to a cloud platform, AI analysis, and generating maintenance recommendations.
6. The system of claim 3, wherein the cloud-based platform provides a user interface to display real-time infrastructure health and predictive insights.
A smart infrastructure monitoring system, comprising:
• A plurality of IoT sensors configured to monitor structural parameters, including vibration, strain, temperature, and moisture;
• A communication module configured to transmit sensor data wirelessly to a cloud-based platform;
• An AI-based analytics engine configured to detect anomalies and predict potential infrastructure failures; and
• An alert mechanism configured to notify maintenance personnel based on predictive analytics outputs.

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