Case Study — AI

AI Predictive Maintenance Platform

An IoT sensor analytics dashboard using LSTM neural networks to predict mechanical failures in manufacturing machinery.

Project Overview

An industrial IoT dashboard designed for factory engineers. The platform connects to vibration and temperature sensors, analyzing historical trends to identify mechanical wear and estimate remaining useful life (RUL) before failures happen.

Key Features & Scope

Real-time sensor graphs rendering 100+ points per second via canvas rendering

Predictive health scores calculating machinery decay rates

Automated service ticket creation linked to enterprise maintenance tools

Secure device registry managing IoT tokens and device credentials

System Architecture

Node.js WebSocket server streams telemetry data from IoT hubs. Historical analytics are queries from InfluxDB time-series storage, feeding predictions via TensorFlow microservices.

Client InterfaceNext.js / React
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Backend CoreNode.js / Express
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Database NodeInfluxDB & MongoDB
Database: InfluxDB & MongoDB
Deployment: Google Cloud IoT Core & GKE
95
Performance
95
Accessibility
98
Best Practices
100
SEO
Verified Production Metrics
ReactInfluxDBNode.jsPython TensorFlowWebSocketsTypeScript

Keywords and concepts covered in this project case study:

AI Predictive Maintenance IoTInfluxDB real-time telemetry dashboardTensorFlow LSTM factory predictionWebSocket IoT sensor monitoring

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