Interactive Demo: AI-Enhanced Predictive Maintenance
Real-Time Equipment Monitoring with Hybrid Statistical & AI Analysis
Industrial equipment failures result in costly unplanned downtime, safety incidents, and production losses. Traditional maintenance approaches are either reactive (fixing after failure) or time-based (preventive scheduling), both of which are inefficient and costly. Modern industrial facilities need:
- •Real-time monitoring: Continuous tracking of equipment health across thousands of sensors
- •Early warning systems: Detect anomalies before they cause failures
- •Intelligent analysis: Understand complex degradation patterns and failure modes
- •Actionable recommendations: Clear maintenance guidance with cost-benefit analysis
This interactive demonstration showcases an AI-enhanced predictive maintenance pipeline. Using a hybrid approach, it combines deterministic statistical methods for reliable anomaly detection and trend analysis with AI-powered recommendations for complex decision-making. This architecture is more robust and cost-effective than pure AI approaches:
Real-Time Data Streaming
Server-Sent Events (SSE) stream live sensor data from a simulated centrifugal pump
Statistical Anomaly Detection
Deterministic analysis continuously monitors for deviations and threshold violations
Mathematical Trend Analysis
Calculates degradation rates and predicts equipment health trajectories
AI-Powered Recommendations
GPT-5 generates contextual maintenance actions with cost-benefit analysis
Hybrid Architecture: Deterministic + AI
This demo uses a three-stage pipeline: (1) Statistical Anomaly Detection - z-score analysis and threshold monitoring (deterministic, explainable), (2) Mathematical Trend Analysis - degradation tracking and RUL prediction (deterministic calculations), (3) AI-Powered Maintenance Planning - GPT-5 generates contextual recommendations (AI for complex decision-making). This hybrid approach provides reliable detection with intelligent, cost-effective recommendations.
Normal Operation (Clean Start)
The demo starts with a simulated centrifugal pump in normal operating condition. Even without anomalies, the system includes natural degradation (0.2% per reading) to simulate realistic equipment wear over time. The Degradation metric on the dashboard shows this cumulative wear factor as a percentage (0-100%).
Demo Duration & Timeline
The demo runs for 2 minutes (120 seconds) with sensor readings every 1.5 seconds. At 30 seconds, the AI generates baseline maintenance recommendations. Additional recommendations may be triggered if anomalies are detected or degradation becomes significant.
Interactive Anomaly Injection
While the demo runs, you can inject anomalies (Temperature Surge, Cavitation, Vibration Spike) to test how the system responds. Each anomaly lasts 30 seconds and triggers statistical analysis and AI recommendations. This lets you see how the hybrid pipeline handles both normal wear and unexpected equipment issues.
Dashboard Metrics Explained
- • Elapsed Time: Time since demo started (MM:SS format)
- • Readings: Total sensor readings collected (one per 1.5 seconds)
- • Degradation: Cumulative equipment wear (0-100%), increases naturally over time
- • Progress: Demo completion percentage (0-100%)
💡 Pro Tip: Try running the demo multiple times with different scenarios: once without any anomalies (clean operation), once with a single anomaly injection, and once with multiple anomalies to see how the AI adapts its recommendations.
Try the Interactive Demo
Watch real-time statistical analysis and AI-powered recommendations
Click "Start Demo" below • Optionally inject anomalies • Watch AI generate recommendations
Waiting for sensor data...
Start the demo to see real-time charts
Waiting for sensor data...
Start the demo to see real-time charts
Waiting for sensor data...
Start the demo to see real-time charts
Waiting for sensor data...
Start the demo to see real-time charts
Downtime Reduction
Predict failures before they occur
Maintenance Cost Savings
Optimize maintenance scheduling
Failure Prevention
Catch issues before critical failure
Data Simulation & Streaming
Realistic centrifugal pump simulator generates sensor data (vibration, temperature, pressure, flow rate) with physics-based correlations and configurable anomalies. Data streams via Server-Sent Events (SSE) every 1.5 seconds.
Statistical Anomaly Detection
First stage uses deterministic z-score analysis and moving averages to detect unusual patterns, spikes, or threshold violations in real-time. Flags anomalies by severity (warning, critical). This statistical approach is explainable and reliable.
Mathematical Trend Analysis
Second stage uses mathematical calculations to identify degradation trends, calculate rates of change, and estimate remaining useful life (RUL). Deterministic predictions of when equipment will reach critical thresholds.
AI-Powered Maintenance Planning
Third stage uses GPT-5 to synthesize anomaly and trend data, generating prioritized maintenance recommendations with detailed reasoning, failure mode analysis, and cost-benefit estimates. AI is applied strategically where complex decision-making adds the most value.
Technology Stack: Built with Next.js 15, TypeScript, Server-Sent Events (SSE), recharts for visualization, and OpenAI GPT-5 for intelligent recommendations.