Core Features

Explore the main capabilities of the Novo AI WatchMen Platform, including AI-driven machine monitoring and predictive maintenance. Learn how to leverage these features for industrial applications.

Overview

Novo AI WatchMen Platform empowers you to monitor industrial machines in real-time, predict failures before they occur, and optimize operations using advanced AI. You gain actionable insights through intuitive dashboards, automated alerts, and customizable workflows. These core features help reduce downtime, lower maintenance costs, and boost productivity across manufacturing, energy, and logistics sectors.

WatchMen integrates seamlessly with IoT sensors and existing PLC systems. Start by connecting your devices via the dashboard at https://dashboard.example.com.

Key Features

Discover the platform's standout capabilities through these feature cards.

Real-time Monitoring

Track machine health metrics like vibration, temperature, and RPM instantly.

Predictive Maintenance

AI models forecast failures with 95% accuracy using historical data.

Data Analytics

Generate custom reports and visualize trends for better decisions.

Custom Workflows

Build tailored automation rules without coding expertise.

Real-time Machine Monitoring

Set up continuous monitoring to receive instant alerts on anomalies.

Connect Devices

Add your IoT sensors in the dashboard.

// Example API call to register a device
const response = await fetch('https://api.example.com/v1/devices', {
  method: 'POST',
  headers: { 'Authorization': `Bearer ${YOUR_API_KEY}` },
  body: JSON.stringify({
    name: 'Machine-001',
    type: 'vibration-sensor',
    location: 'Factory-Line-A'
  })
});

Configure Alerts

Define thresholds for key metrics.

View Dashboard

Access live data streams.

Alerts notify you via email, SMS, or webhooks when metrics exceed limits, such as temperature {>80°C}.

Predictive Maintenance

Leverage AI models trained on your data to predict issues proactively.

Detects imbalances early.

import requests

payload = {
    "model": "vibration-predictor",
    "data": [120.5, 115.2, 130.1]  # Recent vibration readings
}
response = requests.post('https://api.example.com/v1/predict', json=payload)
print(response.json()['failure_probability'])  # e.g., 0.12

Predicts overheating trends.

// JavaScript example
const data = { readings: [75, 78, 82, 85] };
const prediction = await fetch('https://api.example.com/v1/predict/temperature', {
  method: 'POST',
  body: JSON.stringify(data)
});