NodeHub develops intelligent sensors for predictive maintenance, utilizing on-device machine learning to monitor equipment conditions in real-time. This technology reduces unexpected downtime and maintenance costs by enabling timely interventions based on data from vibration, temperature, and acoustic measurements.
Funding
Funding not disclosed
Founders
Product
Problem
Unexpected equipment downtime in industrial settings leads to significant production losses and increased maintenance costs. Traditional maintenance schedules often result in unnecessary interventions or fail to address emerging issues in a timely manner.
Solution
NodeHub provides intelligent sensors and a predictive maintenance platform that enables real-time monitoring of equipment health. The sensors, available in Edge and NX models, measure vibration, temperature, acoustic emissions, and magnetic field, providing a comprehensive view of equipment conditions. On-device machine learning algorithms analyze this data to detect anomalies and predict potential failures before they occur. The platform offers a user-friendly interface for visualizing equipment status, scheduling reports, and receiving notifications via SMS, email, Slack, or Telegram. By integrating with existing SCADA and MES systems, NodeHub facilitates a seamless transition to condition-based maintenance, reducing downtime, optimizing maintenance schedules, and increasing overall productivity.
Target Audience
NodeHub targets industries such as biomass, food and beverage, and paper production, focusing on companies that rely on rotating equipment like motors, fans, compressors, pumps, and gearboxes.
Features
- Multi-sensor devices measuring vibration, temperature, acoustic emission, and magnetic field
- On-device machine learning for real-time anomaly detection
- Integration with existing SCADA and MES systems via OPC UA, MQTT, and REST API
- User-friendly platform for real-time equipment status monitoring
- Customizable reports and notifications via SMS, email, Slack, or Telegram
- Two sensor models (Edge and NX) with different communication protocols (IO-Link, WiFi, and Bluetooth)
- Battery-powered option for the NX sensor, allowing for deployments without external power
- Algorithms based on supervised and unsupervised learning