SV500 overview
SV500 is a complete condition monitoring system: signal acquisition, edge analysis, diagnostic software, data interfaces and a web UI in one DIN-class device.
What SV500 is
SV500 combines electrical signal analysis with AI to detect developing abnormalities and support condition-based maintenance. It installs in the power or control panel, reads the voltage and current that already feed the machine, and builds a asset baseline model of how that machine normally behaves.
Because the measurement point is the feeder, one device can watch equipment that is hot, submerged, rotating at height or otherwise hard to reach with vibration sensors.
Physical motor
- Voltage variations
- Operating states
- Stress events
- Maintenance and aging
- Load variations
Asset baseline model motor
- 24-bit, 8 kHz three-phase V/I waveforms
- Mathematical diagnostic algorithms
- Power spectral density analysis
- Power quality analysis
- Machine learning and predictive AI
Key capabilities
High-resolution AI analysis
Waveform-level measurement combined with digital-twin algorithms and AI analytics gives condition insight that RMS values alone cannot.
Embedded web UI
Open a browser to see live measurements, equipment status, power quality, alarms and event history. Nothing to install.
Many equipment types, one platform
Motors, transformers, VFDs, pumps, fans and chillers are monitored with the same device and interface.
Industrial networking
RS-485 and dual-port Ethernet with RSTP, Modbus TCP and RTU for straightforward integration with upper systems.
Lower maintenance cost
Maintain only when the data says so, and cut the cost of unplanned downtime.
Edge-first, cloud-ready
Analysis runs on the device; results and raw data can flow to an enterprise server, private cloud or SaaS monitoring.
Compared with periodic inspection
| Traditional approach | edgeSV approach |
|---|---|
| Periodic inspection rounds | Continuous condition monitoring |
| Fixed maintenance schedule | Condition-based maintenance |
| Sensors mounted on each machine | Non-invasive measurement at the panel |
| Manual data interpretation | AI-assisted diagnosis with named indicators |
| Limited history | Long-term trends per asset |