Assess
Which asset needs attention? Compare status, indicators and trends.
EDGE AI · MACHINE HEALTH MONITORING
Read the signal.
Read the signal.
See the change sooner.
edgeSV analyzes electrical signals directly at the edge to detect developing abnormalities early and support condition-based maintenance across motors and critical industrial equipment.
ESAelectrical signal analysisPQpower qualityTemp.temperature monitoringEdge AIlocal intelligence
01
More time to act
Spot developing issues before an unexpected stop.
02
Decisions with context
Connect asset condition, indicators and trends.
03
Scale with your operation
Start with one asset. Connect lines and sites as you grow.
Industries
Different sites. One goal:
Different sites. One goal:
keep operations moving.
Six environments where a single motor, pump or transformer failure stops far more than one machine.
Smart Factory
Process motors, conveyors, compressors and utilities behind the production line.
Explore this industry ↗Smart Building
HVAC, pumps, elevators and transformers in commercial and public buildings.
Explore this industry ↗Distribution Center
Conveyors, sorters, AS/RS and cold-chain refrigeration in logistics hubs.
Explore this industry ↗Data Center
Chillers, cooling pumps, CRAH fans and transformers behind the IT load.
Explore this industry ↗Power Plant
Feed pumps, draft fans, cooling water pumps and station transformers.
Explore this industry ↗Water Management
Intake and booster pumps, aeration blowers and treatment plant motors.
Explore this industry ↗
Why it matters
Failures rarely come without warning. The warning is usually missed.
Fixed inspection intervals and manual rounds see a machine for a few minutes a month. Degradation happens in between, and it shows up first in how the machine draws power.
Without continuous monitoring
- A hidden abnormality starts: bearing wear, misalignment, insulation stress
- Performance degrades between inspection rounds
- The equipment trips or fails unexpectedly
- Emergency repair, production loss and safety risk
With edgeSV
- Voltage and current are measured continuously at the panel
- The asset baseline model compares behavior against the asset's own baseline
- A graded alert names the likely component and indicator
- Maintenance is planned into the next scheduled window
Three diagnostic perspectives
See the machine from signal, power and temperature
edgeSV combines complementary indicators instead of relying on a single sensor type, giving maintenance teams more context for abnormal behavior.
Electrical Signal Analysis
- Voltage and current waveform analysis
- FFT, PSD and Park-vector indicators
- Mechanical and electrical abnormality indicators from the feeder
Power Quality Analysis
- Voltage imbalance and harmonic conditions
- Power events and electrical stress indicators
- One measurement point for machine and power context
Temperature Monitoring
- Temperature as a complementary condition indicator
- Trend abnormal heat together with electrical behavior
- Support faster maintenance triage
Electrical signal intelligenceA signal you can measure.
A signal you can measure.
An indicator you can interpret.
edgeSV brings the waveform, diagnostic finding and condition trend into the same context, so your team can understand what deserves attention.
Explore diagnostic indicators
Cooling pump P-201 · live
3P4W · 8 kHz
IL1
IL2
IL3
Bearing defect band
Caution
Outer-race bearing frequency rising
Sideband amplitude +6.8 dB over 14-day baseline
Plan inspection
SIGNAL → INDICATOR → ACTIONAn illustration of the diagnostic workflow
Edge-to-cloud
Analyze here.
Analyze here.
Connect everywhere.
edgeSV reads the electrical signal where equipment is powered and analyzes it on the spot. Diagnosis happens at the panel in real time; the cloud or your server adds alerts, trends and fleet-wide insight across every asset and site.
edgeSV Sense
Non-invasive current and voltage sensing at the feederCTs and voltage inputs sit in the power or control panel, not on the machine. Every motor, pump, inverter and transformer is already wired to a feeder, so that is where edgeSV listens.
That makes submerged pumps, fans at height and equipment in hot or hazardous zones as easy to monitor as anything else, and installation can be scheduled around panel maintenance.
8 + 8voltage and current channels
5 mA–3,000 Arange, depending on CT
690 Vline-to-line input
2leakage current channels
edgeSV Edge (SV500)
Signal processing and connectivity in one industrial deviceSV500 samples three-phase waveforms at 8 kHz with 24-bit resolution and processes them on the device: FFT, power spectral density, Park vector and power quality indices, all computed locally.
Because nothing waits on a network round trip, abnormal behavior is flagged as it happens, and the device keeps diagnosing if the connection drops. It also acts as the site gateway: dual Ethernet with RSTP, an RS-485 master port for nearby meters and Modbus TCP/RTU upward.
8 kHz24-bit waveform sampling
±0.2%voltage accuracy
2 × GbEswitch with RSTP
< 5 Wat DC 24 V
edgeSV AI
Learns each asset's normal behavior and explains every deviationA asset baseline model models how each machine behaves under its own voltage, load and operating states. Machine learning scores how far current behavior departs from that baseline and links the change to a component such as bearings, rotor, stator, windings or DC link.
Every alert carries the indicator behind it, such as a bearing defect frequency or even-order harmonic voltage, so engineers can verify the reasoning. The baseline is refined as operating patterns change over time.
20diagnostic items
3asset families
4status levels
Per assetlearned baseline
edgeSV Cloud
One view across assets and sites, deployed the way your IT policy requiresDiagnostic results and measurement data flow from each SV500 to a central server, a private cloud or managed SaaS. There they are kept as long-term history, compared across similar assets and organized by site, facility and equipment group.
Users, roles and alarm routing are managed centrally, so headquarters, site managers and service partners each see what they need. Start with one site and add more without changing what runs at the edge.
12SV500 units per HMI server
4deployment models
Multi-siteasset hierarchy
Role-basedaccess
edgeSV Platform
A clear, shared picture of equipment healthThe platform brings edge and cloud data together in one interface: status by asset, health scores, voltage and current trends, waveform and spectrum views, power quality events and alarm history.
Trend analysis and historical comparison show which machines need attention and when, so work is prioritized by condition rather than by calendar. The same UI is served by every SV500 and by the central server.
Browserno install
63rdharmonic order
Trendsand history
Reportsand export
How it works
From electrical signal to maintenance decision
1
Acquire
Current and voltage sensors on the feeder capture three-phase waveforms at 8 kHz. No sensor is mounted on the machine itself.
2
Analyze at the edge
FFT, power spectral density, Park vector and power quality analysis run on the device, feeding a digital-twin model.
3
Diagnose
Machine learning scores deviation from normal behavior and maps it to components such as bearings, rotor, stator or windings.
4
Act
Results reach the embedded web UI, your server or cloud, and SCADA over Modbus, so the team sees status, trends and alarms in one place.
Platform
One view.
One view.
A clearer next step.
Each SV500 serves its own web UI with no software to install. Connect several units to a central server and the same picture scales to a plant, then to a portfolio of plants.
- Four-level status: normal, caution, warning, critical
- Health score, trends and waveform view per asset
- Alarm and event history with power quality indices
- Reports for maintenance planning
Plant A · Utility buildingOverview
Assets
Alarms
Power quality
Reports
Settings
42Normal
5Caution
2Warning
1Critical
| Asset | Health | Status | Finding |
|---|---|---|---|
| Chiller No.1 compressor motor Motor | 94 | Normal | — |
| Cooling water pump P-201 Pump | 71 | Caution | Bearing outer-race frequency |
| Cooling tower fan VFD VFD | 58 | Warning | DC link ripple rising |
| Main transformer TR-1 Transformer | 88 | Normal | — |
| RO high-pressure pump Pump | 36 | Critical | Current imbalance 9.4% |
From insight to actionAn alert is the beginning.
An alert is the beginning.
A better decision is the goal.
Make the information useful to the people who keep your operation running.
Prioritize
Consider operational impact alongside the diagnostic finding.
Prepare
Align the inspection window, people and parts before a stop.
Review
Compare the condition history with inspection and maintenance records.
Application scenariosSee what earlier insight
See what earlier insight
can change on your site.
Explore how a diagnostic finding can become a maintenance plan. The following are illustrative scenarios.
01Rotating equipment
An abnormal bearing trend
Check the electrical indicators and trend, then schedule alignment and bearing inspection in a planned window.
Motors · pumps · fansExplore the scenario ↗02Building utilities
Cooling continuity in peak season
Track chiller, pump and fan condition together to prioritize maintenance before the busiest operating period.
Chillers · VFDs · cooling towersExplore the scenario ↗03Material handling
A worn drive before a peak window
Review load and torque trends and place maintenance outside the shipping window.
Conveyors · gearmotors · sortersExplore the scenario ↗
What is Edge AI?
Why intelligence belongs
Why intelligence belongs
close to the equipment.
Edge AI runs AI algorithms and models directly on the edge devices where data is created, such as sensors, gateways and industrial IoT equipment, so data is analyzed and acted on in real time.
Unlike approaches that send data to the cloud before analyzing it, edge AI processes data on site, the moment it is captured. That makes it effective wherever fast judgment and response matter: detecting equipment anomalies, predicting failures, predictive maintenance and industrial automation.
Five advantages of edge AI
Real-time decisions
Edge AI processes data on site, enabling fast analysis and real-time decision-making.
It responds immediately without waiting on the network or cloud, which suits environments that demand quick action, such as equipment anomaly detection and industrial automation.
Data security and privacy
Processing data inside the edge device minimizes how often sensitive data is sent to external networks or a central cloud.
This lowers the risk of data leaks and raises the level of security and privacy protection.
Efficient network bandwidth
Instead of sending all raw data to the cloud, only the necessary information and analysis results are transmitted, sharply reducing network traffic and data volume.
Bandwidth is used efficiently and the load on communication infrastructure goes down.
Lower operating costs
Running analysis locally reduces cloud computing usage and data transfer.
That cuts the cost of running cloud servers and communications, for a more efficient system overall.
Scalable, distributed AI
Built on distributed computing, edge AI can deploy AI models across many sites and many edge devices.
That makes it well suited to extending AI-based monitoring and diagnostics across factories, buildings, data centers, power plants and other sites and equipment.
Applications
Built for the equipment that keeps facilities running
MotorsCore rotating equipment
PumpsCavitation, load and bearing condition
Fans & BlowersHVAC and process air systems
CompressorsCompressed air and refrigeration
ChillersCompressor, pumps, fans and drives
Inverters / VFDDC link, switching and load stress
ConveyorsDrive motors and material handling
CNC MachinesSpindle and drive condition
Boiler & SteamFans, pumps and auxiliaries
Outcomes
Less firefighting.
Less firefighting.
More planned maintenance.
Deeper insight into each machine, more efficient operation and maintenance that is planned rather than reactive.
Less unplanned downtime
Developing faults are found early and made visible, so parts can be ordered and long lead times planned for before anything stops.
Longer equipment life
Acting on abnormal behavior before heavy wear sets in extends the life of motors, pumps, compressors and transformers.
Better maintenance prioritization
Teams see which machines need attention and when, and plan work on measured condition instead of fixed intervals.
Less firefighting
Earlier awareness means fewer surprises on the floor, fewer emergency decisions and steadier daily operations.
Insight when time matters
Analysis runs at the panel, so findings arrive immediately without waiting on cloud processing or manual review.
Safer, more confident operation
Insulation stress, ground faults and overheating are detected before they turn into incidents.
Start with your critical assetYour next maintenance decision
Your next maintenance decision
starts with a better signal.
Tell us which asset matters most. We will review the measurement points, diagnostic goals and the best way to start a pilot with your team.