Your equipment is already telling you what’s going to fail. 42Hz puts that knowledge in the hands of the people who can act, not on a screen in the control room.
Industrialsystemsgeneratemillionsofalarmsayear.Mostarenoise.Thesignalneverreachesthepeoplewhocanact.
What we do
We connect to the data your equipment already produces: DNP3, Modbus, IEC 61850, OPC UA. Then we do the work your team doesn’t have time for. We correlate thousands of signals, classify patterns against a library of failure signatures, predict what’s failing, explain it in plain language, and deliver the decision to the right person on the right device.
Works with
No new sensor hardware. No rip-and-replace.
Why it matters
A single partial-discharge event. One plant caught it early. One did not. The difference is not luck. It’s whether the data was being read.
Total cost
$184,000
Avg. unplanned MV trip. Illustrative, DNV GL 2024.
Downtime cost
$0
18-day lead time. Planned, not panicked.
Console
Web for supervisors. Mobile for techs in the field. Voice for hands-free. APIs and webhooks into the CMMS, ERP, and parts systems O&M already runs on.
Representative product view
Site overview
CB-E04 · Contact erosion on VCB phase L2
P1 · 99.1% conf · AI predicts failure in 18d · spare pre-ordered
Assets online
1,839/1,842
▲ 99.84%
Open alarms
3
▲ 1 since 08:00
AI predictions
12
14d avg lead
Avoided downtime
$1.24M
this quarter
CB-E04 · Partial discharge
pC · phase L2
Critical assets
Outcomes · Design targets
Targets reflect the performance envelope IoT Edge Analytics is engineered for. Actuals depend on asset class, data quality, and deployment mode. We will publish real customer numbers as pilots complete.
•Target lead time
From first anomaly signal to predicted failure, depending on fault type and data quality.
•False positives
Multi-signal fusion keeps false positives low. We call it when it’s real.
•Target MTTR reduction
Technicians arrive with the right parts, the right runbook, the right window.
•Deployment
Edge or cloud, on your existing protocol stack. No new sensor hardware required.
Calculator
Drag the sliders. We model projected savings against your fleet size, current MTTR, and per-trip cost.
Illustrative model. Assumes 85% trip reduction and 41% MTTR improvement based on published industrial IoT benchmarks.
Projected annual savings