The Watsynq Platform
Dashboard, alerts, RUL trends, and CMMS push — all from one equipment health view. Designed for the reliability engineer, not the data scientist.
Your entire equipment fleet. One view.
Every monitored point sorted by RUL. No searching. The equipment that needs attention is always at the top.
| Equipment | Location | RUL (days) | Confidence | Last reading | Status |
|---|---|---|---|---|---|
| Kiln Fan #3 — Inboard Bearing | Line 2 / Bay 4 | 38 | 95% CI: 29–49 | 07:14 today | Watch |
| Conveyor Head Drive #1 | Belt Line A | 72 | 95% CI: 61–84 | 07:14 today | Watch |
| Raw Mill Fan — Outboard | Mill 1 / North | 104 | 95% CI: 92–118 | 07:14 today | Healthy |
| Pump P-204 — Drive-end | Process Area 2 | 118 | 95% CI: 104–132 | 07:13 today | Healthy |
| Crusher Drive — Pinion Gear | Crusher Bay 3 | 141 | 95% CI: 128–155 | 07:13 today | Healthy |
Every alert tells the full story.
One click from the fleet view. Failure mode prediction, signal breakdown, confidence interval, and recommended action — in a single card.
- Failure mode prediction with defect stage
- Signal contribution breakdown (AE / Thermal / Oil)
- One-click CMMS work order push
- Sensor quality indicator — know when a sensor degrades
90-day RUL trend. With the confidence band visible.
The trend line is the story. Watch the confidence band tighten as the model accumulates data. See exactly when the RUL crossed your planned maintenance window.
Kiln Fan #3 — Inboard Bearing · 90-day RUL trend
Built for how reliability engineers actually work.
- Role-based access control — technicians see alerts; managers see fleet summary
- 24/7 automated polling — no daily log-in required
- Export to PDF report — for shift handover or maintenance review meetings
- API access for all alert data — pull into any reporting tool
- Configurable thresholds per equipment class and criticality tier
- Audit log for compliance and maintenance records
Most predictive maintenance platforms are designed for data scientists. Watsynq is designed for the person who needs to decide whether to pull a bearing this outage or the next one.
That means plain-language alert summaries, confidence intervals expressed as day ranges (not probability percentages), and recommended actions written for a work-order system, not a Jupyter notebook.
The sensor quality indicator exists because reliability engineers need to know when to trust the score — and when a sensor has degraded and the model is working with partial data.
See a live demo for your equipment fleet.
We will walk through a real RUL output from equipment in your industry and show how it maps into your CMMS workflow.