Get Started with Predictive Maintenance
Use this guide when a site is preparing its first Predictive Maintenance workflow in FactVerse. The goal is to make equipment health, anomalies, maintenance recommendations, and work-order feedback usable by operations teams without hiding the data and review assumptions behind the model.
Predictive Maintenance works best as a governed operations loop: equipment and signals are connected first, health is reviewed in context, anomalies are triaged by a responsible owner, accepted recommendations become maintenance work, and field outcomes feed the next tuning cycle.
Setup Flow
Prerequisites
Before the first rollout, confirm:
| Requirement | Why it matters |
|---|---|
| Equipment owner | Someone must approve the equipment scope and review health or anomaly output. |
| Data owner | Source-system access, signal meaning, and data quality issues need a named owner. |
| Maintenance owner | Accepted advisories need a team that can create, assign, and close work. |
| Initial equipment scope | A small group of similar assets makes baseline review and operator feedback easier. |
| Work-order feedback path | Outcomes need to return to the workflow after field action. |
Step 1: Select the Equipment Scope
Start with equipment where maintenance ownership, signal data, and failure history are clear enough for review.
| Decision | Guidance |
|---|---|
| Equipment class | Group similar assets such as chillers, pumps, motors, compressors, AHUs, UPS systems, fans, filters, or production utilities equipment. |
| Operating boundary | Define the site, building, production line, room, equipment group, or service system covered by the first rollout. |
| Maintenance owner | Assign a team that will review anomalies, accept or reject advisories, and close work orders with outcomes. |
| Decision horizon | Decide whether the first use case is daily triage, weekly maintenance planning, reliability review, or a pilot dataset validation. |
Avoid starting with every asset in a site. A smaller set with stable identity and usable history creates better early evidence.
Step 2: Prepare Identity and Signals
Predictive Maintenance needs a stable link between each equipment record and its source signals.
| Input | Required preparation |
|---|---|
| Equipment registry | Equipment ID, name, class, location, manufacturer, model, owner, and optional bridge to the main equipment directory. |
| Sensor or telemetry history | Timestamp, equipment reference, signal name, unit, value, and quality status. |
| Work-order history | Equipment reference, failure type when available, open/close timestamps, priority, root cause, action, and outcome. |
| Inspection and alert evidence | Inspection findings, alarms, comments, photos, and event timestamps that can explain the operating context. |
For connector setup, mappings, and source data quality, use Prepare Signal History for Predictive Maintenance.
Step 3: Enable the Operating Surfaces
The current application exposes these primary views:
| View | Route | Use |
|---|---|---|
| Dashboard | /pdm/dashboard | Review health distribution, active anomalies, maintenance status, and high-level operating signals. |
| Equipment list | /pdm/equipment | Find monitored assets and open profile detail. |
| Equipment detail | /pdm/equipment/:id | Review profile, health snapshot, health history, and equipment context. |
| Fleet | /pdm/fleet | Compare equipment groups and component attention signals. |
| Anomalies | /pdm/anomalies | Triage unresolved anomalies and generate maintenance action where appropriate. |
| Maintenance | /pdm/maintenance | Review maintenance records and schedules connected to monitored equipment. |
| Criticality | /pdm/criticality | Prioritize work using risk and criticality signals. |
| Energy | /pdm/energy | Review energy baselines where energy data is connected. |
| Health | /pdm/health | Review health-centered operating views where enabled. |
Use pdm in route and API references. In customer-facing prose, call the capability Predictive Maintenance.
Step 4: Validate the First Equipment
Before adding more assets, validate one representative equipment record end to end:
- Open the equipment profile and confirm identity fields.
- Confirm recent signal history is mapped to the right equipment.
- Review the latest health snapshot and health history.
- Check whether the dashboard includes the equipment in summary counts.
- Open the anomaly list and confirm unresolved events appear with useful context.
- Create or link a maintenance action only after the advisory has been reviewed.
- Close the linked work order with an outcome label.
This validation prevents a common failure mode: building a model workflow before the team trusts the underlying asset and signal identity.
Step 5: Define Review Cadence
Predictive Maintenance should become part of an operating rhythm.
| Cadence | Review focus |
|---|---|
| Daily shift review | New high-severity anomalies, blocked advisories, and urgent equipment status changes. |
| Weekly maintenance planning | Accepted advisories, work-order backlog, criticality ranking, maintenance windows, and parts readiness. |
| Monthly reliability review | Health trend, repeated failure modes, advisory precision, false positives, and rule or template changes. |
| Pilot acceptance | Data completeness, reviewer confidence, work-order outcome capture, and expected next rollout scope. |
Expected Output
At the end of the first setup pass, the team should have:
- a defined equipment scope;
- mapped signal history for the selected assets;
- at least one reviewed equipment profile;
- dashboard and anomaly views that reflect real source data;
- a named owner for advisory decisions;
- a work-order feedback path for accepted recommendations;
- a short list of data or readiness gaps before broader rollout.
Validation Checklist
- Equipment identity is stable across Predictive Maintenance, DFS, CMMS, and work-order records.
- Source signals have expected units, timestamps, and equipment mappings.
- The first equipment profile has health history or a clear no-data state.
- Anomaly review has a named owner and a response path.
- Accepted advisories can create or link to work orders.
- Work-order closure captures outcome evidence.
- Dashboard and Agent summaries match the underlying records.
Troubleshooting
| Symptom | Check |
|---|---|
| Equipment is missing from the dashboard | Tenant scope, equipment profile, source mapping, and route permissions. |
| Health view shows no data | Recent readings, timestamp format, unit mapping, and equipment ID bridge. |
| Anomaly list is empty | Signal coverage, threshold or baseline readiness, and selected time window. |
| Advisory cannot become work | Work-order permission, CMMS configuration, and maintenance ownership. |