Link virtual representations with live telemetry to anticipate failures before they happen. Check your grasp of data, modeling, and operations that make predictive maintenance real.
Which data foundation is essential to enable predictive maintenance with digital twins?
static 3D geometry without live data
marketing survey responses by customer segment
annual financial statements at the plant level
time‑series sensor telemetry linked to asset IDs and operating context
For maintenance planning impact, which KPI most directly evidences value from twin‑driven prediction?
lower storage costs for sensor data
more models deployed to production
higher total number of alerts generated
increase in mean time between failures (MTBF) and fewer unplanned stoppages
Where should fast anomaly‑detection inference run for assets with tight latency constraints?
inside the ERP system’s reporting module
only in a central cloud region regardless of network
at the edge near the equipment, with periodic sync to cloud twins
in a nightly batch after logs are uploaded
What is a common pitfall that degrades predictive accuracy in early twin programs?
using any physics at all in models
over‑documenting interfaces
too much attention to sensor calibration
poor data quality and labeling of failure events
If you lack enough historical failures for supervised RUL models, what is a pragmatic starting approach?
anomaly detection and physics‑informed heuristics before full supervised RUL
wait several years to collect more failures
estimate RUL from monthly averages of sales orders
simulate failures only and deploy without validation
Which practice keeps a digital twin useful as operating conditions change?
delete older data to keep the dataset small
closed‑loop updates: continuous ingestion, monitoring, and scheduled retraining
manually override predictions during holidays only
freeze the model after the best initial AUC
Why integrate the twin with a maintenance system (EAM/CMMS)?
to replace technicians with chatbots
to lock schedules to a fixed calendar
to export 3D models for marketing brochures
to trigger work orders automatically when risk surpasses thresholds
What determines the fidelity required for a twin in predictive maintenance?
the maximum possible polygon count of the 3D model
the vendor’s default setting
the level of physics detail and granularity needed to predict failures cost‑effectively
always the highest‑fidelity CFD simulation
Which signal most clearly indicates model drift in production?
an increase in user logins to the portal
higher dashboard refresh rates
a steady number of sensors connected
systematic rise in residual errors or declining precision/recall over time
For safety‑critical assets, how should you set decision thresholds for predicted failures?
optimise on risk‑adjusted cost, weighing false negatives far more heavily
treat false positives and negatives as equal
set thresholds so alerts are rare regardless of risk
let operators change thresholds randomly each shift
Starter
Great start—review the core concepts and patterns for this topic.
Solid
Strong performance—tighten definitions and apply them to edge cases.
Expert!
Outstanding—translate these insights into system design and decisions.
