Predictive Data Maintenance (PDM)
Predictive maintenance based on real dimensional trends, OEE behavior and QIF (ISO 23952) structured data
IVR PDM analyzes performance indicators and dimensional results automatically collected from IMTE — Inspection Measuring and Test Equipment to predict equipment degradation before failure occurs. Using QIF-structured characteristics and SPC trends, predictive models are grounded in measurable dimensional drift, process capability variation and real OEE losses.
Prediction supported by measurable data
- Dimensional drift detection from IMTE measurement history
- OEE trend correlation with equipment degradation
- QIF-structured characteristic monitoring over time
- SPC-based early instability identification
- Integrated CAD + QIF + DMIS digital traceability
Dimensional trend analysis as predictive input
IVR PDM evaluates historical dimensional results collected directly from IMTE — Inspection Measuring and Test Equipment, identifying gradual drift, repeatability loss or systematic deviation patterns.
These trends are structured according to QIF (ISO 23952), ensuring that each monitored characteristic remains linked to its nominal, tolerance and measurement context.
Prediction is based on measurable dimensional behavior, not estimation.
Correlation between OEE losses and degradation
Availability and performance losses captured by OEE are correlated with dimensional instability and capability reduction.
By linking statistical deviation with equipment efficiency metrics, IVR PDM identifies patterns that precede breakdowns or quality deterioration.
- Availability drop associated with dimensional instability
- Performance variation linked to process drift
- Quality factor reduction based on real measurement data
SPC-driven early warning mechanisms
Continuous SPC monitoring provides early statistical signals of instability, including trends, shifts and increasing variability.
These indicators are incorporated into predictive maintenance logic, allowing intervention before nonconformity or failure occurs.
Statistical evidence becomes a trigger for preventive action.
Integration with TPM and CMMS
IVR PDM integrates with TPM and CMMS modules, converting predictive alerts into structured maintenance work orders.
Each intervention is traceable to the dimensional characteristics and OEE trends that triggered the prediction, closing the loop between analysis and action.
- Automatic work order generation from predictive alerts
- Traceable link between prediction and maintenance execution
- Post-intervention validation through dimensional stability
Continuous digital thread in predictive maintenance
Engineering definitions flow from CAD into QIF structures and DMIS programs. Dimensional measurements from IMTE feed MES, OEE and PDM analytics without manual transcription.
The digital chain — CAD → QIF → DMIS → IMTE → MES → OEE → PDM → CMMS — ensures full traceability from design definition to predictive maintenance action.
Predictive maintenance becomes a structured, data-driven engineering process.