Test MCIFT against real operational data

Define one decision

Select one machine, process or digital system and state the operational decision before looking at results. A narrow question produces evidence that can be acted on.

Possible pilot inputs

• Vibration histories, temperature, pressure, flow, RPM or load
• Maintenance logs and confirmed machine events
• API traces and database metrics
• ETL-stage and queue logs
• Service-dependency and incident data

Proposed study sequence

• Select one bounded system.
• Define the operational decision.
• Freeze the feature mapping before evaluation.
• Establish conventional baselines.
• Replay historical data.
• Measure warning time, false alarms, misses and uncertainty.
• Report improvements, failures and neutral results.
• Decide whether further development is justified.

Conventional comparison

The baseline is chosen for the domain: thresholds and trends, spectral monitoring, established condition indicators, SLO rules, seasonal baselines or current observability alerts. MCIFT is tested beside them, not assumed to replace them.

What the study can answer

• Whether a candidate feature changes warning lead time
• Whether additional warnings are operationally useful
• Where false alarms concentrate
• Which regimes or incident types fail
• Whether further development is justified

Study limitations

Historical labels can be incomplete, data can leak future information and one asset may not generalise. The report must identify these limitations and avoid extrapolating beyond the evaluated scope.

Evidence status

No positive result is assumed. Negative and neutral results are retained. Any later production decision requires engineering review, monitoring design and continued validation.

Data readiness

• Time alignment and stable identifiers
• Documented sensor and telemetry changes
• Enough normal operation to estimate false alarms
• Confirmed events or explicit unsupervised objectives
• Permission to use the data for the agreed study

Evaluation report

• Warning lead time distribution
• False-alarm rate and operator burden
• Missed events
• Precision and recall where labels support them
• Uncertainty and sensitivity by regime
• Comparison with every frozen baseline