Greater Predictive Maintenance vs Lower Monitoring Burden

Business Context

Predictive maintenance can identify deterioration before machinery fails, reducing unplanned downtime and improving maintenance scheduling. Expanding condition monitoring across every machine and component, however, can generate substantial sensor costs, data volumes, analytical requirements, and diagnostic workload. Monitoring everything continuously may cost more than the failures it is intended to prevent.

Agriculture TRIZ Resolution

Predictive monitoring should be concentrated on failure modes with significant operational consequences or useful detectable precursors. Critical components can receive continuous monitoring, while lower-risk assets use periodic inspection, operating-hour thresholds, or simpler condition indicators. Monitoring intensity can increase when early evidence suggests deterioration.

Applicable TRIZ Principles

Principle 1 – Segmentation assigns monitoring intensity according to equipment criticality.

Principle 23 – Feedback increases maintenance attention when condition indicators deteriorate.

Principle 2 – Taking Out eliminates monitoring that does not materially improve maintenance decisions.

Expected Outcome

Earlier detection of critical failures

Lower monitoring workload

Better maintenance resource allocation

Reduced unplanned downtime

Decision Indicators

Early indicators include:

Large quantities of condition data receive little review.

Low-criticality components receive the same monitoring as essential assets.

Diagnostic workload increases faster than maintenance effectiveness.

Significant failures still occur despite extensive data collection.

Monitoring systems generate excessive low-value alerts.

These indicators suggest that predictive maintenance should be risk-based rather than universal.