Use condition and failure data to plan maintenance before breakdown.
Predictive maintenance uses condition trends, operating data, failure history, and inspection evidence to estimate when maintenance should be performed instead of relying only on fixed time intervals or waiting for a breakdown.
Use equipment condition, risk, and production priority to guide maintenance work.
Good maintenance strategy connects machine condition, failure history, planned work, critical spares, and production schedules instead of treating every asset the same.
Prevent avoidable downtime without over-maintaining equipment.
Maintenance effort should be proportional to asset criticality, failure behavior, detection capability, repair risk, spare-parts exposure, and the production consequences of failure.
CRIT-AUse Evidence, Not Guesswork
Predictive work should be based on measurable change, known failure behavior, or inspection evidence rather than vague age-based concern.
Related Guide →CRIT-BFocus on Critical Assets
Prediction effort is most valuable where failure has a significant effect on production, quality, safety, or repair time.
Related Guide →CRIT-CPlan Parts and Labor Early
An approaching failure window can be used to stage parts, tools, and people before the machine is stopped.
Related Guide →CRIT-DVerify the Prediction
After maintenance, compare the observed condition with the prediction to improve future thresholds and intervals.
Related Guide →Make condition and maintenance priority visible.
These diagrams show condition trend and the way maintenance time is divided between inspection, planned work, repair, and verification.
Condition trend.
Monitor change over time so maintenance can respond before the condition becomes a production failure.
Maintenance allocation.
Separate inspection, planned service, repair, and restart work to understand where maintenance time is being consumed.
Relevant maintenance and equipment resources.
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