Most maintenance engineers know, intellectually, that fixed schedules waste money. What surprises them is how much. One widely cited industrial study found that roughly 30% of all preventive maintenance tasks add no value whatsoever — the component was replaced in perfectly serviceable condition. You are paying a technician to pull a healthy bearing out of a machine, throw it away, and install a new one. That is not safety; that is ritual.
This post is about making the organizational and technical leap from that ritual to Condition-Based Maintenance (CBM): specifically the migration process, not the theory. Here I want to focus on what actually changes in your data pipeline, alerting logic, and day-to-day operational workflow when you make the switch.
Why Time-Based Schedules Fail Mathematically
The core problem with fixed-interval maintenance is that it assumes component degradation is uniform and predictable. It almost never is.
Consider a simplified model for bearing failure probability. If we denote the hazard rate as $h(t)$, then for a Weibull distribution:
$$h(t) = \frac{\beta}{\eta} \left(\frac{t}{\eta}\right)^{\beta - 1}$$
where $\beta$ is the shape parameter and $\eta$ is the scale parameter. For early-life failures, $\beta < 1$. For wear-out failures, $\beta > 1$. The point is that $h(t)$ is almost never flat — which is exactly what a fixed-interval schedule assumes.
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