Maintenance And Reliability

Maintenance and reliability

A plant that maintains everything equally maintains the wrong things. Maintenance effort is finite, and spending it evenly across assets means the machine whose failure stops the line gets the same attention as the one with three spares in the cabinet.

Rank by consequence, not by age or cost

The question is not how likely a machine is to fail. It is what happens when it does.

Rank every asset on what its failure costs: production stopped, product scrapped, a safety event, a regulatory exposure, or nothing anyone notices before the next shift. That ranking, not the capital value, decides where preventive effort goes.

Two things this exposes immediately in most plants:

• Assets with no redundancy and no spare — the ones that will stop the plant and keep it stopped while a part ships. These are the whole list worth arguing about.
• Assets on a PM schedule for no reason — inherited from a manual, consuming hours, preventing nothing that would have mattered.

Not everything wears out

The intuition behind interval-based maintenance is that failure probability rises with age, so replacing on a schedule beats waiting. That holds for things that genuinely wear — belts, bearings, seals, tooling, anything in contact.

It does not hold for most electronics, instrumentation and control systems, where failure rate is roughly flat with age and intervention is itself a source of failure. Scheduled replacement of a component that does not wear out converts a stable asset into one that gets disturbed on a cycle, and introduces the installation errors that follow every disturbance.

So the interval has to be chosen by failure mode:

1. Wears predictably → interval-based replacement, set from observed life rather than the manual's default.
2. Degrades observably → condition-based. Vibration, thermal, oil analysis, current draw. Replace on the signal rather than the calendar.
3. Fails randomly, consequence low → run to failure, deliberately, with the decision recorded so it does not read as neglect.
4. Fails randomly, consequence high → redundancy or detection, because no interval helps.

Option 3 is a legitimate strategy and it is the one nobody writes down, which is why it gets mistaken for the program failing.

The backlog is the leading indicator

Downtime is a lagging measure. By the time it moves, the condition that produced it has been present for months.

The honest leading indicator is the state of the planned-work backlog: how much identified work is waiting, how old the oldest item is, and what fraction of the week's hours went to work that was planned before the week started. A plant doing 80% planned work is in a different regime from one doing 30%, and the second cannot schedule production reliably no matter how good its scheduler is.

The reactive trap is self-sustaining. Unplanned failures consume the hours that would have prevented the next ones, so the ratio degrades on its own. Breaking it costs a deliberate, temporary over-allocation to planned work while the backlog is worked down, and that is a decision someone has to fund rather than a habit the team can adopt.