Predictive maintenance is often identified with artificial intelligence and complex models. However, for most organizations, the first valuable step is simpler: have a reliable asset register, interpretable status data and a consistent workflow.

The three maintenance logics

Reactive and plannedRepair after failureIntervention based on time or operating hoursSimple, but can be too early or too late
Condition-dependent and predictiveIntervention based on measurable statusFollow-up of deterioration and riskBetter timing, but requires good data and process

A predictive approach is not justified for all tools. For a cheap, easily replaceable, non-critical component, the reactive strategy can also be economical. The decision must be made based on the consequence of the failure, its frequency, its detectability and the cost of the intervention.

An alarm is not yet a diagnosis

A limit exceeded indicates that something is different from what is expected, but does not necessarily tell you why. The same high temperature can result from load changes, environmental effects, sensor failure, or genuine technical deterioration.

Therefore, the value should be considered in conjunction with operating status, load, environment, and past behavior. The goal is not as many alarms as possible, but the few, justified and prioritized events.

The complete chain: from status to worksheet

Status measurement→Deviation→Risk→Worksheet→Feedback

The event must be related to asset, its critical role and maintenance history. The worksheet records the test, the error found, the material used and the result. This feedback makes the rule or model more accurate later.

From a simple rule to a forecast

It makes sense to proceed gradually. The first level can be a well-chosen limit value and durability condition. The next step is to evaluate the trend, the rate of change or several signals together. A statistical or machine learning model is justified when sufficient, high-quality historical data and properly documented error examples are available.

A complicated model does not make up for incomplete device identification, a bad measurement point, or inconsistent error coding.

Pilot and Success Criterion

A good start is a critical but well-observable group of tools. It is necessary to record in advance what we consider to be the result: fewer unexpected shutdowns, longer forecasting time, fewer unnecessary checks or better component designability. The number of false alarms should be measured in the same way as detected errors.

Decision Checklist

  • Is asset failure a significant operational or financial risk?
  • Is there a measurable signal that prevents the error in time?
  • Can state data be interpreted with load and environmental context?
  • Will the incident become a regulated maintenance task?
  • Do we record the test result and true failure mode?
  • Do we also measure false and missed alarms?

The value of the prediction can be measured in the execution

OrigSmart does not treat the device status as a separate dashboard: the event can be linked to the device's master data, location, maintenance task and closing result.

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