Predictive maintenance should not start with a platform or sensors. It should start with a failure mode that is costly or recurrent. Its purpose is to turn a measurable deviation into a verified decision, planned work and a closed maintenance work order. If a signal only ends up on a chart or in an alert without an owner, machine maintenance does not become more reliable.
Consider a drive motor whose bearing shows a vibration change or a temperature increase before failure. The failure affects production, and the signal may provide time to react. But temperature or vibration alone is not a diagnosis. The team needs to know what it monitors, when it responds, how it verifies the deviation and how it aligns the decision with the production schedule.
Not every failure is a suitable starting point for predictive maintenance. A suitable candidate has three characteristics:
That trace may be a change in vibration, temperature, energy consumption, pressure, flow or cycle time. What matters is that the data can be linked to specific equipment and its operation. If the trace is not stable or cannot be interpreted reliably, a preventive schedule, regular inspection or better basic records may be more appropriate than a prediction model.
Start by describing one case on one equipment type. Record:
This description prevents a common mistake: collecting data before defining the decision that data should support.
Machine condition monitoring is useful only with operating context. The same motor temperature does not mean the same thing at idle, under full load or immediately after start-up. The same applies to vibration, pressure and energy consumption.
Link the signal to available operational data, such as:
The context does not need to be complex at first. Its initial purpose is to help the person receiving an alert distinguish an expected change from a deviation that needs checking. Without it, a system may generate many alerts that the team gradually starts to ignore. An important signal can then be lost among false alarms.
A threshold is not only a number above which a notification is sent. It is an operating rule: under what conditions a signal requires a response, who is responsible and what must happen before a work order is opened.
It is useful to distinguish at least two levels. The first requires verification but does not assume a failure. The second requires a faster intervention decision or escalation because the deviation persists, worsens or appears alongside other indicators.
Define the following for every threshold in advance:
The initial check may be a visual inspection, a repeated measurement, comparison with another parameter or a review of recent changes to the equipment. For example, increased vibration may require confirmation by measurement at the same point, a fastening check and comparison with bearing temperature. This process reduces the risk of changing the work plan based on an unverified signal.
Open a maintenance work order when verification confirms a deviation, or when the escalation rule requires work without further waiting. The work order turns a technical signal into work that can be planned, completed and closed.
To be executable, the order should include:
Connecting technical events to work execution matters for machine maintenance and production alike. Manufacturing work orders provides context for why a work order should carry clear information about the task to be performed, rather than only a general alarm note.
The technically preferred time for an intervention is not always operationally available. At the same time, deferral can increase the likelihood of deterioration or the consequence of failure. The decision should therefore not remain only with the technical team or only with the production plan.
A joint review by production and maintenance should consider:
If work is deferred, record the decision, the person who accepted the risk and the date or condition for the next review. This record does not remove risk, but it makes risk visible and supports consistent action in the next similar case.
Closing the work order is not the end of the process. It creates feedback that shows whether the signal was useful and whether the threshold or verification procedure needs to change.
After the intervention, record at least:
If verification shows that a signal was not linked to the expected failure, that is not necessarily a failure of the process. It may show that the threshold is too low, operating context is missing, measurement practice is inconsistent or another indicator should be monitored. A model, whether based on rules or an analytical approach, learns only when work outcomes return to the record.
It is more useful to monitor the operating flow than the number of sensor data points collected. Track whether alerts were reviewed within the defined deadline, how many checks confirmed justified work and whether planned work replaced some unplanned interruptions. These measures show where the process is slowing down: in the signal, verification, planning or execution.
Predictive maintenance does not remove the need for basic maintenance discipline. It cannot replace unclear equipment records, unavailable parts, undefined responsibilities or work orders without feedback. Nor is every signal change a reason to stop a machine.
It is therefore reasonable to begin with a limited scope: one equipment type, one failure mode, a known signal and an agreed path from alert to decision. Expanding sensors and more complex models makes sense when the team can consistently explain why an order was opened, what was found and how the rule will be checked next time.
As a next step, select one frequent or costly failure, map the path from signal to closed order and check whether every point has an owner, a deadline and an outcome record. ORKA manufacturing provides context for connecting work orders and execution with operational data.