Can you do predictive maintenance without vibration sensors?

In many cases yes. Cycle time drift, motor current, temperatures, alarm patterns and maintenance history describe most degradation that develops over days or weeks. Vibration remains the better signal for early bearing and rotating faults, so it is worth adding on the specific assets where those failures dominate.

How much history is needed?

A few months covering at least a handful of real failures or interventions. The model learns from what went wrong, so a period with no events teaches it very little, however long it is.

Do older machines without a PLC work?

A machine that produces no data at all cannot be modelled from data. For those assets a minimal retrofit, often a single current or temperature reading, is enough to get started, which is far cheaper than a full sensor package.

Is it as accurate as a sensor-based system?

For failure modes the existing signals can see, the warning horizon is comparable. For high-frequency mechanical faults a dedicated sensor detects earlier. The honest comparison is not accuracy in the abstract, it is coverage of the failures that actually stop your line.

What happens after the warning?

An alert on its own still leaves the hard part to a person. Atherya proposes the maintenance window and the revised production sequence around it, and applies the change only with your sign-off.

Practical guide

Predictive maintenance without new sensors: what your plant already knows

Most predictive maintenance projects stall on the same step: a quote for vibration sensors on every critical asset, a retrofit plan, and a first result a year later. In many plants that step is avoidable, because the machines are already producing the signals a model needs.

Short answer

Yes, predictive maintenance is possible without adding sensors, when the plant already records cycle times, motor currents, temperatures, alarms and work orders through PLC, SCADA, MES or CMMS. Those signals describe how a machine degrades over weeks. New hardware is worth adding later, on the few assets where the existing data genuinely cannot see the failure mode.

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Signals your machines already produce

Cycle time drift

A machine that slowly takes longer to complete the same cycle is often the earliest visible sign of mechanical wear, and it is recorded by every PLC.

Motor current and load

Rising absorption for the same job points to friction, misalignment or a loaded transmission. Drives already expose it, no extra probe required.

Micro-stops and alarm patterns

The sequence and frequency of minor alarms before a breakdown is a fingerprint. Counted one by one they look like noise, read as a pattern they are a warning.

Maintenance history

Work orders, part replacements and repair notes in the CMMS tell the model what actually failed and when, which is what turns a pattern into a prediction.

Sensor-first vs data-first: what changes

Time to first result

Sensor-first approachMonths. Sensor selection, installation windows, wiring and commissioning happen before any data exists.

Data-first approachWeeks. Historical data is already there, so the model can be tested on failures that already happened.

Upfront cost

Sensor-first approachHardware per asset, plus installation and plant downtime to fit it.

Data-first approachIntegration effort only. Cost scales with the number of systems, not the number of machines.

Coverage

Sensor-first approachDeep on the assets that were instrumented, blind on everything else.

Data-first approachBroad across every connected machine from day one, shallower on failure modes the existing signals cannot see.

What it connects to

Sensor-first approachUsually its own portal, separate from planning and scheduling.

Data-first approachThe same data already used for production, so a maintenance warning can change the plan instead of just raising an alert.

This comparison reflects how the two approaches are commonly deployed as of 2026. Every plant has a different data maturity, so treat it as a starting point for your own assessment rather than a verdict.

How to start without buying hardware

Pick one painful asset

Not the whole plant. The machine whose stops cost the most, where everyone already agrees there is a problem.

Collect what exists

A few months of PLC or SCADA history, the alarm log, and the maintenance records for the same period.

Replay the last failures

Run the model backwards over days you already know the outcome of. If it would have warned you, the signals are sufficient.

Add hardware only where blind

The replay shows which failure modes stayed invisible. Those, and only those, justify a sensor.

When new sensors are genuinely needed

Existing data has blind spots. Early bearing wear on a slow-turning shaft, lubrication loss, and some structural faults leave almost no trace in cycle times or motor current. If one of those failure modes is what stops your line, a vibration or ultrasound sensor on that specific asset is the right investment. The point is not to avoid hardware, it is to buy it for a handful of assets after the data has shown where it is missing, rather than for the whole plant up front.

Common questions

Can you do predictive maintenance without vibration sensors?: In many cases yes. Cycle time drift, motor current, temperatures, alarm patterns and maintenance history describe most degradation that develops over days or weeks. Vibration remains the better signal for early bearing and rotating faults, so it is worth adding on the specific assets where those failures dominate.
How much history is needed?: A few months covering at least a handful of real failures or interventions. The model learns from what went wrong, so a period with no events teaches it very little, however long it is.
Do older machines without a PLC work?: A machine that produces no data at all cannot be modelled from data. For those assets a minimal retrofit, often a single current or temperature reading, is enough to get started, which is far cheaper than a full sensor package.
Is it as accurate as a sensor-based system?: For failure modes the existing signals can see, the warning horizon is comparable. For high-frequency mechanical faults a dedicated sensor detects earlier. The honest comparison is not accuracy in the abstract, it is coverage of the failures that actually stop your line.
What happens after the warning?: An alert on its own still leaves the hard part to a person. Atherya proposes the maintenance window and the revised production sequence around it, and applies the change only with your sign-off.

What makes Atherya different

Most predictive maintenance tools score signals. Atherya builds an operational memory of the plant first, then reasons on top of it — with the evidence in plain sight and the decision left to a person.

Operational memory

Every event, anomaly, intervention and shift becomes shared memory. What was a log yesterday is experience tomorrow, and the model reads new signals against it.

Déjà Vu: it has seen this before

Not just "anomaly detected", but when it already happened, how similar it was, how it evolved and which action worked. The senior maintainer's memory, available to everyone.

Context that kills false alarms

Machine defects, weak points, how the crew actually works and the environment around the line. A deviation that is normal for that machine, that shift or that season stays quiet.

Living FMEA

FMEA, manuals and procedures become an active part of the reasoning: causes, effects, sensors and suggested actions are connected, instead of sitting in a document nobody opens.

Explainable, not magic

Every alert arrives with the signals involved, the comparable history, the failure mode and a confidence level. Data, interpretation and decision stay separate and verifiable.

It acts, with your sign-off

Atherya does not stop at the warning: it proposes the maintenance window against real orders and shifts, and replans when something changes. Nothing is applied until a person approves it.

On the data you already have

It works on PLC, SCADA, MES, ERP, maintenance records and feedback as they are — fragmented and legacy included. Sensors are added only where no existing signal carries the degradation.

One brain, not a maintenance silo

Maintenance, production and planning read the same operational state, so a predicted failure immediately becomes a scheduling question instead of a separate dashboard.

Test it on one critical asset

Pick the machine that stops you most often and bring a few months of history. We show what the model would have seen before the last failures, before you commit to anything.

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