Identical Model Does Not Mean Identical Behaviour

Maintenance teams often manage several units of the same equipment.

A production facility may have:

• Multiple identical pumps
• Parallel air compressors
• Motors from the same manufacturer
• Similar cooling tower fans
• Repeated conveyor drive assemblies
• Several identical gearboxes across production lines

Because the machines have the same specifications, it may seem reasonable to apply the same alarm settings and AI model to all of them.

However, equipment rarely operates under perfectly identical real-world conditions.

NASA’s C-MAPSS prognostics dataset represents fleets of engines of the same type, but each engine begins with different degrees of initial wear and manufacturing variation. These differences are treated as part of normal operation rather than as faults. All equipment behaves in a similar way. Two machines can share the same design while developing different healthy operating signatures.

For an AI monitoring system, those differences matter.

What Is a Machine Baseline?

A machine baseline is a reference model of how an asset behaves during healthy operation.

Traditional condition monitoring may establish a baseline using measurements such as:

• Overall vibration velocity
• Acceleration
• Bearing temperature
• Motor current
• Lubricant condition
• Pressure
• Flow
• Rotational speed

An AI-based baseline can analyse additional relationships and patterns across time, including:

• How vibration changes at different loads
• How temperature behaves throughout a production cycle
• The relationship between speed and vibration
• Changes in sound at different operating modes
• Differences between startup, steady operation and shutdown
• Combinations of sensor readings that normally occur together

After learning the machine’s normal patterns, the system can look for deviations that may indicate changing equipment condition.

Siemens describes AI condition monitoring that can consume machinery information such as power consumption, rotating speed and operating mode in addition to other time-series data for anomaly detection. lt does not automatically prove that a component is faulty. It indicates that the machine is behaving differently from the expected pattern and may need further investigation.

Seven Reasons Identical Machines May Need Different Baselines

1. Installation Conditions

A machine’s measured behaviour depends partly on how and where it is installed.

Differences may include:

• Sensor mounting position
• Sensor orientation
• Surface preparation
• Pipe connections
• Cable routing
• Nearby structural vibration
• Distance from other operating equipment
• Mechanical forces transferred from connected systems

For example, an accelerometer mounted on a clean, flat bearing housing may produce a different signal from one mounted on a painted or uneven surface.

Similarly, vibration from nearby machines may travel through a shared structure and influence the data.

The machines may be identical, but their signal paths are different.

Maintenance Implication

When comparing similar assets, maintenance teams should verify that sensor installation and data-collection methods are consistent.

A difference in readings may come from the machine, the process, the sensor installation or a combination of all three.

2. Foundation, Mounting and Alignment

Foundation stiffness can influence the vibration measured on a machine.

One pump may be mounted on a solid concrete base. Another identical pump may sit on a more flexible steel structure.

Other differences may include:

• Loose mounting bolts
• Soft foot
• Grout condition
• Baseplate distortion
• Shaft alignment
• Coupling condition
• Pipe strain

Even relatively small installation differences can change the way mechanical energy travels through the asset.

A machine-specific baseline captures the behaviour of the complete installed system — not merely the equipment model listed on the nameplate.

Maintenance Implication

When a machine’s baseline changes after alignment, foundation repair or coupling replacement, the new pattern should be reviewed and, where appropriate, established as a new healthy reference.

3. Operating Load and Speed

Machine signals often change with operating conditions.

A fan operating at one steady speed may have a predictable vibration profile. The same fan operating under variable-speed control may show a wider range of healthy vibration patterns.

SKF’s vibration-monitoring guidance notes that equipment may operate under different conditions involving load, speed, processed materials and environment. Operating conditions that can affect machine data include:

• Rotational speed
• Flow rate
• Pressure
• Torque
• Production volume
• Valve position
• Number of active production lines
• Startup and shutdown frequency

Changes in speed can also shift the frequencies visible in vibration data. A system that ignores speed may confuse a normal operating transition with a developing mechanical issue.

Maintenance Implication

AI models should compare the machine against relevant operating states.

A reading collected at full load should not always be compared directly with data collected while the machine is idling.

4. Maintenance and Repair History

Two identical machines may have very different maintenance histories.

Machine A may have recently received:

• Bearing replacement
• Precision alignment
• Lubrication correction
• Shaft repair
• Coupling replacement
• Impeller balancing

Machine B may still contain older components that remain serviceable.

Both machines may be healthy, but their normal vibration, sound and temperature signatures may differ.

Maintenance events can also produce step changes in sensor trends. A sudden reduction in vibration after alignment may be a positive change rather than suspicious data.

Maintenance Implication

Maintenance records should be connected with condition-monitoring data whenever possible.

When technicians replace or adjust a major component, the AI system should be informed so the post-maintenance behaviour can be validated and, when necessary, used to update the baseline.

5. Environmental Temperature and Surrounding Conditions

The environment around an asset can influence both the machine and the sensor readings.

Relevant factors include:

• Ambient temperature
• Humidity
• Dust
• Moisture
• Ventilation
• Nearby heat-generating processes
• Outdoor weather conditions
• Exposure to washdowns or chemicals

An identical motor operating near a furnace may run hotter than one in a ventilated utility room.

Temperature can also affect lubricant viscosity, material expansion and machine clearances.

This does not mean temperature increases should be ignored. It means the system needs context to distinguish environmental effects from abnormal equipment behaviour.

Maintenance Implication

Maintenance teams should consider ambient and process temperature when reviewing thermal trends.

A temperature alarm is more useful when the system can determine whether the rise came from the surrounding environment, increased machine load or internal deterioration.

6. Production Process and Material

Identical machines may support different parts of the production process.

For example:

• Two pumps may handle fluids with different viscosities.
• Two conveyors may carry different product weights.
• Two mixers may process different formulations.
• Two fans may encounter different airflow resistance.
• Two crushers may receive materials of different hardness.

These process differences affect the forces placed on the equipment.

A process change can therefore alter vibration, temperature, sound, pressure or power consumption even when the machine remains healthy.

Maintenance Implication

Condition-monitoring data should be reviewed alongside relevant process data.

Without production context, a model may incorrectly identify normal process variation as a mechanical anomaly.

7. Age, Wear and Operating History

Two machines installed on the same day will not necessarily age at the same rate.

One may experience:

• More running hours
• More starts and stops
• Temporary overloads
• Lubrication contamination
• Process upsets
• Cavitation events
• Higher environmental exposure
• Longer intervals between maintenance

Gradual wear can change the machine’s signal without causing immediate functional failure.

A useful AI system needs to recognise both short-term anomalies and longer-term changes in the machine’s condition.

Maintenance Implication

Baselines should not be treated as permanent and unchanging.

They require governance. Maintenance teams should review whether a gradual shift represents normal ageing, a new operating state or developing deterioration that requires intervention.

Why One Universal Threshold Can Be Misleading

Universal alarm limits are valuable.

OEM guidance, engineering judgement, safety limits and standards can help teams determine when vibration, temperature or another measurement has reached an unacceptable level.

ISO 20816-3 provides general requirements for evaluating vibration for several categories of industrial machinery under normal operating conditions. General thresholds and individual AI baselines answer different questions.

A universal threshold asks:

Has this measurement exceeded a predefined limit?

A machine-specific baseline asks:

Has this machine moved away from its own normal behaviour?

Both questions are useful.

The problem occurs when a maintenance programme uses only the first one.

Example: The Fault That Remains Below the Alarm

Consider two identical pumps:

Asset	Normal vibration	General alarm
Pump A	2.0 mm/s	5.0 mm/s
Pump B	3.5 mm/s	5.0 mm/s

Pump A’s vibration gradually increases from 2.0 mm/s to 4.2 mm/s.

It remains below the general alarm level. However, the measurement is now more than twice its previous healthy level.

Pump B remains stable at approximately 3.5 mm/s.

A threshold-only system may continue showing both machines as acceptable.

A machine-specific system may identify that Pump A has experienced a meaningful deviation and alert the maintenance team earlier.

This does not confirm the cause. The change may relate to alignment, imbalance, looseness, bearing condition, process load or another factor.

But it gives technicians an opportunity to investigate before the measurement reaches a critical level.

Example: The Healthy Machine That Produces Repeated Alerts

Now consider a machine whose normal vibration is naturally close to a general alarm threshold because of its foundation, operating load or installation arrangement.

If the same alarm is applied without context, the machine may repeatedly generate warnings despite remaining stable.

This can lead to:

• Unnecessary inspections
• Wasted maintenance hours
• Avoidable component replacement
• Reduced confidence in the monitoring system
• Alert fatigue

When teams receive too many low-value alerts, they may begin ignoring the system — including the alerts that genuinely matter.

The objective should not be to generate as many alerts as possible.

The objective is to generate actionable alerts supported by meaningful evidence.

Should AI Baselines Replace OEM or Safety Limits?

No.

Machine-specific AI baselines should complement — not replace — OEM limits, machinery-protection systems, engineering standards and safety procedures.

A practical monitoring strategy can use several layers:

Layer 1: Protection Limits

These help protect people and equipment from dangerous operating conditions and may initiate alarms or shutdowns.

Layer 2: Engineering and OEM Thresholds

These provide established reference levels for evaluating equipment condition.

Layer 3: Machine-Specific Baselines

These identify smaller or earlier deviations from the individual asset’s normal behaviour.

Layer 4: Diagnostic Interpretation

Maintenance engineers, reliability teams or specialists review the evidence and determine the probable cause and appropriate response.

AI is most valuable when it strengthens these layers rather than attempting to replace them.

How Should an AI Baseline Be Established?

A reliable baseline should represent the machine across its genuine healthy operating range.

The process should include:

1. Confirm that the machine is healthy.
   Avoid teaching the system that an existing fault is normal.
2. Collect representative data.
   Include normal loads, speeds, products, shifts and operating modes.
3. Record operational context.
   Capture relevant process information such as speed, load, flow, pressure or production state.
4. Check sensor installation.
   Verify position, orientation, mounting and data quality.
5. Document maintenance events.
   Record component changes, alignment work, lubrication and repairs.
6. Validate detected patterns.
   Involve technicians and engineers who understand the machine.
7. Review the baseline over time.
   Update it carefully when equipment or operating conditions materially change.

When Should a Machine Baseline Be Reviewed?

A baseline should be reviewed after events such as:

• Major overhaul
• Bearing or gearbox replacement
• Motor replacement
• Shaft alignment
• Balancing
• Foundation or baseplate repair
• Sensor relocation
• Significant process change
• Permanent speed or load change
• Production recipe change
• Machine relocation
• Long shutdown or recommissioning

A baseline should not automatically reset every time the machine behaves abnormally. Doing so could teach the AI that deterioration is normal.

The change should first be investigated and validated.

The Future of Industrial Monitoring: Automatic Baselining

As industrial facilities deploy condition-monitoring systems across hundreds or even thousands of assets, manually creating and maintaining a baseline for every machine becomes increasingly difficult.

The future of industrial condition monitoring is automatic baselining.

An automatic baselining system continuously analyses healthy machine data and learns the normal operating behaviour of each individual asset. Instead of requiring engineers to manually define one fixed operating range, the system can identify patterns across different:

• Loads and speeds
• Production modes
• Shifts and operating schedules
• Environmental conditions
• Startup, steady-state and shutdown periods
• Maintenance and repair events

This allows each machine to develop its own operating profile while still being evaluated against OEM recommendations, engineering thresholds and machinery-protection limits.

Automatic baselining is particularly valuable for large industrial facilities. A plant may contain hundreds of motors, pumps, fans, compressors and gearboxes operating under different conditions. Creating and regularly updating each baseline manually would require significant engineering time.

With automatic baselining, the monitoring system can:

• Learn the healthy behaviour of newly connected assets
• Recognise multiple normal operating states
• Adjust comparisons according to load, speed or production mode
• Detect when a verified repair has created a legitimate new normal
• Identify gradual changes without immediately accepting deterioration as healthy behaviour
• Scale machine-specific monitoring across an entire facility or fleet

However, an automatic baseline should not mean an uncontrolled baseline.

The system should not automatically absorb every change into the machine’s normal behaviour. An unexplained increase in vibration, temperature or sound may represent developing deterioration. Before a significant change becomes part of a new baseline, it should be investigated, supported by operational context and, where necessary, validated by the maintenance team.

This creates a practical partnership between AI and industrial expertise.

The AI continuously learns, compares and identifies meaningful changes. Maintenance and reliability teams provide context, confirm maintenance activities and determine whether a new operating pattern is healthy, temporary or potentially harmful.

As industrial AI develops, baselines are likely to become more adaptive, contextual and increasingly automated. The goal is not simply to apply the same alarm settings to more machines. It is to give every asset an intelligent reference that reflects how it actually operates in the real world.

This is the future of industrial condition monitoring: scalable automatic baselining, supported by engineering guardrails and human judgement.

What Maintenance Teams Should Ask an AI Provider

Before deploying AI condition monitoring, maintenance and reliability teams should ask:

• Is a separate baseline created for every asset?
• How does the system account for changes in speed and load?
• Can it distinguish production changes from mechanical changes?
• What operating data can be included?
• How much healthy data is needed?
• How is baseline quality validated?
• Can technicians provide feedback after an inspection?
• What happens after a repair or component replacement?
• How are false positives measured?
• Can the system show trends, waveforms or other evidence behind an alert?
• Are OEM and engineering thresholds still supported?
• How does the system prevent abnormal behaviour from being learned as normal?

These questions help determine whether the solution is providing genuine condition insight or merely applying generic limits through a new interface.

Frequently Asked Questions

Can two machines of the same make and model have different vibration levels?

Yes. Differences in foundation stiffness, alignment, mounting, process load, speed, component history and environmental conditions can produce different healthy vibration levels.

The important question is not whether the measurements are identical, but whether each machine is stable within its expected operating behaviour.

What is an AI baseline in predictive maintenance?

An AI baseline is a learned representation of a machine’s healthy operating behaviour. It may include vibration, temperature, sound and process variables across different loads and operating states.

The system compares new data with the baseline to identify unusual changes.

Does an AI anomaly mean the machine is faulty?

Not necessarily.

An anomaly means that the machine’s behaviour differs from the expected pattern. Maintenance teams should review the evidence, operating context and physical condition before confirming a fault.

Why are fixed thresholds not always enough?

Fixed thresholds can identify measurements that exceed an established limit, but they may miss smaller changes that are significant for a particular machine.

They may also create false alarms when an asset’s stable normal behaviour is naturally close to the threshold.

Should every machine have its own AI model?

Not necessarily an entirely separate AI architecture, but each asset should have an individual baseline or asset-specific context.

Shared models can provide useful knowledge across similar equipment, while individual baselines account for each machine’s installation and operating behaviour.

Can the baseline change over time?

Yes, but baseline changes should be controlled.

A verified repair, permanent process adjustment or equipment modification may create a legitimate new normal. However, an unexplained increase in vibration or temperature should not automatically be absorbed into the baseline.

How long does it take to create a machine baseline?

There is no universal duration.

The required period depends on the machine’s operating cycle, variability, available data and the number of operating states that need to be represented. A machine with a steady operating condition may require less data than one experiencing multiple products, speeds and loads.

What happens when operating conditions change frequently?

The system should include operational context such as speed, load, pressure, flow, power or production mode.

Research and industrial condition-monitoring systems recognise varying operating conditions as an important challenge because operational changes can affect measured features and mask or imitate faults. Final Takeaway

Two machines can be identical in design but different in behaviour.

Their normal signatures are shaped by:

• How they were installed
• Where they are mounted
• How they are operated
• What they process
• What maintenance they have received
• The environment around them
• Their age and individual wear history

Universal thresholds remain useful as engineering guardrails. But comparing every machine against one fixed value can produce missed warnings, false positives and low-confidence alerts.

Machine-specific AI baselines add another layer of intelligence by asking:

Is this individual machine behaving differently from the way it normally behaves under comparable conditions?

For maintenance teams, that distinction can provide earlier, more relevant and more actionable insight.

Because in the real world, identical machines do not always have an identical normal.