Industrial compressed-air systems are essential to modern manufacturing, yet automation discussions often overlook them. Because compressed air is often treated as basic utility infrastructure, much like electricity or water, compressors can run quietly in the background until declining performance, excessive energy use, or an unexpected failure disrupts production.
That is beginning to change. The global industrial air compressor market was valued at approximately $20.0 billion in 2025 and is projected to grow from $20.9 billion in 2026 to $28.3 billion by 2033. Increasing demand for compressed air across manufacturing, food processing, automotive, energy, construction, and other industries is driving this growth. At the same time, manufacturers are under increasing pressure to improve energy efficiency, reduce unplanned downtime, and get more value from existing equipment and operational data.
This creates an ideal opportunity for machine learning.
Modern compressors generate a continuous stream of operational data, including discharge pressure, temperature, motor current, vibration, airflow, and load state. Individually, these measurements tell us what is happening at a particular moment. Together, they can reveal operating patterns, detect subtle changes in equipment behavior, and help identify developing faults before they become serious problems.
But no single machine-learning algorithm answers every question. Intelligent fault detection becomes much more powerful when several analytical techniques work together:
– Clustering — What patterns exist? Discover groups of similar compressor behavior without requiring predefined labels.
– Multivariate Anomaly Detection — Is something changing? Identify unusual relationships among multiple process variables, even when individual measurements remain within acceptable limits.
– Multiclass Classification — What is most likely wrong? Compare abnormal behavior with known fault conditions and identify the most likely fault.
– Rule-Based Expert System — What should we do about it? Combine machine-learning results with engineering knowledge and real-time operating conditions to determine severity and recommend appropriate action.
Together, these technologies can move us from:
With the machine-learning algorithms and Rule-Based Expert System built into ADISRA SmartView 5.0, these capabilities can become part of the HMI/SCADA environment itself. Instead of machine learning operating as a separate analytics tool, compressor data can be analyzed alongside the real-time process.
In this blog, we will follow an industrial air compressor as its behavior begins to change and see how these four technologies can work together to answer four practical questions:
What patterns exist? Is something changing? What is most likely wrong? And what should we do about it?
What Is an Industrial Air Compressor and What Is It Used For?
An industrial air compressor takes air from the surrounding environment, compresses it to increase its pressure, and supplies that compressed air to equipment and processes throughout a facility. Depending on the system, compressed air may be stored in a receiver tank and distributed through piping wherever it is needed.
Compressed air is sometimes called the “fourth utility” in manufacturing because, like electricity, water, and natural gas, it supports a wide range of production processes. It can power pneumatic cylinders and actuators, operate tools, control valves, move and position products, clean components, provide blow-off air, and support automated machinery.
The automotive industry is a good example. Automotive manufacturing depends on precision, speed, and reliability across stamping, welding, painting, assembly, and material-handling operations. Compressed air supports pneumatic tools, actuators, robotic equipment, clamping systems, part handling, and other automated processes throughout the plant.
For example, we will focus on a metal stamping operation.
A metal stamping machine forms or cuts sheet metal into specific shapes using dies and considerable force. In automotive manufacturing, stamping processes commonly produce body panels, brackets, structural components, and many other metal parts.
While a stamping press may be mechanical, servo-driven, or hydraulic, compressed air can support many operations around the press. Pneumatic cylinders and actuators can position components, operate clamps, assist with part handling, control tooling, and remove scrap or finished parts using compressed-air blow-off.
Why Monitor the Air Compressor?
If the compressor supplying this equipment begins to deteriorate, the effects can extend beyond the compressor itself. Changes in pressure, airflow, temperature, vibration, or motor current may indicate declining performance or a developing mechanical problem. Eventually, unreliable compressed air can affect the production equipment that depends on it.
Fortunately, the compressor continuously generates data that helps us understand what is happening.
For our example, we will monitor pressure, airflow, discharge temperature, vibration, motor current, and load state. Instead of looking at each measurement independently, we use machine learning to examine patterns and relationships in the data.
We use this data to understand how the compressor normally behaves, detect when that behavior changes, determine the most likely fault, and recommend what maintenance personnel should do.
Let us start with clustering.
What Patterns Exist? — Clustering
Think about sorting laundry before washing it. You might separate clothes based on similarities such as color, fabric, or type. Clustering works in much the same way, except instead of sorting clothes, the algorithm sorts data.
Clustering is an unsupervised machine-learning algorithm, meaning we do not have to tell it beforehand what each data point represents. Instead, the algorithm analyzes the data, identifies similarities, and groups data points that behave alike.
For our industrial air compressor, we can provide historical operating data such as:
– Motor current
– Discharge pressure
– Airflow
– Discharge temperature
– Vibration
– Load state
The algorithm analyzes relationships among these variables and groups similar operating conditions.
Discovering the Compressor’s Operating Patterns
Our compressor supplies compressed air to an automotive metal stamping operation. Demand changes as production operates, so the compressor does not behave exactly the same way all the time.
After analyzing historical sensor data, clustering might discover several distinct groups.
One cluster could represent periods when the compressor is loaded and actively supplying compressed air. These data points might share higher motor current, greater airflow, and relatively stable discharge pressure.
Another cluster might represent periods when the compressor is unloaded or operating at low demand, with lower motor current and airflow while system pressure remains within its normal operating range.
Additional clusters could represent high-demand production, startup, or other recurring operating states.
The important point is that we did not tell the clustering algorithm what these groups were. It discovered them by finding similarities within the compressor data. An engineer can then examine each cluster’s characteristics and determine what they most likely represent.
Establishing a Baseline of Normal Behavior
This gives us a picture of how the compressor typically behaves under different operating conditions.
Instead of defining “normal” as a single pressure, temperature, or vibration value, we can understand normal operation as patterns involving multiple process variables.
For example, higher motor current by itself may not indicate a problem. Higher current accompanied by increased airflow during high production demand could be perfectly normal. Likewise, lower airflow may simply indicate an unloaded compressor.
Clustering provides context by helping us discover these operating patterns. However, clustering alone does not necessarily tell us whether a new condition represents a developing failure.
That leads to our next question:
What happens when the compressor begins behaving differently from the patterns we have learned to expect?
Is Something Changing? — Multivariate Anomaly Detection
Traditional industrial monitoring often relies on fixed alarm thresholds. If discharge temperature exceeds a specified limit, generate an alarm. If vibration exceeds an acceptable level, generate another alarm.
These alarms remain extremely important, particularly for conditions that have reached established operational or safety limits.
But what if several measurements begin changing together before any individual value crosses its alarm threshold?
Imagine our compressor is operating in a familiar production state. Discharge temperature begins to rise slightly. Vibration increases. Motor current also moves upward. Each measurement is still within its acceptable range, so none of the traditional alarms are triggered.
Taken together, however, these changes may tell a very different story.
This is where Multivariate Anomaly Detection becomes valuable.
Rather than evaluating one variable at a time, multivariate anomaly detection analyzes multiple variables simultaneously and learns the relationships that characterize normal compressor behavior, including:
– Vibration
– Discharge temperature
– Discharge pressure
– Airflow
– Motor current
– Load state
The algorithm can recognize when the combination and relationships among these measurements begin to deviate from the behavior it has learned to expect.
For example:
– Air leak: Pressure or system performance declines while the compressor runs longer or consumes more power to maintain demand.
– Bearing problem: Vibration and temperature increase together, potentially accompanied by changes in motor current.
– Restricted airflow or clogged filter: Airflow decreases while the compressor works harder to meet system demand.
The important distinction is that no single measurement necessarily proves something is wrong. The relationship among multiple measurements makes the behavior unusual.
Consider our compressor again. Vibration is gradually increasing, discharge temperature is trending upward, and motor current has increased slightly. None has reached its individual alarm limit.
A traditional threshold-based system might still indicate that everything is within acceptable limits. The multivariate anomaly detection model, however, recognizes that this combination differs from the compressor’s learned behavior and flags the condition as anomalous.
We now have an important early indication:
Something is changing.
But anomaly detection does not necessarily tell us what is wrong. The anomaly could result from bearing degradation, restricted airflow, an air leak, a cooling problem, a change in operating conditions, or something else.
Now we need to answer:
What is the most likely fault?
What Is Most Likely Wrong? — Multiclass Classification
Multivariate Anomaly Detection has identified an unusual combination of increasing vibration, rising discharge temperature, and slightly higher motor current.
But the maintenance team needs more than an anomaly alert. They want to know:
What is most likely wrong?
This is where Multiclass Classification comes into the picture.
Multiclass classification is a supervised machine-learning technique that takes input data and assigns it to one of several predefined categories, or classes. Unlike clustering, where the algorithm discovers groups without predefined labels, multiclass classification is trained using historical examples with known outcomes.
For our industrial air compressor, we might train the model using historical sensor data associated with five operating conditions:
– Normal Operation
– Bearing Degradation
– Clogged Air Filter
– Cooling System Problem
– Air Leak
During training, the model learns the combinations of vibration, temperature, pressure, airflow, motor current, load state, and other variables associated with each condition.
From Anomaly to Possible Diagnosis
Let us return to our compressor.
Multivariate Anomaly Detection has already identified an unusual combination of increasing vibration, rising discharge temperature, and slightly higher motor current.
The Multiclass Classification model evaluates those measurements against patterns learned from previously labeled operating and fault data and might determine:
Predicted Fault: Bearing Degradation
Instead of simply telling the operator, “Something does not look right,” the system can now provide a possible explanation:
This unusual behavior most closely resembles bearing degradation.
Why Classification Is Not the Final Answer
A machine-learning classification should not automatically be treated as a confirmed diagnosis.
Industrial equipment operates under changing conditions, and different faults can produce similar sensor patterns. A clogged filter and an air leak, for example, might both affect airflow, pressure, motor current, and compressor loading.
Classification accuracy also depends heavily on training data. Industrial equipment may operate normally for months or years while actual failures occur relatively infrequently. That can result in large amounts of normal data but relatively few examples of each fault.
Accurate classification therefore depends on good sensor data, correctly labeled historical maintenance records, and representative examples of different operating and fault conditions.
The goal is not to replace the maintenance technician’s expertise. It is to use historical data to narrow the possibilities and provide a more informed starting point.
Now suppose our model predicts bearing degradation. Should we shut down the compressor immediately? Reduce its load? Schedule an inspection? Or simply continue monitoring it?
Machine learning can suggest the most likely fault, but operational decisions require engineering knowledge and context.
That brings us to the final step.
What Should We Do About It? — Rule-Based Expert System
At this point, our intelligent fault-detection system has discovered the compressor’s operating patterns, detected unusual behavior, and identified bearing degradation as the most likely fault.
But the operator still needs an answer to the most important question:
What should we do about it?
A Rule-Based Expert System uses predefined IF-THEN rules to capture engineering knowledge and apply it to real-time operating conditions.
Think of it as taking the knowledge an experienced compressor technician uses to troubleshoot equipment and expressing it as rules the software can continuously evaluate.
An experienced technician probably would not replace a bearing simply because a machine-learning model predicts bearing degradation. The technician would look for supporting evidence. Is vibration increasing? Is temperature rising? Is the compressor heavily loaded? How severe is the condition?
A rule-based expert system can perform a similar evaluation.
Combining Machine Learning with Engineering Knowledge
Our Multiclass Classification model has determined that the abnormal sensor pattern most closely resembles bearing degradation. Instead of immediately acting on that prediction, the Rule-Based Expert System evaluates it against actual operating conditions.
For example:
IF Multiclass Classification = Bearing Degradation
AND Vibration is above the warning level
AND Temperature is increasing
AND Compressor Load > 70%
THEN
Confirm suspected bearing degradation
Set condition = Maintenance Required
Recommend: Schedule bearing inspection during the next planned maintenance window
Now suppose the condition becomes more severe:
IF Multiclass Classification = Bearing Degradation
AND Vibration exceeds the critical limit
AND Temperature exceeds the critical limit
THEN
Set condition = Critical
Recommend: Reduce compressor load and inspect immediately
Machine learning provides evidence about what may be happening, while the Rule-Based Expert System applies engineering knowledge to determine what that information means operationally.
Making the Decision Explainable
One of the biggest advantages of a Rule-Based Expert System is transparency.
Instead of simply displaying:
WARNING: Compressor Problem
the HMI/SCADA application could provide:
Suspected Fault: Bearing Degradation
Supporting Conditions: Increasing vibration + rising temperature + elevated motor current
Severity: High
Recommended Action: Reduce load and schedule bearing inspection
Reason: Machine-learning diagnosis confirmed by predefined engineering rules
Because the logic is explicitly defined, engineers can understand why the system reached its conclusion. If experience, operating requirements, or maintenance practices change, engineers can modify the rules without retraining the machine-learning model.
New engineering knowledge can also be incorporated over time. If maintenance personnel discover a new combination of conditions that reliably indicates a particular problem, that knowledge can become another rule.
Conclusion: From Compressor Data to Intelligent Action
Our industrial air compressor started with something manufacturers already have in abundance: data.
Clustering helped us discover patterns in that data and understand how the compressor typically operates.
Multivariate Anomaly Detection identified when the relationships among vibration, temperature, pressure, airflow, motor current, and other variables began to change.
Multiclass Classification used those patterns to determine that the abnormal behavior most closely resembled bearing degradation.
Finally, the Rule-Based Expert System combined the machine-learning diagnosis with real-time process conditions and engineering knowledge to determine the problem’s severity and recommend an appropriate response.
We have moved from:
DATA → DETECTION → DIAGNOSIS → ACTION
That last step is particularly important in industrial automation.
Operators and maintenance personnel do not just need an algorithm to tell them, “I found something unusual.” They need answers to the questions that follow:
What is happening? Why is it happening? How serious is it? And what should I do about it?
Combining machine learning with engineering knowledge can make industrial analytics more practical, explainable, and actionable. Machine learning can uncover patterns that are difficult to identify by monitoring individual process values, while a Rule-Based Expert System can provide the context needed to turn those findings into operational decisions.
With ADISRA SmartView 5.0, machine-learning algorithms and the Rule-Based Expert System are integrated directly into the HMI/SCADA environment, making industrial data and analytics part of the same environment operators and engineers already use to monitor and manage their processes.
While we used an industrial air compressor supporting an automotive stamping operation for this example, the same approach applies to many industrial assets and processes, from pumps and motors to conveyors, chillers, production machines, and other critical equipment.
Ready to Put Your Industrial Data to Work?
Download ADISRA SmartView 5.0 at the beginning of September and explore how machine learning and a Rule-Based Expert System can turn your industrial data into actionable intelligence. You can download ADISRA SmartView here.
Want to see how these capabilities could work in your application? Request an ADISRA SmartView 5.0 demonstration here.
At ADISRA, we work directly with customers to understand their applications and how our software can fit into their industrial environment. If you have questions or would like to discuss a potential machine-learning application, contact us at info@adisra.com.
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