Industrial automation software is moving toward a new generation in which HMI and SCADA platforms are expected to do more than display process information.

Operators increasingly need systems that can help identify abnormal behavior, interpret large amounts of operational data, and provide useful information for decision-making.

ADISRA has introduced SmartView 5.0, a new generation of its HMI/SCADA platform that integrates machine learning and a rule-based expert system directly into the automation software environment.

The release reflects a broader trend across industrial automation: moving analytics and artificial intelligence closer to the operators and control systems that generate the data.

Why Traditional SCADA Is Changing

Traditional SCADA systems have several fundamental responsibilities.

They collect data.

They display process conditions.

They provide alarms.

They store historical information.

They allow operators to interact with industrial processes.

These functions remain essential.

However, modern factories generate much more data than operators can realistically monitor manually.

A single production line may contain hundreds or thousands of signals.

A large process plant can contain tens of thousands of variables.

An operator cannot continuously evaluate every trend.

This creates an opportunity for machine learning.

From Data Visualization to Data Interpretation

The next generation of industrial HMI and SCADA systems is moving from visualization toward interpretation.

Instead of simply showing that a motor temperature has increased, an intelligent system could potentially identify that the temperature trend is unusual compared with historical operating conditions.

Instead of simply showing a compressor alarm, an analytics system could evaluate multiple signals and identify a developing equipment problem.

The objective is to give operators more context.

ADISRA SmartView 5.0 combines machine learning with a rule-based expert system to provide this type of capability.

Machine Learning Inside the HMI/SCADA Environment

One of the main characteristics of SmartView 5.0 is that machine learning capabilities are built directly into the HMI/SCADA environment.

This is important from an automation engineering perspective.

Many industrial AI projects require separate analytics platforms.

Data may need to be exported from the SCADA system.

It may then be processed by an external analytics engine.

The resulting information needs to be returned to the plant environment.

This can create additional integration work.

Embedding machine learning capabilities directly into the HMI/SCADA environment can reduce some of that complexity.

The analytics functions operate closer to the data and the operators.

Combining Machine Learning With Engineering Knowledge

Machine learning is powerful at identifying patterns.

However, industrial processes require engineering context.

A machine-learning model may identify that several process variables are behaving differently.

It may not automatically understand why that matters.

This is where a rule-based expert system can add value.

Engineering rules can provide additional context.

For example, a plant may define relationships between pressure, temperature, motor load, vibration, and operating status.

The system can then combine learned patterns with engineering knowledge.

This can make industrial analytics more understandable.

Why Explainability Matters in Industrial Automation

Industrial automation decisions can have physical consequences.

Operators therefore need to understand why an alert or recommendation has been generated.

A black-box prediction may be difficult to trust.

If an intelligent system reports that a compressor may be developing a fault, the operator may need to understand which measurements contributed to the conclusion.

This is one reason hybrid approaches combining machine learning with engineering rules are attractive.

The machine-learning layer can identify complex patterns.

The rule-based layer can provide additional process context.

Together, they can produce information that is more useful to industrial personnel.

Supporting Predictive Maintenance

One of the major applications for SmartView-style machine learning is predictive maintenance.

Industrial equipment rarely fails without any warning.

There may be changes in temperature, vibration, pressure, current, speed, or other variables before a major failure occurs.

Traditional maintenance strategies may use fixed schedules.

For example, a motor might be inspected every six months.

Condition-based maintenance takes a different approach.

The equipment is monitored continuously.

Maintenance is scheduled when evidence suggests that equipment condition is deteriorating.

Machine learning can help identify subtle changes in behavior.

Industrial Air Compressors as an Example

ADISRA has highlighted industrial air compressors as one example of intelligent fault detection.

An air compressor may generate information about pressure, temperature, load, operating hours, and other variables.

Individually, each measurement may appear normal.

However, the combination of several variables may indicate a developing abnormal condition.

Machine learning can evaluate these relationships.

The system can then identify unusual patterns that may not be obvious through conventional alarm thresholds.

This is particularly valuable for rotating equipment.

Why Conventional Alarms Are Not Enough

Traditional industrial alarms are usually based on fixed thresholds.

For example:

• Pressure exceeds a limit.
• Temperature exceeds a limit.
• Motor current exceeds a limit.
• Tank level falls below a limit.

Threshold alarms are important because they are predictable and easy to understand.

However, they have limitations.

Equipment can behave abnormally without crossing a fixed threshold.

A gradual change may indicate deterioration even though the value remains inside the normal operating range.

Machine learning can evaluate trends and relationships rather than simply checking individual limits.

This can provide earlier warning.

Machine Learning Does Not Replace PLC Control

An important distinction must be made between analytics and control.

The PLC or DCS remains responsible for deterministic control logic.

Safety interlocks remain essential.

Emergency shutdown functions remain independent.

Machine learning should not automatically be treated as a replacement for safety-rated control systems.

Instead, intelligent analytics can operate alongside the control system.

The architecture can therefore look like:

Sensors → PLC/DCS → HMI/SCADA → Machine Learning → Operator Decision

In more advanced applications, analytics may also provide recommendations or controlled optimization inputs.

But the underlying deterministic control architecture remains critical.

Benefits for Automation Engineers

For automation engineers, integrating analytics into the HMI/SCADA environment can reduce system complexity.

Engineers may not need to build a completely separate data pipeline for every application.

Operational data is already available within the automation environment.

Machine learning can work with that data.

The result can be a more unified software architecture.

This also makes the technology more accessible to traditional automation teams.

Engineers who understand PLC programming, SCADA configuration, process control, and industrial networking can increasingly participate in AI projects without becoming full-time data scientists.

HMI Is Becoming More Intelligent

Historically, an HMI primarily answered one question:

What is happening right now?

Modern HMI systems are increasingly expected to answer additional questions:

Why is it happening?

What is likely to happen next?

Is this behavior normal?

What should the operator investigate?

These questions require more than visualization.

They require analytics and contextual information.

This is why intelligent HMI platforms are becoming an increasingly important part of industrial digital transformation.

Edge Computing and Industrial AI

The growth of machine learning in HMI/SCADA systems is also connected to the broader development of edge computing.

Industrial companies increasingly want data to be processed close to the machine.

There are several reasons.

First, local processing can reduce latency.

Second, it can reduce the amount of raw data transmitted to cloud platforms.

Third, some industrial environments require local operation even when external connectivity is unavailable.

Fourth, local processing can simplify data governance and security.

HMI and SCADA platforms are therefore becoming natural locations for certain types of edge analytics.

Cybersecurity Considerations

Adding intelligence to an industrial control system also creates cybersecurity considerations.

Machine learning applications need access to operational data.

If external services or remote analytics platforms are used, additional communication pathways may be created.

Therefore, security needs to be considered during system design.

Access control, network segmentation, authentication, software updates, data protection, and monitoring remain important.

The goal should be to add intelligence without creating unnecessary exposure.

The Importance of High-Quality Industrial Data

Machine learning is only as useful as the data available to it.

If sensors are poorly calibrated, timestamps are inconsistent, tags are incorrectly configured, or process data is incomplete, the resulting model may produce unreliable conclusions.

This means digital transformation still depends on traditional automation fundamentals.

Good sensors matter.

Reliable PLCs matter.

Accurate instrumentation matters.

Correct signal scaling matters.

Industrial network reliability matters.

Proper historian configuration matters.

AI does not eliminate these requirements.

Instead, AI makes them even more important.

A New Opportunity for Legacy Plants

Intelligent HMI/SCADA can also provide a path for older factories to adopt advanced analytics.

A plant does not necessarily need to replace all of its PLCs to begin using machine learning.

If existing controllers can provide reliable operational data, an analytics-capable supervisory platform may be able to use that information.

This makes incremental digital transformation possible.

Manufacturers can begin with one production line or one critical asset.

After validating the results, they can expand the technology.

This approach is often more practical than attempting a complete factory-wide transformation immediately.

What This Means for the Future of SCADA

SCADA platforms are evolving from visualization systems into operational intelligence platforms.

The basic functions of monitoring and control remain.

But new capabilities are being added around them:

• Machine learning
• Predictive maintenance
• Advanced analytics
• Rule-based reasoning
• Event correlation
• Data contextualization
• Edge computing
• Digital twins
• Energy optimization

This does not mean every SCADA platform will immediately become an AI system.

Instead, the direction of the market is toward greater integration between automation data and intelligent software.