PLC SCADA and DCS: Introduction
Artificial Intelligence is becoming a hot topic in the automation world. From PLC programming to SCADA configuration and even DCS system design, engineers are now wondering whether AI tools can actually help in real industrial work or whether they are just good at giving theoretical answers.
There is also growing concern around job security. Many automation professionals are asking a serious question:
Will AI tools replace PLC and DCS engineers in the future?
Instead of guessing, this article takes a hands-on, practical approach.
We evaluate how an AI tool performs when tested against real-world PLC SCADA and DCS engineering scenarios, exactly the kind of problems automation engineers face on-site and during commissioning.
Part 1: Evaluating AI for PLC Troubleshooting and Programming
PLC troubleshooting is a daily activity for automation engineers. It requires logical thinking, field experience, and a structured approach. To test AI capability, common PLC-related problems were presented and the responses were carefully evaluated.
PLC Scenario 1: I/O Module Not Responding
A very common issue in plants is when sensors appear to be working, but the PLC does not react. At first glance, it looks like a communication failure, but the actual cause can be electrical, wiring-related, or program-related.
Power Supply and Grounding Checks
The AI starts with the most fundamental checks:
Verifying that all PLC and I/O modules are receiving the correct power supply
Checking for blown fuses or tripped circuit breakers
Ensuring proper grounding of PLC racks and I/O modules
These steps are basic but extremely important. Many real-life failures are resolved at this stage itself.
Wiring and Physical Connection Inspection
Next, the focus moves to physical connections:
Inspecting wiring between sensors, I/O modules, and PLC
Checking for loose terminals, damaged cables, or incorrect wiring
Confirming sensors are connected to the correct terminals
This reflects real site troubleshooting, where simple wiring mistakes often cause major confusion.
Checking I/O Module Status Indicators
The AI correctly emphasizes the importance of module LEDs:
Power LEDs confirming module energization
Communication LEDs showing PLC connectivity
Input/output LEDs reflecting real-time signal changes
Abnormal LED behavior usually points directly to the fault area.
PLC Program and Hardware Configuration Review
After hardware checks, software verification becomes critical:
Confirming that the configured I/O modules in the PLC software match the physical hardware
Ensuring inputs are correctly mapped in the program
Checking whether any logic disables or overwrites I/O signals
This step is especially important in modified or expanded systems.
Sensor Signal Verification
The AI also suggests signal-level testing:
Using a multimeter or oscilloscope to check sensor outputs
Manually triggering sensors and observing input LEDs
Verifying analog signal ranges such as 4–20 mA or 0–10 V
This shows a good understanding of instrumentation fundamentals.
PLC Scenario 2: Motor Not Starting on Start Command
Another classic problem was tested: a motor does not start when the start button is pressed, even though the hardware appears healthy.
Input and Safety Circuit Checks
The AI begins correctly by checking:
Start button signal using a multimeter
PLC input LED status
Stop buttons, emergency stops, overload relays, and interlocks
In many plants, safety circuits are the actual reason motors fail to start.
Logic and Output Troubleshooting
The AI continues with:
Reviewing ladder logic for correct input usage
Verifying output coil energization
Checking output addressing
Testing contactor and motor starter coils
This sequence closely matches real commissioning workflows.
Can AI Write a Basic PLC Program?
The AI was also tested for writing a simple PLC program for pump or motor control.
It successfully generated:
Start and stop logic
Output control logic
Address mapping
While small corrections may be needed, especially regarding normally open and normally closed contacts, the logic structure is usable particularly for beginners.
PLC–HMI and SCADA Communication Support
The AI also demonstrated decent capability in explaining:
PLC–HMI communication setup
Tag mapping and addressing
Basic SCADA communication concepts
Modbus-based communication fundamentals
Although the explanations are generic, they provide a useful starting point.
Part 2: Testing AI for DCS Engineering Tasks
DCS systems are very different from PLC systems. They control entire plants such as refineries, power stations, and chemical units. DCS engineering involves redundancy, advanced control strategies, alarm management, and historian systems.
The key question tested was simple but critical:
Can AI design a complete DCS control system from scratch?
DCS Scenario: Pressure Control Loop for a Refinery
The AI was asked to design a DCS-based pressure control system including:
PID loop configuration
HMI visualization
Alarm handling
Data logging
Redundancy considerations
Where AI Performs Well
The AI correctly explains:
The purpose of PID control
High and low pressure alarms
Setpoint adjustment concepts
Operator interaction through HMI
These explanations are useful from a conceptual learning perspective.
Where AI Falls Short
However, major gaps become visible:
The responses remain generic
No platform-specific implementation workflow is explained
Incorrect or vague references to configuration tools appear
No real function block diagrams are provided
DCS systems require deep knowledge of:
Control databases
Engineering tools
Redundancy architecture
Security and change management
Generic guidance is not enough to build or commission a real DCS system.
Comparison Between AI Responses
When comparing two different AI responses for the same DCS task:
One gives faster and better-structured answers
The other struggles with platform-specific details
However, neither can replace a real DCS engineer. The responses resemble guidance from someone who understands concepts but lacks hands-on system experience.
Are Automation Jobs at Risk?
PLC Engineers
AI can:
Assist in troubleshooting
Help beginners learn faster
Save time during diagnostics
But it cannot replace field experience.
DCS Engineers
AI:
Cannot design or configure real DCS systems independently
Lacks platform-specific depth
Cannot handle commissioning, safety, or responsibility
DCS engineering jobs are safe.
The Real Role of AI in Automation Engineering
AI should be seen as:
A learning assistant
A troubleshooting guide
A productivity booster
Not as a replacement for skilled engineers.
Industrial automation is still a human-driven discipline. PLC and DCS systems demand experience, judgment, and responsibility especially when safety and plant reliability are involved.
Engineers who use AI wisely as a tool will grow faster.
Engineers who fear AI may fall behind.
The future belongs to engineers + AI, not AI alone.
I hope you like above blog. There is no cost associated in sharing the article in your social media. Thanks for Reading !! Happy Learning