AI and machine learning in SCADA in SCADA systems - Step-by-Step Execution

Process Flow - AI and machine learning in SCADA

Data Collection from Sensors

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Data Preprocessing & Cleaning

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Feature Extraction

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Train ML Model

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Deploy Model in SCADA

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Real-time Monitoring & Prediction

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Trigger Alerts or Actions

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Feedback Loop

↩Back to Data Collection

This flow shows how SCADA systems collect sensor data, preprocess it, train AI models, deploy them for real-time monitoring, and use feedback to improve continuously.

Execution Sample

SCADA systems

sensor_data = collect_data()
clean_data = preprocess(sensor_data)
model = train_model(clean_data)
prediction = model.predict(new_data)
if prediction > threshold:
    trigger_alert()

This code collects sensor data, cleans it, trains a model, predicts on new data, and triggers an alert if prediction exceeds a threshold.

Process Table

Step	Action	Input	Output	System State Change
1	collect_data()	Raw sensor signals	Raw data array	Data collected from sensors
2	preprocess(raw data)	Raw data array	Cleaned data array	Noise removed, missing values handled
3	train_model(cleaned data)	Cleaned data array	Trained ML model	Model parameters set
4	model.predict(new_data)	New sensor data	Prediction value	Prediction ready for decision
5	if prediction > threshold	Prediction value	True/False	Decision to alert or not
6	trigger_alert()	True condition	Alert sent	Operator notified
7	Feedback loop	Operator response	Model updated	Model improved with new data

💡 Process repeats continuously for real-time monitoring and improvement

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
sensor_data	None	Raw sensor signals	Raw sensor signals	Raw sensor signals	Raw sensor signals	Raw sensor signals	Raw sensor signals	Raw sensor signals
clean_data	None	None	Cleaned data array	Cleaned data array	Cleaned data array	Cleaned data array	Cleaned data array	Cleaned data array
model	None	None	None	Trained ML model	Trained ML model	Trained ML model	Trained ML model	Updated ML model
prediction	None	None	None	None	Prediction value	Prediction value	Prediction value	Prediction value
alert_status	False	False	False	False	False	True/False	True	True

Key Moments - 3 Insights

Why do we preprocess sensor data before training the model?

Preprocessing cleans the data by removing noise and fixing missing values, which helps the model learn better. See execution_table step 2 where raw data becomes clean data.

What happens if the prediction does not exceed the threshold?

No alert is triggered and the system continues monitoring. This is shown in execution_table step 5 where the condition is False and no alert is sent.

How does the feedback loop improve the model?

Operator responses and new data are used to update the model, making it more accurate over time. This is shown in execution_table step 7 where the model is updated.

Visual Quiz - 3 Questions

Look at the execution table, what is the output of step 3?

A Cleaned data array

B Trained ML model

C Prediction value

D Raw data array

💡 Hint

Check the 'Output' column for step 3 in the execution_table.

At which step does the system decide to trigger an alert?

A Step 4

B Step 5

C Step 6

D Step 7

💡 Hint

Look at the 'Action' and 'System State Change' columns around decision making in the execution_table.

If sensor data is noisy and not cleaned, which step's output will be affected most?

A Step 1

B Step 2

C Step 3

D Step 6

💡 Hint

Consider how clean data affects model training in the variable_tracker and execution_table.

Concept Snapshot

AI and ML in SCADA:
1. Collect sensor data continuously.
2. Clean and preprocess data to remove noise.
3. Train ML models on clean data.
4. Deploy models for real-time predictions.
5. Trigger alerts based on predictions.
6. Use feedback to improve models over time.

Full Transcript

This visual execution shows how AI and machine learning integrate with SCADA systems. First, sensor data is collected from the industrial environment. Then, this raw data is cleaned to remove noise and handle missing values. Next, a machine learning model is trained using the clean data. The trained model predicts outcomes on new incoming data. If the prediction exceeds a set threshold, an alert is triggered to notify operators. Finally, operator feedback and new data are used to update and improve the model continuously. This cycle repeats to maintain real-time monitoring and smart decision-making in SCADA.