Scripts → Tutorial | Concept | How-to Guide | Reference

Step 1: Create Monitoring Tags

1. Navigate to Unified Namespace → Tags
2. Create tags:
   • Tag.SensorValue (Double) - Current reading
   • Tag.AnomalyScore (Double) - Detection score
   • Tag.IsAnomaly (Boolean) - Alert flag
   • Tag.Threshold (Double) - Detection threshold (default: 0.3)

Step 2: Create ML Detection Task

1. Go to Scripts → Tasks
2. Create task: AnomalyDetector
3. Set trigger: Period = 1000ms
4. Add code:

csharp

// Simple spike detection using ML.NET
using Microsoft.ML;
using Microsoft.ML.Data;

// Static ML context (initialized once)
if (@Tag.MLContext == null)
{
    @Tag.MLContext = new MLContext(seed: 0);
    @Tag.DetectionEngine = InitializeDetector();
}

// Data class for ML model
public class SensorData
{
    public float Value { get; set; }
}

public class AnomalyPrediction
{
    [VectorType(3)]
    public double[] Prediction { get; set; }
}

// Initialize detector (runs once)
private ITransformer InitializeDetector()
{
    var dataView = @Tag.MLContext.Data.LoadFromEnumerable(new List<SensorData>());
    
    var pipeline = @Tag.MLContext.Transforms
        .DetectSpikeBySsa(
            outputColumnName: "Prediction",
            inputColumnName: "Value",
            confidence: 95,
            pvalueHistoryLength: 30,
            trainingWindowSize: 90,
            seasonalityWindowSize: 30);
    
    return pipeline.Fit(dataView);
}

// Detection logic (runs every second)
var currentValue = (float)@Tag.SensorValue;

var data = new SensorData { Value = currentValue };
var prediction = @Tag.DetectionEngine.Transform(
    @Tag.MLContext.Data.LoadFromEnumerable(new[] { data }));

var result = @Tag.MLContext.Data
    .CreateEnumerable<AnomalyPrediction>(prediction, false)
    .First();

// Update tags with results
@Tag.AnomalyScore = result.Prediction[0];  // Spike score
@Tag.IsAnomaly = result.Prediction[0] > @Tag.Threshold;

// Log anomalies
if (@Tag.IsAnomaly)
{
    @Alarm.GlobalSettings.AuditTrail.AddCustomMessage(
        $"Anomaly detected: Sensor={currentValue:F2}, Score={result.Prediction[0]:F3}");
}

Step 3: Create Simple Simulator

1. Create task: SensorSimulator
2. Set trigger: Period = 500ms
3. Add simulation code:

csharp

// Simulate normal sensor data with occasional spikes
Random rand = new Random();
double baseValue = 50.0;
double noise = rand.NextDouble() * 5 - 2.5;

// Inject anomaly occasionally (5% chance)
if (rand.NextDouble() < 0.05)
{
    @Tag.SensorValue = baseValue + (rand.NextDouble() * 30 + 20);  // Spike
}
else
{
    @Tag.SensorValue = baseValue + noise;  // Normal variation
}

Step 4: Create Monitoring Display

1. Create display with:
   • Trend chart for SensorValue
   • Indicator for IsAnomaly
   • Text display for AnomalyScore
   • Threshold adjustment slider

Step 5: Test ML Detection

1. Start runtime
2. Observe sensor simulation
3. Watch for anomaly detection
4. Adjust threshold as needed
5. Check audit trail for logged anomalies

Next Steps

• Advanced MCP Tools → Complex multi-tool scenarios
• ML.NET Models → Regression and classification
• Edge Computing → Deploy to field devices

These tutorials provide simple, practical starting points for both MCP Tools and ML.NET integration, focusing on real industrial scenarios while keeping complexity minimal for learning purposes.

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