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language	yaml
title	Skill Frontmatter

---
title: "Edge ML Pipeline — ML.NET Anomaly Detection"
tags: [ml,

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machine-learning, anomaly, detection, mlnet, script, prediction, edge-ai, intelligence]

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description: "End-to-end pipeline: sensor tags

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 ? data collection

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 ? ML.NET anomaly detection model

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 ? prediction tags

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 ? alarms ? dashboard"
version: "1.0"
min_product_version: "10.1"
author: "Tatsoft"
---

Excerpt
Build a complete edge ML pipeline that runs ML.NET anomaly detection on-premise: sensor tags feed an ML script class, predictions are written to output tags, wired to alarms, and displayed on a dashboard.

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What This Skill Does

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Build a complete edge ML pipeline that runs deterministic machine learning models on-premise using ML.NET. Data flows from sensor tags through an anomaly detection model, and results are written back to tags for alarms

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and visualization.

Code Block

language	text
title	Pipeline Architecture

Sensor Tags

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? ML Script Class

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? Prediction Tags

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? Alarms

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? Dashboard

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?
  Devices/Simulator                                    Operator sees anomalies

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Table of Contents

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When to Use This Skill

Use this skill when:

The user wants to add machine learning or anomaly detection to a solution
The user mentions "predictive maintenance", "ML", "anomaly", or "edge AI"
The user wants to run ML models on-premise (not cloud-based)
Building a ProveIT-style demo with intelligent monitoring

Do NOT use this skill when:

The user wants cloud AI / LLM integration (use MCP for Runtime skill instead)
The user needs only simple threshold alarms (use skill-alarm-pipeline

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)
The user wants to train a model (this skill covers inference only — for training, see ML.NET Model Builder docs)

Prerequisites

Solution with sensor tags already created (at least 1-2 analog tags with changing values)
Value Simulator or real device feeding data to those tags
If starting from scratch, apply skill-getting-started first

MCP Tools and Tables Involved

Category	Items
Tools

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	`get_table_schema`, `write_objects`, `get_objects`, `list_elements`, `search_docs`
Tables

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UnsTags, ScriptsTasks, ScriptsClasses, ScriptsExpressions, AlarmsItems, AlarmsGroups, DisplaysList

Implementation Steps

Step 1: Create ML Output Tags

Before writing the ML script, create tags to receive the model's predictions. These sit alongside the sensor tags in the UNS.

Code Block

language	text

get_table_schema('UnsTags')

Code Block

language	json
title	write_objects call

{
  "table_type": "UnsTags",
  "data": [
    {
      "Name": "Plant/Reactor1/ML/AnomalyScore",
      "DataType": "Double",
      "Description": "ML anomaly score (0=normal, higher=more anomalous)"
    },
    {
      "Name": "Plant/Reactor1/ML/IsAnomaly",
      "DataType": "Boolean",
      "Description": "True when anomaly detected by ML model"
    },
    {
      "Name": "Plant/Reactor1/ML/Confidence",
      "DataType": "Double",
      "Description": "Model prediction confidence (0-1)"
    },
    {
      "Name": "Plant/Reactor1/ML/LastPrediction",
      "DataType": "DateTime",
      "Description": "Timestamp of last ML prediction"
    }
  ]
}

Key decisions:

Place ML outputs under a /ML/ subfolder for clean separation from raw sensor data
AnomalyScore is continuous (for trending), IsAnomaly is boolean (for alarms)
Confidence lets the operator gauge reliability
LastPrediction timestamp helps detect if the model stops running

Step 2: Import the AnomalyML Script Class from Library

FrameworX ships with a pre-built AnomalyML class in the Script Library. Import it rather than writing from scratch.

Code Block

language	text

get_table_schema('ScriptsClasses')

To import from library,

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instruct the user to:

Navigate to Scripts → Classes
Click New → Import from Library
Select AnomalyML

Alternatively, create the class via MCP with the anomaly detection logic:

Code Block

language	json
title	write_objects — ScriptsClasses
collapse	true

{
  "table_type": "ScriptsClasses",
  "data": [
    {
      "Name": "AnomalyML",
      "ClassContent": "Methods",
      "Code": "// ML.NET Anomaly Detection\nusing Microsoft.ML;\nusing Microsoft.ML.Data;\nusing Microsoft.ML.TimeSeries;\n\nprivate static MLContext mlContext = new MLContext(seed: 0);\nprivate static ITransformer model;\nprivate static List<SensorData> trainingBuffer = new List<SensorData>();\nprivate static bool modelTrained = false;\nprivate const int TrainingWindowSize = 100;\nprivate const int SeasonalityWindowSize = 10;\n\npublic class SensorData\n{\n    public float Value { get; set; }\n}\n\npublic class AnomalyPrediction\n{\n    [VectorType(7)]\n    public double[] Prediction { get; set; }\n}\n\npublic static void Check(double sensorValue)\n{\n    trainingBuffer.Add(new SensorData { Value = (float)sensorValue });\n    \n    if (!modelTrained && trainingBuffer.Count >= TrainingWindowSize)\n    {\n        TrainModel();\n    }\n    \n    if (modelTrained)\n    {\n        RunPrediction(sensorValue);\n    }\n}\n\nprivate static void TrainModel()\n{\n    var dataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);\n    var pipeline = mlContext.Transforms.DetectSpikeBySsa(\n        outputColumnName: nameof(AnomalyPrediction.Prediction),\n        inputColumnName: nameof(SensorData.Value),\n        confidence: 95.0,\n        pvalueHistoryLength: SeasonalityWindowSize,\n        trainingWindowSize: TrainingWindowSize,\n        seasonalityWindowSize: SeasonalityWindowSize);\n    \n    model = pipeline.Fit(dataView);\n    modelTrained = true;\n}\n\nprivate static void RunPrediction(double sensorValue)\n{\n    var dataView = mlContext.Data.LoadFromEnumerable(\n        new[] { new SensorData { Value = (float)sensorValue } });\n    var predictions = model.Transform(dataView);\n    var results = mlContext.Data.CreateEnumerable<AnomalyPrediction>(\n        predictions, reuseRowObject: false).First();\n    \n    double isAnomaly = results.Prediction[0];\n    double score = results.Prediction[1];\n    double pValue = results.Prediction[2];\n    \n    @Tag.Plant/Reactor1/ML/IsAnomaly.Value = isAnomaly > 0;\n    @Tag.Plant/Reactor1/ML/AnomalyScore.Value = Math.Abs(score);\n    @Tag.Plant/Reactor1/ML/Confidence.Value = 1.0 - pValue;\n    @Tag.Plant/Reactor1/ML/LastPrediction.Value = DateTime.Now;\n}"
    }
  ]
}

Info

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The code above is a reference implementation. The actual AnomalyML library class may differ. Always check for the library version first via the Designer UI.

Step 3: Create

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Script

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Expressions to Trigger the Model

Expressions connect the ML class to live tag changes. When a sensor value changes, the expression calls the ML model.

Code Block

language	text

get_table_schema('ScriptsExpressions')

Code Block

language	json
title	write_objects — single sensor

{
  "table_type": "ScriptsExpressions",
  "data": [
    {
      "Name": "ML_CheckTemperature",
      "Expression": "Script.Class.AnomalyML.Check(Tag.Plant/Reactor1/Temperature)",
      "Execution": "OnChange",
      "TriggerTag": "Plant/Reactor1/Temperature"
    }
  ]
}

To monitor multiple tags, add one expression per tag

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:

Code Block

language	json
title	write_objects — multiple sensors

{
  "table_type": "ScriptsExpressions",
  "data": [
    {
      "Name": "ML_CheckTemperature",
      "Expression": "Script.Class.AnomalyML.Check(Tag.Plant/Reactor1/Temperature)",
      "Execution": "OnChange",
      "TriggerTag": "Plant/Reactor1/Temperature"
    },
    {
      "Name": "ML_CheckPressure",
      "Expression": "Script.Class.AnomalyML.Check(Tag.Plant/Reactor1/Pressure)",
      "Execution": "OnChange",
      "TriggerTag": "Plant/Reactor1/Pressure"
    }
  ]
}

Step 4: Add Alarm on Anomaly Detection

Create an alarm that triggers when the ML model detects an anomaly.

Code Block

language	text

get_table_schema('AlarmsGroups')
get_table_schema('AlarmsItems')

Code Block

language	json
title	write_objects multi-table call

{
  "tables": [
    {
      "table_type": "AlarmsGroups",
      "data": [
        { "Name": "MLAlarms", "Description": "Machine Learning generated alarms" }
      ]
    },
    {
      "table_type": "AlarmsItems",
      "data": [
        {
          "Name": "AnomalyDetected",
          "Group": "MLAlarms",
          "TagName": "Plant/Reactor1/ML/IsAnomaly",
          "Type": "Digital",
          "Description": "ML model detected anomaly on Reactor 1"
        }
      ]
    }
  ]
}

Step 5: Create ML Dashboard

Build a display that shows sensor data alongside ML predictions.

Code Block

language	text

list_elements('Dashboard,TrendChart,CircularGauge')
get_table_schema('DisplaysList')

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Code Block

language	json
title	write_objects — ML Dashboard

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{
  "table_type": "DisplaysList",
  "data": [
    {
      "Name": "MLMonitor",
      "PanelType": "Dashboard",
      "Columns": 2,
      "Rows": 3,
      "Title": "ML Anomaly Monitor",
      "Elements": [
        {
          "Type": "TrendChart",
          "Column": 0, "Row": 0, "ColumnSpan": 2,
          "Pens": [
            { "TagName": "Tag.Plant/Reactor1/Temperature", "Color": "#FF3498DB" },
            { "TagName": "Tag.Plant/Reactor1/ML/AnomalyScore", "Color": "#FFE74C3C" }
          ]
        },
        {
          "Type": "TextBlock",
          "Column": 0, "Row": 1,
          "LinkedValue": "Tag.Plant/Reactor1/ML/AnomalyScore",
          "Label": "Anomaly Score"
        },
        {
          "Type": "TextBlock",
          "Column": 1, "Row": 1,
          "LinkedValue": "Tag.Plant/Reactor1/ML/Confidence",
          "Label": "Confidence"
        },
        {
          "Type": "TextBlock",
          "Column": 0, "Row": 2,
          "LinkedValue": "Tag.Plant/Reactor1/ML/IsAnomaly",
          "Label": "Anomaly Detected"
        },
        {
          "Type": "TextBlock",
          "Column": 1, "Row": 2,
          "LinkedValue": "Tag.Plant/Reactor1/ML/LastPrediction",
          "Label": "Last Prediction"
        }
      ]
    }
  ]
}

Verification

get_objects('ScriptsClasses', names=

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('AnomalyML'

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)) — confirm class exists
get_designer_state() — check for compilation errors (ML.NET references must be resolved)
get_objects('ScriptsExpressions') — confirm expressions are configured
Start runtime → wait 1-2 minutes for the model to train on initial data
browse_namespace('Tag.Plant/Reactor1/ML') — verify ML output tags exist
Check that AnomalyScore and Confidence values are

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updating

Common Pitfalls

Mistake	Why It Happens	How to Avoid

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ML.NET assembly not found

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Missing reference to Microsoft.ML

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Check Scripts → References.

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Add NuGet package or use

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library import

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No output for first 1-2 minutes	Model needs

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~100 data points

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to train	This is expected. The training buffer fills first, then predictions start
Static class state

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lost	`mlContext` and `model` are static

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in-process only

A full runtime restart retrains from scratch.

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This is by design
Wrong alarm threshold on score	`AnomalyScore` is unbounded (not 0-1)

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Only Confidence is 0-1. Don't set

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thresholds on score without understanding

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data range

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High CPU on fast data	OnChange execution runs model every value change

For high-frequency data, consider

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periodic execution to reduce CPU load

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Variations

Variation A: Pre-trained Model from File

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Export model from Visual Studio ML.NET Model Builder
Place .zip file in the solution directory
Modify the script to call mlContext.Model.Load(modelPath) at startup
See skill-mlnet-model-builder or the ML.NET Model Builder documentation

Variation B: Multiple Independent Models

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Create separate classes:

AnomalyML_Temperature, AnomalyML_Pressure, etc.
Each maintains its own training buffer and model
Better isolation but higher memory usage

Variation C: Historian-Fed Training

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Use Dataset.Query to fetch past 24 hours of sensor data
Train model on historical data at startup
Provides better initial model but requires Historian to be configured first

Related Skills

skill-getting-started — Create the base solution with tags and simulator
skill-alarm-pipeline — Configure alarms (used in Step 4)
skill-historian-configuration — Log data for ML training and analysis
skill-cloud-ai-integration — Connect Claude/LLMs via MCP for Runtime (complementary to edge ML)

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In this section...

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Versions Compared

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Key

What This Skill Does

Build a complete edge ML pipeline that runs deterministic machine learning models on-premise using ML.NET. Data flows from sensor tags through an anomaly detection model, and results are written back to tags for alarms

When to Use This Skill

Prerequisites

MCP Tools and Tables Involved

Implementation Steps

Step 1: Create ML Output Tags

Step 2: Import the AnomalyML Script Class from Library

Step 3: Create

Script

Expressions to Trigger the Model

Step 4: Add Alarm on Anomaly Detection

Step 5: Create ML Dashboard

Verification

Common Pitfalls

Variations

Related Skills

In this section...