Create a computation model (AWS CLI)ExecuteAction API payload preparation Train the model (AWS CLI)Start and stop retraining the model (AWS CLI)Start and stop inference (AWS CLI)

Enable anomaly detection on sensors across assets

Create a computation model (AWS CLI)

To create a computation model, use the AWS Command Line Interface (AWS CLI). After you define the computation model, train the model and schedule inference to do anomaly detection across assets in AWS IoT SiteWise.

The following steps explain this process:

To set up anomaly detection, use the UpdateAssetModel (AWS CLI), and meet the following requirements:
1. At least one input property that is of either DOUBLE or INTEGER data type. It is either a measurement or transform property, and is used to train the model.
2. A result property of STRING data type. It must be a measurement property, and stores the anomaly detection results.

Create a file anomaly-detection-computation-model-payload.json with the following content:

Note

Create a computation model by directly providing assetProperty as the data source.


{
    "computationModelName": "name of ComputationModel",
    "computationModelConfiguration": {
        "anomalyDetection": {
            "inputProperties": "${properties}",
            "resultProperty": "${p3}"
        }
    },
    "computationModelDataBinding": {
        "properties": {
            "list": [
                {
                    "assetProperty": { 
                        "assetId": "asset-id",
                        "propertyId": "input-property-id-1"
                    }
                },
                {
                    "assetProperty": { 
                        "assetId": "asset-id",
                        "propertyId": "input-property-id-2"
                    }
                }
            ]
        },
        "p3": {
            "assetProperty": { 
                "assetId": "asset-id",
                "propertyId": "results-property-id"
            }
        }
    }
}

Run the following command to create a computation model:


aws iotsitewise create-computation-model \
    --cli-input-json file://anomaly-detection-computation-model-payload.json

ExecuteAction API payload preparation

The next steps to execute training and inference is performed with the ExecuteAction API. Both training and inference are configured with a JSON action payload configuration. When invoking the ExecuteAction API, the action payload must be provided as a value with a stringValue payload.

The payload must strictly adhere to the API requirements. Specifically, the value must be a flat string with no control characters (for example, newlines, tabs, or carriage returns). The following options provides two reliable ways to supply a valid action-payload.

Option 1: Use a clean payload file

The following procedure describes the steps for a clean payload file:

Clean the file to remove control characters.


tr -d '\n\r\t' < original-action-payload.json > training-or-inference-action-payload.json

Execute the action with the file @=file://....


aws iotsitewise execute-action \
    --target-resource computationModelId=<MODEL_ID> \
    --action-definition-id <ACTION_DEFINITION_ID> \
    --action-payload stringValue@=file://training-or-inference-action-payload.json

Option 2: Inline string with escaped quotes

The following steps describes the steps to supply the payload inline, and avoid intermediary files:

Use escaped double quotes (\") inside the JSON string.
Wrap the entire StringValue=.. expression within double quotes.

Example of an escaped action payload:


aws iotsitewise execute-action \
    --target-resource computationModelId=<MODEL_ID> \
    --action-definition-id <ACTION_DEFINITION_ID> \
    --action-payload "stringValue={\"exportDataStartTime\":1717225200,\"exportDataEndTime\":1722789360,\"targetSamplingRate\":\"PT1M\"}"

Train the model (AWS CLI)

Run the following command to find the actionDefinitionId of the AWS/ANOMALY_DETECTION_TRAINING action. Replace computation-model-id with the ID returned in the previous step.
```
aws iotsitewise describe-computation-model \
    --computation-model-id computation-model-id
```
Create a file called anomaly-detection-training-payload.json and add the following values:

Note
The payload must conform to Option 1: Use a clean payload file.
1. StartTime with the start of the training data, provided in epoch seconds.
2. EndTime with the end of the training data, provided in epoch seconds.
3. You can optionally configure Advanced inference configurations.
  1. (Optional) TargetSamplingRate with the sampling rate of the data.
  2. (Optional) LabelInputConfiguration to specify time periods when anomalous behavior occurred for improved model training.
  3. (Optional) ModelEvaluationConfiguration to evaluate model performance by running inference on a specified time range after training completes.
```
{
  "exportDataStartTime": StartTime,
  "exportDataEndTime": EndTime
}
```
Example of a training payload example:
```
{
  "exportDataStartTime": 1717225200,
  "exportDataEndTime": 1722789360
}
```

Run the following command to start training (without providing asset as a target resource). Replace the following parameters in the command:


aws iotsitewise execute-action \
    --target-resource computationModelId=computation-model-id \
    --action-definition-id training-action-definition-id \
    --action-payload stringValue@=file://anomaly-detection-training-payload.json

Run the following command to check for status of the model training process. The latest execution summary shows the execution status (RUNNING/COMPLETED/FAILED).
```
aws iotsitewise list-executions \
    --target-resource-type COMPUTATION_MODEL \
    --target-resource-id computation-model-id
```
Run the following command to check the configuration of the latest trained model. This command produces an output only if at least one model has completed training successfully.
```
aws iotsitewise describe-computation-model-execution-summary \
    --computation-model-id computation-model-id
```

Start and stop retraining the model (AWS CLI)

After initial model training, you can configure automatic retraining to address data drift and maintain model accuracy over time. The retraining scheduler allows you to set up periodic model updates with configurable promotion modes.

Start retraining scheduler

Prepare the same payload as mentioned in Start retraining scheduler.
Execute training action (without providing asset as target resource). Replace the following parameters in the command:
1. computation-model-id with the ID of the target computation model.
2. training-action-definition-id with the ID of the AWS/ANOMALY_DETECTION_TRAINING action.
```
aws iotsitewise execute-action \
    --target-resource computationModelId=computation-model-id \
    --action-definition-id training-action-definition-id \
    --action-payload stringValue@=file://anomaly-detection-start-retraining-payload.json
```
Run the following command to check for status of start retraining scheduler process. The latest execution summary shows the execution status (RUNNING/COMPLETED/FAILED).
```
aws iotsitewise list-executions \
    --target-resource-type COMPUTATION_MODEL \
    --target-resource-id computation-model-id
```

Stop retraining scheduler

Prepare the same payload as mentioned in Stop retraining scheduler.
Execute training action (without providing asset as target resource). Replace the following parameters in the command:
1. computation-model-id with the ID of the target computation model.
2. training-action-definition-id with the ID of the AWS/ANOMALY_DETECTION_TRAINING action.
```
aws iotsitewise execute-action \
    --target-resource computationModelId=computation-model-id \
    --action-definition-id training-action-definition-id \
    --action-payload stringValue@=file://anomaly-detection-stop-retraining-payload.json
```
Run the following command to check for status of stop retraining scheduler process. The latest execution summary shows the execution status (RUNNING/COMPLETED/FAILED).
```
aws iotsitewise list-executions \
    --target-resource-type COMPUTATION_MODEL \
    --target-resource-id computation-model-id
```

Start and stop inference (AWS CLI)

After training the model, start the inference, which instructs AWS IoT SiteWise to begin monitoring your industrial assets for anomalies.

Start inference

Run the following command to find the actionDefinitionId of the AWS/ANOMALY_DETECTION_INFERENCE action. Replace computation-model-id with the actual ID of computation model created earlier.
```
aws iotsitewise describe-computation-model \
    --computation-model-id computation-model-id
```
Create a file anomaly-detection-start-inference-payload.json and add the following code. Replace the following parameters as described:

Note
The payload must conform to Option 1: Use a clean payload file.
1. DataUploadFrequency: Configure the frequency at which the inference schedule runs to perform anomaly detection. Allowed values are: PT5M, PT10M, PT15M, PT30M, PT1H, PT2H..PT12H, PT1D.
```
"inferenceMode": "START",
"dataUploadFrequency": "DataUploadFrequency"
```
2. (Optional) DataDelayOffsetInMinutes with the delay offset in minutes. Set this value between 0 and 60 minutes.
3. (Optional) TargetModelVersion with the model version to activate.
4. (Optional) Configure the weeklyOperatingWindow with a shift configuration.
5. You can optionally configure Advanced inference configurations.
Run the following command to start inference. Replace the following parameters in the payload file.
1. computation-model-id with the ID of the target computation model.
2. inference-action-definition-id with the ID of the AWS/ANOMALY_DETECTION_INFERENCE action from Step 1.
```
aws iotsitewise execute-action \
    --target-resource computationModelId=computation-model-id \
    --action-definition-id inference-action-definition-id \
    --action-payload stringValue@=file://anomaly-detection-inference-payload.json 
```
Run the following command to check if inference is still running. The inferenceTimerActive field is set to TRUE when inference is active.
```
aws iotsitewise describe-computation-model-execution-summary \
    --computation-model-id computation-model-id
```

The following command lists all the inference executions:


aws iotsitewise list-executions \
    --target-resource-type COMPUTATION_MODEL \
    --target-resource-id computation-model-id

Run the following command to describe an individual execution. Replace execution-id with the id from previous Step 5.
```
aws iotsitewise describe-execution \
    --execution-id execution-id
```

Stop inference

Run the following command to find the actionDefinitionId of the AWS/ANOMALY_DETECTION_INFERENCE action. Replace computation-model-id with the actual ID of computation model created earlier.
```
aws iotsitewise describe-computation-model \
    --computation-model-id computation-model-id
```
Create a file anomaly-detection-stop-inference-payload.json and add the following code.
```
{
    "inferenceMode": "STOP"
}
```
Note
The payload must conform to Option 1: Use a clean payload file.
Run the following command to stop inference. Replace the following parameter in the payload file:
1. computation-model-id with the ID of the target computation model.
2. inference-action-definition-id with the ID of the AWS/ANOMALY_DETECTION_INFERENCE action from Step 1.
Example of the stop inference command:
```
aws iotsitewise execute-action \
--target-resource computationModelId=computation-model-id \
--action-definition-id inference-action-definition-id \
--action-payload stringValue@=file://anomaly-detection-stop-inference-payload.json 
```

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Document Conventions

Enable anomaly detection on sensors of an asset

Advanced training configurations

Enable anomaly detection on sensors across assets

Create a computation model (AWS CLI)

Note

ExecuteAction API payload preparation

Option 1: Use a clean payload file

Option 2: Inline string with escaped quotes

Example of an escaped action payload:

Train the model (AWS CLI)

Note

Example of a training payload example:

Start and stop retraining the model (AWS CLI)

Start retraining scheduler

Stop retraining scheduler

Start and stop inference (AWS CLI)

Start inference

Note

Stop inference

Note

Example of the stop inference command: