Amazon Monitron is no longer open to new customers. Existing customers can continue to use the service as normal. For capabilities similar to Amazon Monitron, see our [blog post](https://aws.amazon.com/blogs/machine-learning/maintain-access-and-consider-alternatives-for-amazon-monitron).

# Understanding the v1 data export schema


**Note**  
Amazon Monitron Kinesis data export schema v1 has been deprecated. Learn more about the [v2 data export schema](monitron-kinesis-export-v2.md). 

Each measurement data and its corresponding inference result are exported as one Kinesis data stream record in JSON format.

**Topics**
+ [

## v1 schema format
](#data-export-schema-format)
+ [

## v1 schema parameters
](#data-export-schema-parameters)

## v1 schema format


```
{
    "timestamp": "string",
    "eventId": "string",
    "version": "string",
    "projectDisplayName": "string",
    "siteDisplayName": "string",
    "assetDisplayName": "string",
    "sensorPositionDisplayName": "string",
    "sensor": {
        "physicalId": "string",
        "rssi": number
    },
    "gateway": {
        "physicalId": "string"
    },
    "measurement": {
        "features": {
            "acceleration": {
                "band0To6000Hz": {
                    "xAxis": {
                        "rms": number
                    },
                    "yAxis": {
                        "rms": number
                    },
                    "zAxis": {
                        "rms": number
                    }
                },
                "band10To1000Hz": {
                    "resultantVector": {
                        "absMax": number,
                        "absMin": number,
                        "crestFactor": number,
                        "rms": number
                    },
                    "xAxis": {
                        "rms": number
                    },
                    "yAxis": {
                        "rms": number
                    },
                    "zAxis": {
                        "rms": number
                    }
                }
            },
            "temperature": number,
            "velocity": {
                "band10To1000Hz": {
                    "resultantVector": {
                        "absMax": number,
                        "absMin": number,
                        "crestFactor": number,
                        "rms": number
                    },
                    "xAxis": {
                        "rms": number
                    },
                    "yAxis": {
                        "rms": number
                    },
                    "zAxis": {
                        "rms": number
                    }
                }
            }
        },
        "sequenceNo": number
    },
    "models": {
        "temperatureML": {
            "persistentClassificationOutput": "string",
            "pointwiseClassificationOutput": "string"
        },
        "vibrationISO": {
            "isoClass": "string",
            "mutedThreshold": "string",
            "persistentClassificationOutput": "string",
            "pointwiseClassificationOutput": "string"
        },
        "vibrationML": {
            "persistentClassificationOutput": "string",
            "pointwiseClassificationOutput": "string"
        }
    },
    "assetState": {
        "newState": "string",
        "previousState": "string"
    }
}
```

## v1 schema parameters


timestamp  
+ The timestamp when the measurement is received by Monitron service in UTC
+ Type: String
+ Pattern: yyyy-mm-dd hh:mm:ss.SSS

eventId  
+ The unique data export event ID assigned for each measurement. Can be used to deduplicate the Kinesis stream records received.
+ Type: String

version  
+ Schema version
+ Type: String
+ Current Value: 1.0

projectDisplayName  
+ The project name displayed in the App and console
+ Type: String

siteDisplayName  
+ The site name displayed in the App
+ Type: String

assetDisplayName  
+ The asset name displayed in the App
+ Type: String

sensorPositionDisplayName  
+ The sensor position name displayed in the App
+ Type: String

sensor.physicalId  
+ The physical ID of the sensor from which the measurement is sent
+ Type: String

sensor.rssi  
+ The sensor bluetooth received signal strength indicator value
+ Type: Number
+ Unit: dBm

gateway.physicalId  
+ The physical ID of the gateway used to transmit data to Amazon Monitron service
+ Type: String

measurement.features.acceleration.band0To6000Hz.xAxis.rms  
+ The root mean square of the acceleration observed in the frequency band 0–6000 Hz in the x axis
+ Type: Number
+ Unit: m/s^2

measurement.features.acceleration.band0To6000Hz.yAxis.rms  
+ The root mean square of the acceleration observed in the frequency band 0–6000 Hz in the y axis
+ Type: Number
+ Unit: m/s^2

measurement.features.acceleration.band0To6000Hz.zAxis.rms  
+ The root mean square of the acceleration observed in the frequency band 0–6000 Hz in the y axis
+ Type: Number
+ Unit: m/s^2

measurement.features.acceleration.band10To1000Hz.resultantVector.absMax  
+ The absolute maximum acceleration observed in the frequency band 10–1000 Hz
+ Type: Number
+ Unit: m/s^2

measurement.features.acceleration.band10To1000Hz.resultantVector.absMin  
+ The absolute minimum acceleration observed in the frequency band 10–1000 Hz
+ Type: Number
+ Unit: m/s^2

measurement.features.acceleration.band10To1000Hz.resultantVector.crestFactor  
+ The acceleration crest factor observed in the frequency band 10–1000 Hz
+ Type: Number

measurement.features.acceleration.band10To1000Hz.resultantVector.rms  
+ The root mean square of the acceleration observed in the frequency band 10–1000 Hz
+ Type: Number
+ m/s^2

measurement.features.acceleration.band10To1000Hz.xAxis.rms  
+ The root mean square of the acceleration observed in the frequency band 10–1000 Hz in the x axis
+ Type: Number
+ m/s^2

measurement.features.acceleration.band10To1000Hz.yAxis.rms  
+ The root mean square of the acceleration observed in the frequency band 10–1000 Hz in the y axis
+ Type: Number
+ m/s^2

measurement.features.acceleration.band10To1000Hz.zAxis.rms  
+ The root mean square of the acceleration observed in the frequency band 10–1000 Hz in the z axis
+ Type: Number
+ m/s^2

measurement.features.temperature  
+ The temperature observed
+ Type: Number
+ °C/degC

measurement.features.velocity.band10To1000Hz.resultantVector.absMax  
+ The absolute maximum velocity observed in the frequency band 10–1000 Hz
+ Type: Number
+ mm/s

measurement.features.velocity.band10To1000Hz.resultantVector.absMin  
+ The absolute minimum velocity observed in the frequency band 10–1000 Hz
+ Type: Number
+ mm/s

measurement.features.velocity.band10To1000Hz.resultantVector.crestFactor  
+ The velocity crest factor observed in the frequency band 10–1000 Hz
+ Type: Number

measurement.features.velocity.band10To1000Hz.resultantVector.rms  
+ The root mean square of the velocity observed in the frequency band 10–1000 Hz
+ Type: Number
+ mm/s

measurement.features.velocity.band10To1000Hz.xAxis.rms  
+ The root mean square of the velocity observed in the frequency band 10–1000 Hz in the x axis
+ Type: Number
+ mm/s

measurement.features.velocity.band10To1000Hz.yAxis.rms  
+ The root mean square of the velocity observed in the frequency band 10–1000 Hz in the y axis
+ Type: Number
+ mm/s

measurement.features.velocity.band10To1000Hz.zAxis.rms  
+ The root mean square of the velocity observed in the frequency band 10–1000 Hz in the z axis
+ Type: Number
+ mm/s

measurement.sequenceNo  
+ The measurement sequence number
+ Type: Number

models.temperatureML.persistentClassificationOutput  
+ The persistent classification output from the machine learning based temperature model
+ Type: Number
+ Valid Values: `UNKNOWN | HEALTHY | WARNING | ALARM`

models.temperatureML.pointwiseClassificationOutput  
+ The point–wise classification output from the machine learning based temperature model
+ Type: String
+ Valid Values: `UNKNOWN | INITIALIZING | HEALTHY | WARNING | ALARM`

models.vibrationISO.isoClass  
+ The ISO 20816 class (a standard for measurement and evaluation of machine vibration) used by the ISO based vibration model
+ Type: String
+ Valid Values: `CLASS1 | CLASS2 | CLASS3 | CLASS4 | FAN_BV2`

models.vibrationISO.mutedThreshold  
+ The threshold to mute the notification from the ISO based vibration model
+ Type: String
+ Valid Values: `WARNING | ALARM`

models.vibrationISO.persistentClassificationOutput  
+ The persistent classification output from the ISO based vibration model
+ Type: String
+ Valid Values: `UNKNOWN | HEALTHY | WARNING | ALARM`

models.vibrationISO.pointwiseClassificationOutput  
+ The point–wise classification output from the the ISO based vibration model
+ Type: String
+ Valid Values: `UNKNOWN | HEALTHY | WARNING | ALARM | MUTED_WARNING | MUTED_ALARM`

models.vibrationML.persistentClassificationOutput  
+ The persistent classification output from the machine learning based vibration model
+ Type: String
+ Valid Values: `UNKNOWN | HEALTHY | WARNING | ALARM`

models.vibrationML.pointwiseClassificationOutput  
+ The point–wise classification output from the machine learning based vibration model
+ Type: String
+ Valid Values: `UNKNOWN | INITIALIZING | HEALTHY | WARNING | ALARM`

assetState.newState  
+ The machine status after processing the measurement
+ Type: String
+ Valid Values: `UNKNOWN | HEALTHY | NEEDS_MAINTENANCE | WARNING | ALARM`

assetState.previousState  
+ The machine status before processing the measurement
+ Type: String
+ Valid Values: `UNKNOWN | HEALTHY | NEEDS_MAINTENANCE | WARNING | ALARM`