AWS IoT TwinMaker Tutorial | Digital Twins Introduction & Demo

Summary

This tutorial demonstrates setting up AWS IoT Twin Maker for visualizing motor data in a factory. It guides through creating a motor model in SiteWise, generating and sending data with a Python script, and visualizing it in Twin Maker and Grafana. The process includes defining attributes, creating assets, setting up workspaces, and configuring data sources, culminating in a real-time 3D visual representation of motor status with color-coded speed indicators.

Takeaways

• 🏭 The tutorial is focused on creating a visual representation of motor data in a factory setting using AWS IoT Twin Maker.
• 📈 The primary data of interest for the motors is their speed, which is measured in RPM (Revolutions Per Minute).
• 📝 AWS IoT SiteWise is used to receive and manage the motor data, with a digital twin created in Twin Maker for visualization.
• 🔧 The process begins by creating a 'Motor Model' in SiteWise, defining attributes such as serial number and measurements like speed.
• 📍 An 'Asset' is then created in SiteWise using the defined 'Motor Model', with properties like asset name and measurement aliases.
• 🔄 Data is sent to the asset using a Python script, simulating motor data transmission in a real-world scenario.
• 🛠️ Twin Maker requires the setup of a workspace, including an S3 bucket for storage, roles, and policies for proper access and execution.
• 🌐 The Twin Maker workspace integrates with AWS IoT SiteWise to fetch and visualize the motor's data.
• 📊 Grafana is used for dashboard management, allowing for real-time monitoring and historical data visualization.
• 🎨 A 3D model of the motor is added to Twin Maker to create a visual scene, with annotations and shaders to represent data.
• 📝 Rules are created in Twin Maker to change the visual representation (e.g., color) of the motor based on its operational parameters, such as speed thresholds.

Q & A

What is the main purpose of the AWS IoT Twin Maker tutorial?

The main purpose of the AWS IoT Twin Maker tutorial is to demonstrate how to create a visual representation of current and historical status of motors in a factory, specifically focusing on their speed, using AWS IoT Twin Maker and Grafana for visualization.

What does the production supervisor want to achieve with AWS IoT Twin Maker?

The production supervisor wants to see a visual representation of the current and historical status of the motors on the production floor, particularly their speed, to better monitor and manage the production process.

What is the first step in creating a motor model in AWS IoT SiteWise?

The first step in creating a motor model in AWS IoT SiteWise is to navigate to the 'Models' section and click 'Create model', providing a model name such as 'Motor Model' and defining attributes and measurements like 'Serial Number' and 'Speed' with appropriate units and data types.

How is the motor data sent to AWS IoT SiteWise in the tutorial?

In the tutorial, a Python script named 'send_data.py' is used to generate and send motor data to AWS IoT SiteWise. The script sends randomly generated speed values with timestamps at 1-second intervals.

What is the role of a property alias in AWS IoT SiteWise?

A property alias in AWS IoT SiteWise is used to associate a measurement with a specific asset. In the tutorial, a property alias 'SL_Factory_SL_Motor_One/speed' is created to send data to the 'Motor One' asset.

What is the purpose of creating a workspace in AWS IoT Twin Maker?

Creating a workspace in AWS IoT Twin Maker is necessary to organize and manage the digital twin components, entities, and scenes. It also involves setting up an S3 bucket for data storage and defining roles and policies for permissions.

Why is an S3 bucket needed when creating a workspace in Twin Maker?

An S3 bucket is needed to store the data and assets related to the Twin Maker workspace, ensuring that the 3D models, annotations, and other resources are saved and accessible for the visualization.

How is the 3D model of the motor added to the Twin Maker scene?

The 3D model of the motor is added to the Twin Maker scene by selecting 'Add 3D Model' from the scene interface, choosing the motor model file from the resource library, and placing it within the scene environment.

What is the significance of the 'Factory Rule' created in the Twin Maker scene?

The 'Factory Rule' is significant as it defines the conditions under which the visual representation of the motor changes color based on its speed. For example, if the speed is greater than 400 RPM, the motor section turns red, and if it is less than or equal to 400 RPM, it turns green.

How is Grafana used in conjunction with AWS IoT Twin Maker?

Grafana is used as a visualization tool to create dashboards that display the Twin Maker scene and time series data of the motor's speed. It connects to the Twin Maker data source and allows users to monitor the motor's performance in real-time.

What is the final outcome of the AWS IoT Twin Maker tutorial?

The final outcome of the tutorial is a functional Twin Maker workspace with a 3D model of a motor, annotations for speed display, and a scene that updates based on motor speed. Additionally, a Grafana dashboard is set up to visualize the motor's speed data in real-time.

Outlines

00:00

📚 AWS IoT Twin Maker Tutorial Setup

This paragraph introduces a tutorial on setting up AWS IoT Twin Maker for a factory motor monitoring use case. It explains the creation of a motor model in AWS IoT Sitewise, including attributes and measurements like speed in RPM. The tutorial proceeds with the setup of an asset named 'Motor One' and the configuration of data streaming to AWS Sitewise using a Python script. The script simulates motor data being sent to the cloud at one-second intervals.

05:02

🛠 Configuring AWS IoT Twin Maker Workspace

The second paragraph details the process of creating a workspace in AWS IoT Twin Maker, which includes setting up an S3 bucket named 'factory WS bucket' and defining necessary roles and policies for Twin Maker, Sitewise, and S3 access. A policy named 'Factory policy' is created for permissive permissions, and a role 'Factory role' is established for service access. The workspace creation involves selecting Amazon managed Grafana for dashboard management and associating it with the previously created role.

10:04

🏭 Building the Twin Maker Visual Representation

This section describes the creation of entities and components within the Twin Maker workspace. It involves adding a motor entity and a component that connects to the IoT Sitewise data. The paragraph also covers testing the component to ensure data connectivity and the addition of a 3D model of the motor as a resource. Furthermore, it explains the creation of a scene called 'factory scene' and the integration of the 3D model into the scene, including scaling and annotations for displaying motor speed.

15:06

🎨 Customizing Visual Alerts with Rules and Shaders

The fourth paragraph focuses on customizing the visual representation by adding rules and shaders to the Twin Maker scene. It explains creating a 'motor rule' that changes the color of a motor section based on speed thresholds—red for speeds greater than 400 RPM and green for speeds 400 or below. The paragraph also details the process of applying a model shader to the 3D model to reflect these rules visually.

20:10

📊 Integrating Grafana for Real-time Data Visualization

The final paragraph outlines the integration of Grafana for real-time data visualization. It covers creating a 'factory workspace, Grafana' and setting up a user named 'Joe' with access to this workspace. The tutorial continues with configuring a data source in Grafana to connect with AWS IoT Twin Maker, and creating a dashboard with panels for visualizing the Twin Maker scene and a time series graph of motor speed data. The dashboard updates in real-time as motor data is sent from the Python script, reflecting the color changes on the 3D model based on speed.

Keywords

💡AWS IoT Twin Maker

AWS IoT Twin Maker is a service that allows users to create digital twins of physical assets for monitoring and management purposes. In the context of the video, it is used to create a digital representation of a motor on a factory production floor, providing a visual and data-driven interface for production supervisors to monitor the motor's status. The script describes the process of setting up Twin Maker with a workspace, entities, components, and integrating it with AWS IoT SiteWise and Grafana for visualization.

💡AWS CloudSiteWise

AWS IoT SiteWise is a service that collects, stores, and organizes industrial data from various sources. It is integral to the video's tutorial as it receives motor data and serves as the data source for the digital twin created in Twin Maker. The script mentions creating a model and asset in SiteWise, which are then used to send and visualize the motor's data.

💡Digital Twin

A digital twin is a virtual representation of a physical object or system. In the video, the digital twin is created in Twin Maker to mirror the real-world motor's operational data, such as speed and historical performance. The script details the creation of a 'motor model' and 'motor one asset' as part of setting up the digital twin.

💡Grafana

Grafana is an open-source platform for time-series analytics and visualization. In the video, it is used to create a dashboard that displays the motor's data in real-time, providing a visual representation of the motor's status as monitored by the digital twin in Twin Maker. The script outlines the process of setting up a Grafana workspace and integrating it with Twin Maker.

💡Asset Model

In the context of AWS IoT SiteWise and Twin Maker, an asset model defines the data structure and properties for a type of asset, such as a motor. The script describes creating a 'motor model' with attributes and measurements like 'speed' and 'serial number' that are used to structure the data sent from the motor.

💡Measurement

A measurement in the script refers to a specific data point that is associated with an asset, such as the speed of the motor. It is defined during the creation of the asset model in SiteWise and is crucial for capturing and visualizing the operational data of the motor.

💡Property Alias

A property alias in SiteWise is a unique identifier used to send data to a specific asset. In the script, a property alias is created for the motor's speed measurement, which allows data to be accurately routed and associated with the correct asset within the system.

💡3D Model

A 3D model in the script represents the physical motor in the digital space within Twin Maker. It is used to create a visual scene that corresponds with the motor's operational data, allowing for a more intuitive understanding of the motor's status. The script describes adding a 3D model of the motor to the Twin Maker scene.

💡Scene

In Twin Maker, a scene is a virtual environment where digital twins and their associated data are displayed. The script describes creating a 'factory scene' that includes the 3D model of the motor and visual indicators for the motor's status based on its speed.

💡Annotation

Annotations in the script are used within the Twin Maker scene to display specific data points, such as the current speed of the motor. They provide a way to overlay data directly onto the 3D model, enhancing the visual representation and understanding of the motor's status.

💡Model Shader

A model shader in the script is a component that changes the visual appearance of a 3D model based on certain conditions or data values. It is used to make parts of the motor's 3D model turn green or red depending on the motor's speed, providing a visual indicator of the motor's performance.

💡Rule

Rules in Twin Maker define conditions under which certain actions or visual changes occur, such as changing the color of a model based on its speed. The script describes creating a 'motor rule' that triggers the model shader to change the color of the motor's 3D model in the scene.

Highlights

Introduction of AWS IoT Twin Maker for creating a digital twin of a motor in a factory setting.

Use case scenario where production supervisors require a visual representation of motor status.

Architecture overview involving AWS Cloud, Sitewise, Twin Maker, and Grafana for visualization.

Step-by-step guide on creating a motor model in Sitewise with attributes and measurements.

Asset creation in Sitewise using the predefined motor model.

Adding property aliases to measurements for data streaming to AWS Sitewise.

Utilization of a Python script to simulate motor data generation and transmission to Sitewise.

Data verification in the AWS console to ensure motor data is being received.

Workspace creation in Twin Maker with associated S3 bucket and necessary roles and policies.

Entity creation in Twin Maker representing the physical motor with a component.

Component testing in Twin Maker to validate data source connectivity.

Inclusion of a 3D model of the motor as a resource in Twin Maker.

Scene creation in Twin Maker to visualize motor data with a 3D model.

Annotation addition to the scene for displaying current motor speed.

Rule creation for conditional color changes based on motor speed thresholds.

Integration of model shader for visual representation of motor speed conditions.

Grafana workspace setup for dashboard creation and data visualization.

Configuration of a data source in Grafana to connect with Twin Maker.

Dashboard creation in Grafana with panels for real-time and historical motor speed data.

Real-time data visualization demonstrating motor speed with color-coded conditions.

Tutorial conclusion summarizing the setup and functionality of AWS IoT Twin Maker.

Transcripts

AWS iot twin maker

tutorial our use case is we have a

factory where there are Motors running

on the production floor production

supervisors want to see a visual

representation of current and historical

status of these motors specifically

their

speed and this is what our overall

architecture would look

like for the purpose of tutorial we will

use one motor on AWS Cloud sitewise will

receive motor data digital twin is

created in Twin maker and visualized via

grafana let's begin by creating a motor

model in

sitewise in AWS console under AWS iot

sitewise section select

models and here hit create model provide

a model name

we will call that motor

model add an

attribute a name for that serial

number next we will add a

measurement so that will be

speed and uh unit as RPM and data type

as

double

now create

model we need to provide a default value

for the attribute so let's just call it

default hit that create

model and here we have a new Moto model

right so there you can see the attribute

and measurement

definitions next we will create an asset

in sitewise using the model we just

created select the asset submenu and

then hit create

asset and uh choose the model we just

created which is the motor model and

provide an asset name right so this is

motor

one okay and then hit create

asset so that's our motor one

asset now we will add an allies to the

measurement so for that hit edit

asset come down to the measurement

section and add a property allias of the

form SL

Factory SL motor one/ speed make sure

that mqd notification status is

active hit save now Lis is where we send

data to right so when we want to send

data to an asset

we use the property

allias okay so now we are done with the

asset

Creation in real world the motor would

send its data to AWS sitewise on the

cloud via an iot or Greengrass device in

the factory for our tutorial we will use

a python script to generate and send

motor data to

sitewise hit the cloud shell icon in AWS

console

this will open uh Linux prompt and here

I have already uploaded a send data. py

python script let's take a quick look at

that so this will send uh randomly

generated speed along with timestamp to

sitewise at the top you can see the l/f

factory SL motor one/

speeed now uh let's let's run that file

so python send dat.

py and it should send data with 1 second

intervals right so there you can see uh

double value on the right hand side

that's the speed that is being sent so

let's come to AWS console asset section

in sitewise and under

measurements against speed we can see

the latest

value right

let's focus on Twin Maker Now we will

start by creating a workspace while

doing that we will also create S3 bucket

roles and policies we

need we are in AWS iot twin maker

section hit create

workspace we'll provide a name for the

workspace Factory workspace and and we

need to associate an S3 bucket with that

so let's create an S3

bucket okay provide a bucket name we'll

call

that factory WS

bucket and uh leave everything else as

default hit create

bucket all right now go back to our

workspace creation and U associate this

S3

bucket

all right so that is Factory WS

bucket and for the execution role we

will go to the AWS section first we'll

create a

policy okay let's hit create

policy and uh choose Json and paste this

Json where we provide permissions for

twin maker sitewise and S3 now now this

is a really permissive permission now

for your production workloads make sure

that your permissions are restrictive

and you have only what you need okay now

we'll give that policy a name Factory

policy now we will create a role okay so

choose custom trust policy and uh

provide this trust policy for iot twin

maker and grafana services hit next

okay now choose the policy we had

created

earlier Factory policy hit next provide

a role name we'll call that factory

role okay and hit create role we come

back to the workspace and for execution

role we select the role we just created

Factory

role then hit next and for dashboard

management we select Amazon managed

grafana

and for grafana authentication

provider we again choose the factory

Ro hit

next and for the dashboard policy we

leave that as no video

permissions and for dashboard role we

again select Factory role hit

next review everything and hit create

workspace

so our workspace has been created

successfully now that our twin maker

workspace is ready let's add an entity

to

it choose the workspace and uh here we

are going to create an entity so from

the menu on the left select

entities okay and here hit create

entity and provide in

entity name so that will be motor

one okay and then choose create

entity now it's time to add component to

The

Entity select the entity and hit add

component here we will provide the

component name we will call that motor

and for type you will select iot

sitewise

connector and under asset model we will

select motor

model under asset motor

one and we will leave all the rest of

the properties as they

are and hit add

component so our component has been

created so we have a new motor metrix

component under motor one entity

select the test component option to test

this component to make sure that it is

connected to the right data source and

under test select speed time series

property provide

a Time range and hit run test and you

can see the

data next let's add a resource a

resource is simply a 3D model of the

physical asset so in our case it will be

a 3D model of the motor a 3D model can

be created using a variety of 3D

modeling software and it will be in a

specific file format for example

glb choose the resource Library option

hit add

resources then select choose

file and select your 3D model file it

open okay

so our 3D model file has been can added

as a

resource next we will create a scene and

add our resource or 3D model to it the

scene will be configured to show motor

data from sitewise and update the

display based on

it select scenes on the left hand side

hit create scene provide a scene ID we

will call that factory scene hit create

scene and this is our scene interface

here we can see hierarchy rules and

settings options on the left hand

side and if you hit the plus icon you

will see various submenus like add 3D

model annotations Etc select the add 3D

model option and then add a resource

from the resource Library so here we

select the motor model that we had added

earlier to the resource library and

place it here here on the scene so the

model will be loaded

shortly and uh if the model does not

appear to be of correct scale you can

change that so under transform there is

a scale property and you can change the

values of that so that the model appears

in correct

scale so that's our Moto

model you can move it around to take a

look at it from all all

sides let's explore this a bit and on

the left hand side under hierarchy you

can see that motor and when you expand

that you can see various sub models

there so these are sections of the motor

right so as you move your mouse over

them you can see various parts of the

motor getting highlighted okay and when

you select uh one of

them on the right hand side uh uh you

can see there is an inspector

panel Now using that uh plus icon let's

select the add annotation option

so this will allow us to place an

annotation on the

scene uh we'll provide details for that

in the inspector section so we'll rename

that to speed annotation where we want

to display the current speed of the

motor right and in the markdown

section we just write speed colon and

provider markdown dollar with curly

brackets and then hit add binding

option and here

select entity

ID component name and property

speed

right so let's change the binding name

we'll call that speed and update the

markdown section as

shown next we'll add a rule so under the

rule section on the left hand side let's

create a rule called motor

rule okay and uh expand that and add

statements under it so we provide an

expression something like

speed greater than say

400 okay at that point in time the

target color should

turn this particular color red okay and

uh when the speed is less than or equal

to 400 then uh the color should

be uh let's say green

okay right so when speed is greater than

400 the color is red and when the speed

is less than or equal to 400 the color

is green and we will see how to use

this now let's go to uh hierarchy and uh

expand that motor model and under that

let's select a section which we want to

highlight in different colors right so

we'll select that uh Z exag circular

object right so we will turn that into

red or green depending on the speed of

the

motor okay so with that selected we will

add a model Shader right so which will

appear under the inspector

section select the entity

ID select component

name property s speed and in addition

select the rule we just created motor

Rule now our scene is ready overall we

added a 3D model to the scene and we

added a model Shader to one of its

submodels in addition we have added a

annotation to the scene and there are

many other options that you could play

around

with now we can move on to grafana which

is a visualization tool let's begin by

creating a workspace in

grafana in ews console we are in the

Amazon manage grafana section and here

let's it create

workspace and provide a workspace name

we will call

that factory workspace

grafana okay scroll down hit

next okay choose an authentication

method we'll choose AWS IM identity

Center and uh for permission type

customer managed

and we will select

an role so this is the same role that we

created earlier Factory

role

okay and hit

next all right then hit

next okay review all the settings and

then hit create

workspace so our workspace is being

created this takes some

time and here our workspace is ready

Factory workspace

grafana in the IM identity Center under

users let's add a user so this is the

user who will be able to login into

grafana so we will associate this user

with grafana workspace so we'll call

this username

Joe and uh generate a password for that

hit next okay we need also need to

provide an email ID so so let's provide

an email

ID

okay confirm

that provide a first

name and provide the last

name and now we are ready

to hit

next all right hit

next next review all the

details add

user so not down the pass password that

has been

autogenerated in grafana workspace under

authentication assign new

user select the user Joe assign users

and

groups okay now make this user an

admin now go back to the grafana

worksspace and here hit the grafana

workspace URL this will will open a new

window and sign in

here okay provide

username

next provide

password sign

in now this prompts us to um set new

password since the previous password was

autogenerated so let's do

that okay set new

password

and now we are successfully logged in

right so this is

the grafana interface presented to

us next we will configure a data

source select the data sources option to

add a data

source and here filter by twin

maker and select that AWS iot twin

maker so this takes us to a setting

screen where we have to take a look at

the connection details so provide uh the

factory role Arn this is the role we

created earlier so provide Arn for that

and under workspace choose Factory

workspace this is the twin maker

workspace hit save and test and there

you can see twin maker data source

successfully

configured now we can create a dashboard

and panels one for the twin maker scene

and another a Time series

graph now go back to the home screen and

hit

dashboards select add new

panel and for this panel on the right

hand

side select AWS

iot twin maker scene

viewer okay now let's change the title

of this panel

we will call

it Factory Motor

panel

okay let's scroll down and under scene

select the factory scene this is the

same scene that we had created in Twin

maker so this scene should load here now

okay so that scene has loaded here in

the

UI okay now scroll down slightly and

here under data source make sure that uh

and it is AWS it twin maker for query

type select get property value history

by entity select the entity motor one

then select component motor metric and

uh selected properties select speed so

when you do that you can see that in the

panel uh speed shows

up top right hit the save option provide

a name for the dashboard we will call it

Factory dashboard okay hit

save so that's a dashboard with one

panel Factory Motor

panel for this panel select the view

option now we need to send uh data to

this particular asset so let's do that

we'll go back to our Cloud shell and run

our program python send data so this

again start sending data at 1 second

intervals randomly generated

data and here on the screen let's put

the refresh interval to 5

seconds so here you will see that a

section of the motor turns green the

zigzag uh motor part it turns green or

red depending on the speed so whenever

the speed is less than 400 it is green

and whenever it is greater than 400 it

is red

red this is working on expected

lines back to the dashboard let's select

add a new

panel and make sure that it is of type

time series and under data source it is

AWS iot twin maker query type is get

property value history by entity and

entity is motor one and

component motor

metrics and selected

properties Let's uh select speed

there and here we can see the

graph and uh we can also change the time

range so for last 30

minutes this is what the graph looks

like let's change that again

to 15

minutes and

uh how about 5

minutes to see the details in a more

granular

fashion uh in addition we can change

the view to table view right to

see raw data right as it comes in

so this is working on expected lines

let's save the

dashboard hit

save and that's our time series panel so

we will change the title to speed time

series

panel right and hit

apply back to the factory dashboard here

we can see our two panels now speed time

series panel and Factory Motor panel in

the first panel you can see the speed

data as it comes in in real time and in

the second panel you can see the motor

with a part of it turning green or red

depending on the

speed so with that we come to the end of

our AWS iot twin maker

tutorial