Calculating Total Gear Effectiveness (OEE) with AWS IoT SiteWise


Introduction

This weblog submit represents the second entry in a sequence on utilizing Total Gear Effectiveness (OEE) with AWS IoT SiteWise. On this submit, we’ll do a deep dive on tips on how to calculate OEE utilizing AWS IoT SiteWise native capabilities to gather, retailer, rework, and show calculations as an end-to-end resolution. We are going to take a look at a Baggage Dealing with System (BHS) situated at an airport as a use case as an instance the method.  Please, first learn half 1 of this sequence, Industrial Total Gear Effectiveness (OEE) information with AWS IoT SiteWise, for extra context on the use case.

Moreover, we’ll present how one can automate OEE parts to streamline the implementation of this resolution in lots of different use instances, like manufacturing manufacturing strains in pharmaceutical, meals and beverage industries. That can assist you put into follow the ideas offered on this weblog, we additionally present a code repository that permits you to stream artificial knowledge to AWS IoT SiteWise to create an OEE dashboard utilizing the calculations offered right here.

Use case

Earlier than diving deep into the OEE calculations, let’s outline the instance we will probably be utilizing as a body of reference. Our instance is a BHS and the required knowledge factors for an OEE calculation are gathered from the put in {hardware} on the BHS within the carousel.  The {hardware} consists of 4 sensors: two vibration sensors for motor monitoring, one pace sensor for conveyor surveillance, and one photograph electrical sensor counting the luggage throughput.

The structure of the answer is as follows:

Data Ingestion Reference Architecture
Sensor knowledge is collected and formatted by way of CloudRail, an AWS Companion whose resolution tremendously simplifies the gathering and streaming of IIoT knowledge to AWS IoT SiteWise. This integration is configurable by way of the CloudRail administration Portal instantly.  The structure consists of extra elements for making the sensor knowledge out there to different AWS companies by way of an S3 bucket.

AWS IoT SiteWise pre-requisites

Earlier than sending knowledge to AWS IoT SiteWise, you should create a mannequin and outline its properties.  As talked about earlier, we’ve got 4 sensors that will probably be grouped into one mannequin, with the next measurements (knowledge streams from tools):

Mannequin:Carousel

Asset Identify: CarouselAsset

Property {

Measurement: Photograph.Distance

Measurement: Velocity.PDV1

Measurement: VibrationL.Temperature

Measurement: VibrationR.Temperature

}

Along with the measurements, we’ll add just a few attributes (static knowledge) to the asset mannequin. The attributes characterize completely different values that we want within the OEE calculations.

Mannequin:Carousel

Asset Identify: CarouselAsset

Property {

Attribute: SerialNumber

Attribute: Photograph.distanceBase

Attribute: Photograph.distanceThold

Attribute: Velocity.max_speed_alarm

Attribute: Velocity.min_speed_alarm

Attribute: Vibration.max_temp_c_alarm

Attribute: Ideal_Run_Rate_5_min

}

Now, let’s go to the and create the Carousel mannequin and asset that characterize the airport BHS.

Open the navigation menu on the left, select Construct, Fashions, after which select Create Mannequin to outline the attributes and measurements for this mannequin:

IoT SiteWise Model creation

For extra data on creating asset fashions go to the documentation.

Calculating OEE

Let’s check out the OEE definition and its elements.

The usual OEE components is:

Element Components
Availability Run_time/(Run_time + Down_time)
High quality Successes / (Successes + Failures)
Efficiency ((Successes + Failures) / Run_Time) / Ideal_Run_Rate
OEE Availability * High quality * Efficiency

Let’s take a look at the parameter definition for the BHS. For a full description of OEE parameters please go to the documentation.

  • Ideal_Run_Rate:  In our case, the perfect run fee is 300 luggage/hour, which is equal to 0.83333 luggage/second. This worth is determined by the system and needs to be obtained from the producer or based mostly on subject commentary efficiency.

Availability

Availability = Run_time/(Run_time + Down_time)

We’ve got 4 sensors on the BHS and we have to outline what measurements (temperature, vibration, and so on.) from the sensors we need to embody within the calculation.  The temperature coming from the 2 vibration sensors (in Celsius) and the pace of the carousel coming from the pace sensor (m/s) will dictate the provision state.

The appropriate values for proper operation are based mostly on the next attributes of the Asset Mannequin.

Vibration.max_temp_c_alarm = 50

Velocity.min_speed_alarm = 28

Velocity.max_speed_alarm = 32

Let’s outline Equipment_State, an information rework that gives the present state of the BHS in numerical code:
1024 – The machine is idle
1020 – A fault, like an irregular operation of the system, excessive temperature or a pace worth not throughout the regular vary outlined
1000 – A deliberate cease
1111 – A traditional operation

The idle state of the BHS is just not outlined on this simplified use case, nonetheless, it’s potential to combine different knowledge streams into AWS IoT SiteWise and register data coming from Programmable Logic Controllers (PLCs) or different techniques the place a human operator dictates if the system is idle or not.

So as to add a rework, go to the mannequin on the AWS IoT SiteWise console and select Edit. Scroll to the rework definitions and supply a Identify, Information sort (Double) and enter the next components on the respective subject:

Equipment_state =

if((Velocity.PDV1>Velocity.max_speed_alarm) or (Velocity.PDV1<Velocity.min_speed_alarm) or (VibrationL.Temperature>Vibration.max_temp_c_alarm) or (VibrationR.temperature>Vibration.max_temp_c_alarm),1020).elif(eq(Velocity.PDV1,0),1000,1111)

Equipment state transform definition for OEE calculation

The components ought to seem like this as you enter it within the console. The UI will convey ideas so that you can choose attributes and measurements already outlined within the mannequin to construct the components.

Complete formula for equipment state

As soon as Equipment_State is outlined, create the next derived transforms to seize the completely different states of the BHS. Transforms can reference different transforms.

Defining other transforms for OEE calculation

Proceed to outline the next metrics to combination machine knowledge over time. Preserve the identical interval for every metric.

Fault_Time = statetime(Fault) – The machine’s complete fault time (in seconds)

Stop_Time = statetime(Cease) – The machine’s complete deliberate cease time (in seconds)

Run_Time = statetime(Working) – The machine’s complete time (in seconds) working with out challenge.

Down_Time = Idle_Time + Fault_Time + Stop_Time – The machine’s complete downtime

The metric definitions of the mannequin ought to seem like this:

Metric Definitions - OEE Availability

High quality

High quality = Successes / (Successes + Failures)

Right here, we have to outline what constitutes successful and a failure. On this case our unit of manufacturing is a counted bag, so how will we outline when a bag is counted efficiently and when it isn’t?  we use the measurements and knowledge which can be out there from the BHS’s 4 sensors.

The luggage are counted by wanting on the distance the photograph electrical sensor is offering, due to this fact when there may be an object passing the band, the sensor will report a distance that’s much less that the “base” distance. It is a easy option to calculate the baggage passing, however on the identical time it’s vulnerable to a number of circumstances that may affect the accuracy of the measurement.

We use these mannequin attributes on the standard calculation:

Photograph.distanceBase = 108

Photograph.distanceThold = 0.1

The Photograph.distanceBase is the gap reported by the sensor, when there are not any objects in entrance of it. This worth would possibly should be calibrated regularly and adjusted, elements like vibration and misalignment can result in false constructive counts.
Photograph.distanceThold is used for outlining a threshold for the way delicate is the sensor, with a view to keep away from counting particles or small objects (like bag attachments or belts) as a daily bag.

We then set up two transforms for bag depend:

Bag_Count = if(Photograph.Distance < Photograph.distanceBase,1,0)

Dubious_Bag_Count = if((gt(Photograph.Distance,Photograph.distanceBase*(1-Photograph.distanceThold)) and lt(Photograph.Distance,Photograph.distanceBase*0.95)) or (Velocity.PDV1>Velocity.max_speed_alarm) or (Photograph.Distance>Photograph.distanceBase),1,0)

Formula for dubious bag count

Bag_count will account for all luggage passing in entrance of the photograph electrical sensor, and Dubious_Bag_Count will depend the objects detected as luggage underneath two irregular circumstances:

  1. The space detected is throughout the vary of 95% and 90% of the bottom distance; accounting for small objects and really small variations within the measurements, indications of adjustments on account of vibration or a sensor not correctly hooked up.
  2. Luggage counted when the pace of the carousel is above the restrict outlined; underneath this situation the sensor can miss counting luggage which can be too shut collectively on the carousel.

NOTE: the above circumstances are easy guidelines and the precise values for distance base and thresholds should be reviewed and analyzed with subject knowledge for higher outcomes.

Let’s outline successes and failures as metrics:

Successes = sum(Bag_Count) – sum(Dubious_Bag_Count)

Failures = sum(Dubious_Bag_Count)

Lastly we are able to outline OEE Availability as a metric as properly:

High quality = Successes / (Successes + Failures)

Keep in mind to make use of the identical metric interval as in all different metric definitions.

Efficiency

Efficiency = ((Successes + Failures) / Run_Time) / Ideal_Run_Rate

We’ve got Successes and Failures from our High quality calculation, in addition to Run_Time from Availability. Subsequently, we simply want to make use of the Ideal_Run_Rate_5_min, which in our system is 300 luggage/hour = 0.0833333 luggage/second.

OEE Worth

Having Availability, High quality, and Efficiency we proceed to outline our final metric for OEE.

OEE = Availability * High quality * Efficiency

Simplify transforms and metric definitions

In its place, the OEE elements outlined as transforms and metrics may be outlined programmatically as an alternative of utilizing the AWS Console. That is notably helpful when there are complicated formulation that contain a number of variables, just like the Equipment_State and Dubious_Bag_Count transforms; additionally, automated options are much less error inclined than handbook ones and may be configured constantly throughout a number of environments. Let’s check out how we are able to do it utilizing the AWS SDK for Python (Boto3).

First, determine the measurements and attributes property IDs that you may be referencing on the rework/metric calculation, in addition to the mannequin ID.

Carousel Model Property IDs

Then outline a JSON for the metric/rework. As an illustration, to create a brand new rework to calculate the Equipment_State of the BHS we want the next attributes:

Vibration.max_temp_c_alarm

Velocity.max_speed_alarm

Velocity.min_speed_alarm

And the next measurements:

VibrationL.Temperature

VibrationR.Temperature

Velocity.PDV1

Create a file following this construction. Keep in mind to interchange the propertyIds and reserve it as equipment_state.json:

{
     "title": "Equipment_State",
     "dataType": "DOUBLE",
     "sort": {
       "rework": {
         "expression": "if((var_speedpdv1>var_speedmax_speed_alarm) or (var_speedpdv1<var_speedmin_speed_alarm) or (var_vibrationltemperature>var_vibrationmax_temp_c_alarm) or (var_vibrationrtemperature>var_vibrationmax_temp_c_alarm),1020).elif(eq(var_speedpdv1,0),1000,1111)",
         "variables": [
           {
             "name": "var_vibrationrtemperature",
             "value": {
               "propertyId": "b9554855-b50f-4b56-a5f2-572fbd1a8967"
             }
           },
           {
             "name": "var_vibrationltemperature",
             "value": {
               "propertyId": "e3f1c4e0-a05c-4652-b640-7e3402e8d6a1"
             }
           },
           {
             "name": "var_vibrationmax_temp_c_alarm",
             "value": {
               "propertyId": "f54e16fd-dd9f-46b4-b8b2-c411cdef79a2"
             }
           },
           {
             "name": "var_speedpdv1",
             "value": {
               "propertyId": "d17d07c7-442d-4897-911b-4b267519ae3d"
             }
           },
           {
             "name": "var_speedmin_speed_alarm",
             "value": {
               "propertyId": "7a927051-a569-41c0-974f-7b7290d7e73c"
             }
           },
           {
             "name": "var_speedmax_speed_alarm",
             "value": {
               "propertyId": "0897a3b4-1c52-4e80-80fc-0a632e09da7e"
             }
           }
         ]
       }
     }
}

The primary expression is as follows:

if((var_speedpdv1>var_speedmax_speed_alarm) or (var_speedpdv1<var_speedmin_speed_alarm) or (var_vibrationltemperature>var_vibrationmax_temp_c_alarm) or (var_vibrationrtemperature>var_vibrationmax_temp_c_alarm),1020).elif(eq(var_speedpdv1,0),1000,1111)

Receive the script update_asset_model_sitewise.py and extra particulars on tips on how to stream knowledge to AWS IoT SiteWise by visiting this public repository.

Then, run the next script passing the mannequin ID and the title of the file beforehand outlined.

#python3 update_asset_model_sitewise.py --assetModelId [Asset Model ID] --property_file [JSON File defining the new property] --region [AWS Region]

After the script returns a profitable response, the brand new property ID created may be obtained instantly from the AWS Console as described earlier than or through the use of the AWS CLI to question the up to date mannequin definition and the jq utility to filter the end result.

#aws iotsitewise describe-asset-model --asset-model-id [model ID] | jq .'assetModelProperties[] | choose(.title=="Equipment_State_API")'.id

You’ll be able to then repeat the method with the opposite transforms and metrics with a view to create all of the required elements for the OEE calculation.

For extra data on updating an AWS IoT SiteWise asset mannequin please go to the API reference.

Conclusion

On this weblog submit, we explored how we are able to use sensor knowledge from a real-life state of affairs to calculate OEE and get insightful data from our bodily system through the use of AWS IoT SiteWise native capabilities. We walked by way of the method of figuring out the out there knowledge and we outlined the weather that represent the principle OEE parts, Availability, High quality and Efficiency, to lastly take a deep dive into the calculations and the way we are able to automate them.

As a name to motion, we invite you to take the content material offered right here additional, making use of the OEE calculation course of to your personal use instances, in addition to utilizing the automation instruments supplied to simplify and streamline the creation of information that helps monitor your industrial techniques with accuracy.

Within the occasion you don’t have out there knowledge to make use of, we encourage you to observe the steps outlined on this public repository to simply attempt AWS IoT SiteWise with artificial knowledge and uncover the insightful data OEE may give you.

Concerning the Authors

Juan Aristizabal

Juan Aristizabal

Juan Aristizabal is a Options Architect at Amazon Internet Companies. He helps Canada West greenfield clients on their journey to the cloud. He has greater than 10 years of expertise working with IT transformations for firms, starting from Information Middle applied sciences, virtualization and cloud.  On his spare time, he enjoys touring together with his household and taking part in with synthesizers and modular techniques.

Syed Rehan

Syed Rehan

Syed Rehan  is a Sr. International IoT Cybersecurity Specialist at Amazon Internet Companies (AWS) working inside AWS IoT Service staff and is predicated out of London. He’s protecting international span of consumers working with safety specialists, builders and choice makers to drive the adoption of AWS IoT companies. Syed has in-depth data of cybersecurity, IoT and cloud and works on this position with international clients starting from start-up to enterprises to allow them to construct IoT options with the AWS Eco system.

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