Calculating General Tools Effectiveness (OEE) with AWS IoT SiteWise

Introduction

This weblog put up represents the second entry in a collection on utilizing General Tools Effectiveness (OEE) with AWS IoT SiteWise. On this put up, we are going to do a deep dive on how you can calculate OEE utilizing AWS IoT SiteWise native capabilities to gather, retailer, rework, and show calculations as an end-to-end answer. We’ll have 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 collection, Industrial General Tools Effectiveness (OEE) information with AWS IoT SiteWise, for extra context on the use case.

Moreover, we are going to present how one can automate OEE parts to streamline the implementation of this answer in lots of different use instances, like manufacturing manufacturing traces in pharmaceutical, meals and beverage industries. That can assist you put into observe the ideas offered on this weblog, we additionally present a code repository that allows 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 likely be utilizing as a body of reference. Our instance is a BHS and the mandatory 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 picture electrical sensor counting the bags throughput.

The structure of the answer is as follows:

Sensor knowledge is collected and formatted by way of CloudRail, an AWS Companion whose answer vastly simplifies the gathering and streaming of IIoT knowledge to AWS IoT SiteWise. This integration is configurable by the CloudRail administration Portal straight.  The structure consists of further parts for making the sensor knowledge out there to different AWS companies by an S3 bucket.

AWS IoT SiteWise pre-requisites

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

Mannequin:Carousel

Asset Identify: CarouselAsset

Property {

Measurement: Photograph.Distance

Measurement: Velocity.PDV1

Measurement: VibrationL.Temperature

Measurement: VibrationR.Temperature

}

Along with the measurements, we are going to add just a few attributes (static knowledge) to the asset mannequin. The attributes symbolize totally 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 symbolize 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:

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

Calculating OEE

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

The usual OEE formulation is:

Let’s have 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 best run fee is 300 luggage/hour, which is equal to 0.83333 luggage/second. This worth depends upon the system and ought to be obtained from the producer or primarily based on area remark efficiency.

Availability

Availability = Run_time/(Run_time + Down_time)

We’ve 4 sensors on the BHS and we have to outline what measurements (temperature, vibration, and so on.) from the sensors we need to embrace 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 primarily based 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, a knowledge 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 standard operation

The idle state of the BHS shouldn’t be outlined on this simplified use case, nevertheless, 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, Knowledge sort (Double) and enter the next formulation on the respective area:

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)

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

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

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) operating with out concern.

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

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

High quality

High quality = Successes / (Successes + Failures)

Right here, we have to outline what constitutes a hit 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’s not?  we use the measurements and knowledge which can be out there from the BHS’s 4 sensors.

The luggage are counted by trying on the distance the picture electrical sensor is offering, due to this fact when there’s an object passing the band, the sensor will report a distance that’s much less that the “base” distance. It is a easy method to calculate the baggage passing, however on the similar time it’s susceptible to a number of circumstances that may influence 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 aren’t any objects in entrance of it. This worth may 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 a way delicate is the sensor, as a way to keep away from counting particles or small objects (like bag attachments or belts) as an everyday bag.

We then set up two transforms for bag rely:

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)

Bag_count will account for all luggage passing in entrance of the picture electrical sensor, and Dubious_Bag_Count will rely the objects detected as luggage beneath 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 modifications as a consequence of vibration or a sensor not correctly hooked up.
2. Baggage counted when the pace of the carousel is above the restrict outlined; beneath 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 correct values for distance base and thresholds should be reviewed and analyzed with area 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 will outline OEE Availability as a metric as effectively:

High quality = Successes / (Successes + Failures)

Bear 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 Successes and Failures from our High quality calculation, in addition to Run_Time from Availability. Due to this fact, 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 parts outlined as transforms and metrics may be outlined programmatically as an alternative of utilizing the AWS Console. That is significantly helpful when there are advanced 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 guide ones and may be configured constantly throughout a number of environments. Let’s check out how we will do it utilizing the AWS SDK for Python (Boto3).

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

Then outline a JSON for the metric/rework. As an example, 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. Bear in mind to exchange 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)

Get hold of the script update_asset_model_sitewise.py and extra particulars on how you can 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 straight from the AWS Console as described earlier than or by utilizing 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 may then repeat the method with the opposite transforms and metrics as a way to create all of the required parts 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 put up, we explored how we will use sensor knowledge from a real-life situation to calculate OEE and get insightful data from our bodily system by utilizing AWS IoT SiteWise native capabilities. We walked by 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 will 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 comply with 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.

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