Calculating General Gear Effectiveness (OEE) with AWS IoT SiteWise

Introduction

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

Moreover, we’ll present how one can automate OEE components to streamline the implementation of this answer in lots of different use instances, like manufacturing manufacturing strains in pharmaceutical, meals and beverage industries. That can assist you put into apply the ideas offered on this weblog, we additionally present a code repository that allows you to stream artificial information 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 can be utilizing as a body of reference. Our instance is a BHS and the required information 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 velocity sensor for conveyor surveillance, and one photograph electrical sensor counting the bags throughput.

The structure of the answer is as follows:

Sensor information is collected and formatted by way of CloudRail, an AWS Companion whose answer significantly simplifies the gathering and streaming of IIoT information to AWS IoT SiteWise. This integration is configurable by the CloudRail administration Portal straight.  The structure contains extra elements for making the sensor information accessible to different AWS companies by an S3 bucket.

AWS IoT SiteWise pre-requisites

Earlier than sending information to AWS IoT SiteWise, you could create a mannequin and outline its properties.  As talked about earlier, we have now 4 sensors that can be grouped into one mannequin, with the next measurements (information 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’ll add a number of attributes (static information) to the asset mannequin. The attributes signify 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 signify 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 elements.

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 baggage/hour, which is equal to 0.83333 baggage/second. This worth is dependent upon the system and must be obtained from the producer or based mostly on area remark 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 forth.) from the sensors we wish to embody within the calculation.  The temperature coming from the 2 vibration sensors (in Celsius) and the velocity of the carousel coming from the velocity sensor (m/s) will dictate the supply state.

The suitable 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 velocity worth not inside the regular vary outlined
1000 – A deliberate cease
1111 – A traditional operation

The idle state of the BHS isn’t outlined on this simplified use case, nonetheless, it’s attainable to combine different information streams into AWS IoT SiteWise and register data coming from Programmable Logic Controllers (PLCs) or different programs 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 kind (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 appear to be this as you enter it within the console. The UI will carry 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 information over time. Hold the identical interval for every metric.

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

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

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

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

The metric definitions of the mannequin ought to appear to be 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 isn’t?  we use the measurements and information which are accessible from the BHS’s 4 sensors.

The luggage are counted by wanting on the distance the photograph electrical sensor is offering, subsequently 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 solution to calculate the luggage passing, however on the identical time it’s susceptible to a number of situations 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 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, as a way 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)

Bag_count will account for all baggage passing in entrance of the photograph electrical sensor, and Dubious_Bag_Count will depend the objects detected as baggage beneath two irregular situations:

1. The space detected is inside 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 result of vibration or a sensor not correctly connected.
2. Luggage counted when the velocity of the carousel is above the restrict outlined; beneath this situation the sensor can miss counting baggage which are too shut collectively on the carousel.

NOTE: the above situations are easy guidelines and the precise values for distance base and thresholds should be reviewed and analyzed with area information 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 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 baggage/hour = 0.0833333 baggage/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 could be outlined programmatically as a substitute of utilizing the AWS Console. That is significantly 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 susceptible than guide ones and could 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 can be referencing on the rework/metric calculation, in addition to the mannequin ID.

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. Bear in mind to interchange the propertyIds and put it aside as equipment_state.json:

{
     "identify": "Equipment_State",
     "dataType": "DOUBLE",
     "kind": {
       "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 learn how to stream information to AWS IoT SiteWise by visiting this public repository.

Then, run the next script passing the mannequin ID and the identify 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 could 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(.identify=="Equipment_State_API")'.id

You’ll be able to then repeat the method with the opposite transforms and metrics as a way 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 publish, we explored how we will use sensor information from a real-life state of affairs 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 accessible information and we outlined the weather that represent the primary OEE components, 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 programs with accuracy.

Within the occasion you don’t have accessible information to make use of, we encourage you to comply with the steps outlined on this public repository to simply strive AWS IoT SiteWise with artificial information and uncover the insightful data OEE may give you.

Concerning the Authors