MACHINE LEARNING AND PREDICTIVE ANALYTICSSigurd Lazic Villumsen

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WE ARE WORLD CHAMPIONS OF ROOF WINDOWS

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END-USER — AWARENESS / PREFERENCE 2016

PREFERENCE

VELUX GROUP

COMPETITOR X COMPETITOR Y

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No matter how good you are, you can always do it better faster or cheaper

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THE MAIN POINT

We need these technologies to unlock uninvestigated business potential.

• Increase productivity

• We want to grow (acquisitions and increased sales of core products)

• Increase quality even further

• Mitigate manufacturing challenges• Increasing people turnover

• Increasing product program complexity

• Aging workforce

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5 VELUX GROUP PRESENTATION

CULTIVATE ⁄ A SIMPLE IDEA

2017 numbers (EUR)

VKR Holdingrevenue

VKR Holdingnet profit

1740 10,200

2.5bn

340m

Company facts

The VELUX Group is owned by VKR Holding A/S, a limited company wholly owned by the foundations and family.

The VELUX Group’s financial results are incorporated into VKR Holding’s consolidated accounts.

sales companies around the world

production sites in nine countries

employeesglobally

UNCHARTED TERRITORY

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Possible applications

We don’t know where we are so need to explore before we prioritize.

But we need data to be able to do this

POWERBI DASHBOARD FOR MACHIENS

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We are using PowerBI to visualise the data we are collecting from machines in our production.

Benefits

We can easily share data visualizations in our organization.

Very good at making first level analytics.

VELUX COMPANY

IS MACHINE LEARING ALWAYS NEEDED?

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Just by making data more accessible, we gain a lot, and also by using fx the insight function in PowerBI we can learn new stuff about our machines.

VELUX COMPANY

SMART DATA, NOT ONLY BIG DATA

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In VELUX we have our own PackML interface made for data collection from machines.

This gives us the same data foundation for all machines, making it easy to make standardised dashboards fx OEE calculation, Alarm statistics ect.

VELUX COMPANY

UNCHARTED TERRITORY

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Possible applications

Process optimization

IMPROVING PRODUCTION PROCESSES

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What we think

ML can be used as a way of improving production processes.

Why is it important

We need to be in control of our processes if we wish to stay in control of quality, delivery cost.

Why ML

We have a big toolbox for optimizing processes, ML could be an additional tool for that.

Status:

Not very far in these areas

Cases:

1. Aluminium saw

2. PU painting line.

CASE 1: IMPROVING ALUMINIUM SAW

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Why

Varying length is a problem when cutting aluminum on flying saw.

Goal: Remove the varying length by using machine learning

Result.

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CASE 1: IMPROVING ALUMINIUM SAW

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Why

Varying length is a problem when cutting aluminum on flying saw.

Goal: Remove the varying length by using machine learning

Result.

Machine learning was never necessary as linear correlation was found just by looking at the data.

Machine learning not always needed

CASE 2: IMPROVING PU PAINTING

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Goal:

• Improve quality of painted components

• Reduce the used amount of paint.

• Predict maintenance e.g. change of nozzle

• Data available

Visual inspection

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Possible applications

Process optimization

VISUAL INSPECTION

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What we think

We think that we can push the boundaries for visual inspection by using new technology.

Why is it important

Manual inspection is a cost heavy process.

We need to ensure uniform and global quality references

Why ML

We are working with natural products e.g. wood. So far traditional vision solutions has not been good enough

Cases:

3. Wood grading

4. Paint inspection

5. Pane crack recognition

CASE 3: USING DEEP LEARNING FOR AUTOMATIC WOOD GRADING

LUKE WHITE

MARCH 4, 2019

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USING DEEP LEARNING FOR AUTOMATIC WOOD GRADING

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Goal: To develop a machine vision system to find defects in wooden components as well as or better than current human inspection.

Several iterations of training utilizing both “un-supervised” and “supervised” methods where tested.

SUPERVISED VS. UNSUPERVISED

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Unsupervised Training Example

Supervised Training Example

AUTOMATIC WOOD GRADING

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Reclassified good/bad parts and re-trained system. New results of 98%+ certainty.

Determined unsupervised training not allowing different quality standards for different wood surfaces (A, B, C, etc.)

Supervised Training

Allowed different surfaces to have different quality standards.

Resulted in certainties above 99% on defined defects.

Current work:

Testing fine crack/lamination seam testing using current lighting

CASE 4: WHITE PAINTED WOOD COMPONENTS

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DETECTION OF PAINT DEFECTS

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Goal: Classify defects in white painted wood components

Simple POC

Result: Good preliminary results. We can detect contaminations and non uniform paint

RESULTS FROM PRE-STUDY

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CASE 5: DETECTION OF CRACKED PANES

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MOTIVATION

Damaged glass is a source of claims and internal scrap for VELUX

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Manual inspection Hand programmed scanner Machine learning

Currently glass is subject to manual inspection.

A scanner will be able to take high quality and consistent

images of glass. Performance is untested on edge damages,

but images can be fed to a machine learning model

Machine learning will ensure a high detection rate (95%+), while keeping maintenance low, as the models are self

learning.

DEEP LEARNING FOR QUALITY INSPECTION• Using a deep learning, we teach a computer model to recognize glass with damaged edges and scratches through four individual steps

• By introducing the computer model to a large number of images of damaged and undamaged glass. You can say that the computer model is “trained”

• The computer model then predicts whether or not it thinks the glass is damaged.

• We then compare these predictions to the actual state of the glass.

• The computer model then updates itself based how right it predicted the state of the glass.

Window glass Deep Learning model

Layer 1 Layer 2 Layer 3 Layer 4

Predictions

Evaluation

Actual state

Model learns based on error

DATSET• The final dataset consisted of 1200 image tiles. However, only 55 of the images have an actual damage on them, which in this case is the important

information. As such, we have a very skewed dataset, which will have to be taken into consideration when training the models and validating the results.

• Further image preprocessing is performed as part of the data pipeline, this being:

• Random image augmentation is added to increase image diversity. Augmentations include random 90-interval degrees rotation, color scaling, adding random noise, tilting and shifting images by small amounts.

Random image augmentation

INITIAL RESULTS – RANDOM VALIDATION• To solve the issue of the unbalanced dataset (55 damaged image images when subtracting the validation data, vs 1145 undamaged images), we have

to manually balance the dataset. If we leave it unchanged, the model will have a very hard time distinguishing between damaged and undamaged glass.

• Balancing the dataset simply means copying the damaged images until we reach a balanced dataset (1125 damaged and undamaged images). This will make it very easy to overfit the data, which can be alleviated by applying enough data augmentation.

• A validation set can be chosen in various ways, usually it is done by randomly holding back 20% of the data. In this study, we experiment with various small validation sizes, due to the small sample size of data (number of images). We train the model on validation samples of 4, 8 and 12 images of each class for balanced interpretable validation – A total of 8, 16 and 24 images. To validate the results, we do this sampling the validation data 50 times, training a model for each sample and reporting the average validation loss and accuracy across the 50 models.

• Accuracies should be compared to the accuracy of just guessing the majority class – which as we have balanced the dataset is 50%.

• We utilize a model type pre-trained on another very large dataset, such that the model has already learned useful representations. Due to the short time, we utilize an older model architecture (VGG16 with convolutional top layers) that is known to converge well during training – Higher performance is expected when using a more recent model architecture.

Overview of results

Training data1176-1192 images

Validation data4-12 images sampled from 55 damaged

images and 4-12 images sampled from 1145 undamaged images. Each sampled 50 times.

Average of 50 models with 16 validation images

Loss measure

Loss

Training accuracy

-

Validation accuracy

0.909

Average of 50 models with 8 validation images

Loss measure

Loss

Training accuracy

-

Validation accuracy

0.922

Average of 50 models with 24 validation images

Loss measure

Loss

Training accuracy

-

Validation accuracy

0.910

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Rolls Rolls

Viprotronscanner

Camera

Lighting

Viproton PC & software /w VELUX software

Mounted share

Robot arm Discarded Pellet large

Discarded Pellet small

Azure Data Box Edge

Robot com interface

Relay

Azure data lake storage

Reporting on performance

Quality monitor

VELUX TC-SkjernHardware: VELUX IT

PRODUCTION OPERATIONSViprotron

Software: Data & Analytics

UNCHARTED TERRITORY

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Possible applications

Process optimization

Visual inspection

Optimizing business

processes

CASE 6: IMPROVING SERVICE REGISTRATIONS.

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Project goal:

Predict the cost of a service technician and what spareparts he might need based on user input.

Data:

User inputs, Many years of service registrations

PREDICTIVE ANALYTICS PROCESS. Input → Model → Output

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Input:SCO

Product TypeProduct Size

Product VariantComponentSymptom

Ect.

Model:Classification

We run a model that creates a decision tree on the input variable dependent on the

associated output variables.

Output:Spare part

Number of SparepartsStdTextKey

HoursExecution factor

Probability

{

"json_data": {

"sales_company": "V-F",

"product_type": "GPL",

"product_size": "MK04",

"product_variant": "3076F",

"component_group_code": "0004",

"component_code": "4003",

"symptom": "9",

"id": "1"

}

}

{

"json_data": {

"Material": " IPL MK04 0076F",

"Material_Quantity": "1",

"Stdtextkey": "GL_WH",

"Hours": "1",

}

}

PREDICTION MODEL VS. MACHINE LEARNING

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HOW DOES A PREDICTION MODEL WORK

Model

Data

Prediction

Customer request

Service order

Confirmed data

Feedbackloop

What is the difference between a prediction model and machine learning?Prediction model: Uses data and model to create a customer specific prediction Machine learning: Set up that alters the model based on the comparison of the prediction and confirmation data

Learning algorithm

NOT PART OF THE CURRENT PROJECT SCOPE!!!

ARCHITECTURE FOR PREDICTIVE MODELLING IN VELUX

Hana

Data storage on

BW

Development server

ComputeLocal environment

Localfilesystem

CodeTrained model

Trainedmodel

NAS

ODBC

Productionserver

REST API(Plumber)

JSON

JSON

Audit Trail

WebpageVELUX Network Cloud

SAP PEP SRS 2

MAGIC 1 MAGIC 2

DEMO

Service demo

NOTICE: ARCHITECTURE FOLLOWS APPLICATION

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Not necessarily a one size fits all, but sharing of best practice is extremely important

The application requirements will control the architecture.

Pane crack recognition Service predictions

UNCHARTED TERRITORY

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Possible applications

Process optimization

Visual inspection

Optimizing business

processes

MACHINE LEARNING: WHERE ARE WE?

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2018 2019 2020 2021

White painted wooden

components

Pane cracks

Wood defect detection

Sup

ply

Oth

er

White painted wooden

componentsPane cracks

Wood defect detection

Quality prediction Painting line

Aluminum sawPredictive

maintenance ØB

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UNCHARTED TERRITORY

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Possible applications

Process optimization

Visual inspection

Optimizing business

processes

Predictive maintenance

Lead generation

Unknown applications

Mordor

CONCLUDING REMARKS

• A good data foundation is an absolute requirement.

• We have taken the first steps towards using machine learning in VELUX.

• We are in many cases still novices in the field, but making progress.

• I expect we will see this as a common tool in 1-2 years

• Remember your old tools.

• Don’t limit yourself to one supplier

• Partner up

• Work with it

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CONTACT INFO

FIND US HERE

pinterest.com/VELUXGroup/

linkedin.com/company/VELUX

youtube.com/user/VELUX

facebook.com/VELUX

twitter.com/VELUX

Sigurd Lazic Villumsen

Sigurd.Villumsen@velux.com

+45 30707273