LIDENSKAPFORMODERNETEKNOLOGI

VI LEVERER MED

Machine learning:From hype to industrial applications

Vegard Flovik: Lead Data Scientist, Axbit

Background:Automation technician

Physicist (Master + PhD)

Computational neuroscience

Main focus:• AI/Machine learning and data analytics

About me:

AI : Beyond the hype

Machine learning in practice:

Use case examples

Advanced analytics: From

technology to business value

https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/

McKinsey Discussion Paper

Gartner’s Hype Cycle for Emerging Technologies

Machine Learning Timeline

Drivers behind Machine Learning:Connectivity & Data

Drivers behind Machine Learning:Computing Power

2004 2020

35.86 TFLOPS

Worlds fastest Supercomputer 2002-2004

>500kW

14 TFLOPS

$1000 GPU for use in Workstation

250W

Why now? Summary

01Datasets

Connected devices, systems and

user-generated content have

provided enormous datasets.

03Computing Platforms

Cloud computing are commoditized

enabling technologies available to

anyone

02Research & Collaboration

Decades of research, combined with

open collaboration and open-source

software reduce barriers of entry.

04Economic Effects

AI has potential to reduce labor

costs and increase quality above

human capability. Computing cost is

falling.

Machine Learning Algorithms Extracting Information from Data

Data

Text

Measurements

Images

Video

Speech

Sensors

Machine Learning

Model

Labels

Distribution of effort

https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#a284206f637d

Cross-Functional Collaboration

Data Science

Building models, processing

data and extracting information

are the core of the system

Domain Knowledge

Industrial expertise is necessary to

identify goals, limitations and

possibilities in a system

Software Engineering

Software development for

products and applications is the

final step

Getting startedWhere to begin?

• What problem are you trying to solve?Business problem

• Do you have available data? (sensors, images, video, text, …)Data availability

• From business problem to data science problem

• Start simple: Data visualizationFormulate hypothesis

• Key learnings from analyzing your data?

• Static analysis tool or software solution for deployment? Insight or product?

Getting startedEvaluating opportunities for machine learning projects

Machine learning:Use case examples

• Quality assurance

• Sales forecasting

• Condition monitoring

• Image recognition

• Historical data: Machine learning algorithm «learns» which process parameters affect production quality

• Predict whether the produced unit will be «OK» or «Not OK», given process conditions during production.

Pressure Humidity Temperature Flow ......... Status

2 bar 50 % 16 C 1.2 m3/min .... OK

2.1 bar 66 % 18 C 1.1 m3/min .... Not OK

1.8 bar 60 % 14 C 1.1 m3/min .... OK

: : : : .... ....

Historical data: Known status

Pressure Humidity Temperature Flow ......... Status

2.1 bar 55 % 13 C 1 m3/min .... ?

1.9 bar 69 % 20 C 0.95 m3/min .... ?

1.95 bar 57 % 15 C 1.3 m3/min .... ?

: : : : .... ....

Status

OK

OK

Not OK

....

New datapoints: Unknown status

Prediction

Classification model

Machine learning alg.

Logged process-variables

Logged process-variables

Machine learning for production: Quality assurance

Machine learning for production: Optimization

1. Prediction:

• Historical data: Model learns connections between process-variables and production rate/efficiency etc...

2. Optimization:

• Perform a multidimensional optimization with aim of improving production.

3. Actionable output:

• Advice on recommended changes in order to optimize production, as well as estimates of expected improvement.

Control variable Pressure Temperature Flow rate Control valve Pump-RPM

Old setpoint: 1.2 bar 57C 1.6 m3/h 35% 970 o/min

New setpoint 1.1 bar 55C 1.55 m3/h 33% 970 o/min

Machine learning for condition monitoring

Example case: Monitoring of a compressor• Changes in process variables over time (temperature, pressure, flow, vibration, etc.)

• Deviations from «normal» triggers warnings/alarms: Anomaly detection

• Planned maintenance and repair rather than uncontrolled breakdowns.

Model warns of upcoming failure several days before actual event

Bearing failure

Machine learning for sales forecasting

Data:

• Historical sales records from 2013-2017

Challenge:

• Estimating the sales during last quarter of 2017?

Solution:

• Use machine learning to predict future sales

based on historical records.

???

Data 2013-2017

Utsnitt: Data 2017

Machine learning for sales forecasting

Solution:

Average error of approximately 3% for predicted

sales

Value:

Useful information for planning of logistics and

distribution

• Optimize distrubution of goods to each

location

• Warehouse optimization based on demand

forecasting

Predicted vs. Real sales

Artificial Neural Network for image recognition: “Deep Learning”

• Mathematical model that mimics how information is processed in the brain.

• Principles similar to the visual cortex of our brain: Layered network structure.

• Advanced optimization methods “train” the model to perform the desired task

Car

Artificial Neural Network for image recognition: “Deep Learning”

Style transfer learning to produce artificial intelligence “art”

Edward Munch: «The Scream»

Romsdalen Valley (Close to Molde) Artificial Intelligence generated art

Image recognition in healthcare

Using deep learning to detect pneumonia from X-ray images

• Accuracy > 95% : Comparable or better than human radiology experts

PneumoniaHealthy

Image recognition for quality assurance

Image analysis of equipment, inspect characteristics such as e.g. corrosion, cracks, weld quality etc...

Image recognition in aquaculture, fish health

Deformed fish

Lice detection/countingLice detection/counting

Automatic image analysis, extracting information on lice, decease, deformities, ++

• Allows for real-time monitoring of fish health on large fish farms

Machine Learning & Advanced AnalyticsFrom technology to business value: Main takeaway

Lice detection/countingLice detection/counting

• Data is the fuel behind machine learning

• Collaboration between domain experts, data expertise and software engineering

is key for building business value using emerging technologies.

The futureis here

Prepared tutorials:

IoT Sensors

IoT Gateway Cloud Applications

Tutorials have been prepared for a two selected example cases:

1) «Image classification»: Building a «deep learning» model using Keras/Tensorflow to classify images oftraffic signs

2) «Condition monitoring»: Build machine learning models to predict «health state» of equipment. (This tutorial is slightly more technical)

• Image classification vs. Object detection

• Object detection more complex task, and requires more data preparation

• Example case: Build image classification model using Keras/Tensorflow

Deep learning for image classification

Computer: Image = Numbers

• Use pre-trained models from Google, trained on millions of images

• Use «basic features» learned from these models, and adapt them to our own specific task: Transfer learning

Deep learning for image classification

• Example case: Classify traffic signs

• Prepared training set: < 200 images pr. class

Deep learning for image classification

• Common problem: Few training images

• Solution: Image augmentation!

• Artificially increase size of dataset.Flip/rotate/zoom images etc.

• Improves generalization of image classifiermodels

Deep learning for image classificationOriginal

Augmentation

• The power of transfer learning!

• Even with few training images of low quality:Classify correct traffic sign with 99% accuracy

Deep learning for image classification

Machine learning for condition monitoring:

Rotation speed: 2000 RPM

Accelerometers

Radial load: 6000 lbs

• Sensors used: Accelerometers mounted on each bearing

• Failures accured after exceeding designed life time of the bearing (more than 100 million revolutions)

• Challenge: Detect bearing failure before breakdown

• Deviations from «normal» triggers warnings/alarms: Anomaly detection

Machine learning for condition monitoring:• Sensors used: Accelerometers mounted on each bearing

• Failures accured after exceeding designed life time of the bearing (more than 100 million revolutions)

• Challenge: Detect bearing failure before breakdown

• Deviations from «normal» triggers warnings/alarms: Anomaly detection

• Here: Anomaly detection model generates warning3 days ahead of actual bearing failure

Bearing failureWarning3 daysAnomaly score

First: Brief introduction to Google Colaboratory

IoT Sensors

IoT Gateway Cloud Applications

Then: Let`s get to the fun part and start building models!

Traffic sign Classification: http://bit.ly/2SN3eIO

Anomaly detection: http://bit.ly/2SF9q5j