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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
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