Machine Learning Impact on IoT - Part 2
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Transcript of Machine Learning Impact on IoT - Part 2
By: Adj. Prof. Giuseppe Mascarella – Brief Bio
• Contact us for 1 free consultation: [email protected]
• Twitter: @giuseppeHighTec• Linkedin: www.linkedin.com/in/giuseppemascarella
Machine Learning Impact on IoT
What Is Machine Learning for IoT?
With 30 billion sense and kinetic not static sensors by 2020
The Internet of Things (IoT)is a network with the aim to connect physical objects that contain embedded technology to communicate, sense or interact with their internal states or the external environment.
Machine learning is defined as the ability of a machine to vary the outcome of a situation or behavior based on knowledge or observation which is essential for IoT solutions.
Case Study
Machine Learning Impact on IoT
IoT Predictive Maintenance Concepts
Predictive Maintenance in IoT Traditional Predicative Maintenance
Goal Improve production and/or maintenance efficiency
Ensure the reliability of machine operation
Data Data stream (time varying features), Multiple data sources
Very limited time varying features
Scope Component level, System level Parts levelApproach Data driven Model driven
Tasks
Failure prediction, fault/failure detection & diagnosis, maintenance actions recommendation, etc. Essentially any task that improves production/maintenance efficiency
Failure prediction (prognosis), fault/failure detection & diagnosis (diagnosis)
Example Predictive Maintenance Use Cases
Aerospace
Is the ATM going to dispense the next 5 notes without failing?
Utilities
When is this aircraft component likely to fail next?
What is the root cause of the test failure?
Will the component pass the next stage of testing on factory floor or do I need to rework?
Should I replace the break disks in my car or can I wait for another month?
When is my solar panel or wind turbine going to fail next?
What is the likelihood of delay due to mechanical issues?
Manufacturing Transportation & Logistics
What maintenance task should I perform on my elevator?
IoT Predictive Maintenance – Qantas Airways
~24,000 sensors
Qantas A380 Fleet
Technical Delays1
2
$65M+per A380
50%Technical Delays400-
700Fault/warning messages/day
have potential for predictive modelling
Sample Existing Predictive Maintenance Journey
Develop ML model (MATLAB) alongside local university
Optimise code Reduce runtime
Develop user web front endBuild
evaluation module
Refine model parameters
Years
Microsoft Azure ML Predictive Maintenance Journey
Configure model in AML PM template
Evaluate & refine model data & parameters
Visualize results in Power BI
Months
How to identify the right messages to focus limited resources and reduce costly downtime?
/year
Orchestrate data pipeline in Azure Data Factory
Source: www.microsoft.com
Stay ahead of the curve with Cortana Intelligence Suite
Business apps
Custom apps
Sensors and devices
People
Automated systems
Data Intelligence
Cortana Intelligence
Action
Apps
The IoT Ecosystem Around MLIntelligence
Dashboards & Visualizations
Information Management
Big Data Stores Machine Learning and Analytics
CortanaEvent HubsHDInsight (Hadoop and Spark)
Stream Analytics
Data Intelligence Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot Framework
SQL Data WarehouseData Catalog Data Lake
Analytics
Data Factory Machine LearningData Lake
StoreCognitive Services
Power BI
Data Sources
Apps
Sensors and devices
Data
Data & Data Science Process
Source: www.microsoft.com
Outline1. Predictive Maintenance Use Cases2. Building a solution with Cortana
Intelligence Suite3. Data Science Process
Define scope/Preparation/Source/Labeling/Feature Engineering
4. Modeling, Evaluation
Scope
Question is sharp.
Data measures what they care about.
Data is connected.
Data is accurate.
A lot of data.
The better the raw materials, the better the product.
E.g. Predict whether component X will fail in the next Y days; clear path of action with answer
E.g. Identifiers at the level they are predicting
E.g. Will be difficult to predict failure accurately with few examples
E.g. Failures are really failures, human labels on root causes; domain knowledge translated into process
E.g. Machine information linkable to usage information
Machine Learning TerminologyTraining Data: A set of samplesFeatures: Individual column in our data setLabel/Target: Historical outcome related to a set of dataLearner: ML Algorithm
Feature Engineering/Munging: Manitpulating adta to come to a training set
ModellingRegression: Predict the Remaining Useful Life (RUL), or Time to Failure (TTF).Binary classification: Predict if an asset will fail within certain time frame (e.g. days). Multi-class classification: Predict if an asset will fail in different time windows: E.g., fails in window [1, w0] days; fails in the window [w0+1,w1] days; not fail within w1 days
The Process
Data Source Analysis
Solution Design • failure prediction, • failure diagnosis (root cause analysis), • failure detection, • failure type classification• recommendation of mitigation or
maintenance actions after failure
Data Sources
The failure history of a machine or component within the machine.
The repair history of a machine, e.g. previous maintenance records, components replaced, maintenance activities performed. Maintenance types.
The operation conditions of a machine, e.g. data collected from sensors.
FAILURE HISTORY REPAIR HISTORY MACHINE CONDITIONS
The features of machine or components, e.g. production date, technical specifications.
Environmental features that may influence a machine’s performance, e.g. location, temperature, other interactions.
The attributes of the operator who uses the machine, e.g. driver.
MACHINE FEATURES OPERATING CONDITIONS OPERATOR ATTRIBUTES
Sample training data~20k rows, 100 unique engine id
Sample testing data~13k rows, 100 unique engine id
Sample ground truth data100 rows
Please refer to following link of doc for Data description sectionhttps://gallery.cortanaintelligence.com/Experiment/df7c518dcba7407fb855377339d6589f
Classes
•Regression models: How many more cycles an in-service engine will last before it fails? •Binary classification: Is this engine going to fail within w1 cycles? •Multi-class classification: Is this engine going to fail within the window [1, w0] cycles or to fail within the window [w0+1, w1] cycles, or it will not fail within w1 cycles?
Feature EngineeringThe process of creating features that provide better or additional predictive power to the learning algorithm.
a1
a2
… a21
sd1 sd2 … sd21
RUL label1 label2
40+ engineered features
Data LabelingHow far ahead of time the alert of failure should trigger before the actual failure event.
Feature Engineering
Rolling Aggregates
Tumbling Aggregates
Static Features
E.g. Mean, Min, Max for every hour in the last 3 hours
E.g. Mean, Min, Max over the last 3 hours
E.g. Years in service, model
1. Selected raw features 2. Aggregate features
Modeling & Evaluation
Modelling Techniques
Predict failures within a future period of time
BINARY CLASSIFICATION
Predict failures with their causes within a future time period.
Predict remaining useful life within ranges of future periods
MULTICLASS CLASSIFICATION
Predict remaining useful life, the amount of time before the next failure
REGRESSION
Identify change in normal trends to find anomalies
ANOMALY DETECTION
Data Labelingid cycle … RUL label1 label2
1 1 191 0 01 2 190 0 01 3 189 0 01 4 188 0 0
… … … …1 160 32 0 01 161 31 0 01 162 30 1 11 163 29 1 11 164 28 1 11 165 27 1 11 166 26 1 11 167 25 1 11 168 24 1 11 169 23 1 11 170 22 1 11 171 21 1 11 172 20 1 11 173 19 1 11 174 18 1 11 175 17 1 11 176 16 1 11 177 15 1 21 178 14 1 21 179 13 1 21 180 12 1 21 181 11 1 21 182 10 1 21 183 9 1 21 184 8 1 21 185 7 1 21 186 6 1 21 187 5 1 21 188 4 1 21 189 3 1 21 190 2 1 21 191 1 1 21 192 0 1 2
Predefined window size for classification models
w1 = 30w0 = 15
w1
w0
Regression
Binary classificationMulti-class classification
Evaluation• Time dependent split• Train in the past, validate in the future
• Class imbalance• A few failure events• sampling, cost-sensitive learning
• Metrics• Recall, Precision, F1• Random Guess, Weighted Guess
“Most IoT data are not used currently…the data that are used today are
mostly for anomaly detection and control, not optimization and prediction, which provide the greatest value.”1
Reference Material
Acknowledgements• We utilized the following publically available data to help us generate
realistic data for the demo shown. We received assistance in creating this solution as a result of this repository and the donators of the data:
“A. Saxena and K. Goebel (2008). "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA.”
• McKinskey Global Institute, The Internet of Things: Mapping the Value beyond the hype
• Microsoft Cortana Gallery Experiments
Learn and try yourself!• Learn from Cortana Analytics Gallery• Solution package material – deploy by hand to learn
here• Try Cortana Analytics Solution Template –
Predictive Maintenance for Aerospace in private preview
• Try Azure IOT pre-configured solution for Predictive Maintenance
• Read the Predictive Maintenance Playbook for more details on how to approach these problems
• Run the Modelling Guide R Notebook for a DS walk-through
Adj. Prof. Giuseppe Mascarella Brief Bio
• Contact us for 1 free consultation: [email protected]
• Twitter: @giuseppeHighTec• Linkedin: www.linkedin.com/in/giuseppemascarella