Contact us for 1 free consultation: giuseppe@valueamplify ... · The IoT Ecosystem Around ML...
Transcript of Contact us for 1 free consultation: giuseppe@valueamplify ... · The IoT Ecosystem Around ML...
• Contact us for 1 free consultation: [email protected]
• Linkedin: www.linkedin.com/in/giuseppemascarella
Source: www.microsoft.com
Finding patterns in data is the holy grail (the oil in a barrel!)
What Is Machine Learning?
2. Directed Knowledge
where knowledge created
elsewhere (by a central
authority) will be used to
modify edge behavior
Cloud
1. Observed Knowledge which will modify behavior based on local learning (context)
Edge
3. Sensor Fusion Knowledge
the combining of sensory data and
data delivery orchestration such
that the resulting information is in
some sense better than would be
possible when these sources were
used individually. See Kalman filter
IoT Scenario
Predictive Maintenance in IoT Traditional Maintenance
GoalImprove production and/or maintenance
efficiency at lowest cost
Ensure scheduled
maintenance has been done
Data-Data stream (time varying features)
-Multiple data sourcesTasks completed to be done
Tasks
-Failure prediction
-Fault/failure detection & diagnosis,
-Recommendation maintenance actions
-Fault/failure tracking
-Procedure for Diagnosis
Develop ML model (MATLAB) alongside local university
Optimise code Reduce runtime
Build evaluation module
Refine model parameters
Develop user web front end
IoT Predictive Maintenance – Qantas Airways
~24,000 sensors
Qantas A380 Fleet
Technical Delays
12
$65M+per A380
50%Technical Delays
400-700Fault/warning messages/day
have potential for predictive modelling
Configure model in AML PM template
Evaluate & refine model data & parameters
Visualize results in Power BI
Months
/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
DataMachine Learning
Ecosystem
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 Action
People
Automated Systems
Apps
Web
Mobile
Bots
Bot
FrameworkSQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory Machine
LearningData Lake Store
Cognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data
Machine Learning
Ecosystem
In The Cloud
Source: www.microsoft.com
DefineScope
Good Scope for ML Experiment
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
Load
The Data
Labeling
Features
Engineering
Build
The Model
Load The Data: Data Sources
The failure history of a machine
or a component
The repair history
Previous maintenance records,
Components replaced
Maintenance opeators
Performance 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
DefineScope
Engineer Feature1. Selected raw features
2. Aggregate features
DefineScope
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
Confusion Matrix
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
• Contact us for 1 free consultation: [email protected]
• Twitter: @giuseppeHighTec
• Linkedin: www.linkedin.com/in/giuseppemascarella