• Contact us for 1 free consultation: giuseppe@valueamplify.com

• 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: giuseppe@valueamplify.com

• Twitter: @giuseppeHighTec

• Linkedin: www.linkedin.com/in/giuseppemascarella