By: Adj. Prof. Giuseppe Mascarella – Brief Bio

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

• 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: giuseppe@valueamplify.com

• Twitter: @giuseppeHighTec• Linkedin: www.linkedin.com/in/giuseppemascarella