data analysis and predictive maintenance with matlab and

1© 2021 The MathWorks, Inc.

Data analysis and Predictive Maintenance with

MATLAB and Simulink

Dr.-Ing. Marco RossiEdu Customer Success Engineer, The MathWorks

mrossi@mathworks.com 26th July 2021

2

Conclusions

Case Study: flow pack machine

Predictive Maintenance workflow

Predictive Maintenance and what to expect from it

Event outline

3

MathWorks Today

in 2017 revenues with

60% from outside the US

$900+

million

4000+

staffin 31 offices around

the world

3 million+

usersin more than 180

countries

and profitable every year

Privately

held

Headquarters

Natick, MA USA Europe

France

Germany

Ireland

Italy

Netherlands

Spain

Sweden

Switzerland

UK

Asia-Pacific

Australia

China

India

Japan

Korea

North America

United States

4

Our software is used to design the products we rely on every day

Commercial Aircraft

Smartphones

Automobiles

Consumer Goods

5

And the breakthroughs transforming how we live, learn, and work

Advanced ProstheticsReusable Rockets

Covid-19 Research Ecology

6

Pool 1

Let’s test your knowledge

https://forms.office.com/r/Yueu8cuEFx

Scan me!

7

MATLAB and …

Math. Graphics. Programming.

• data analysis

• optimization

• predictive modelling

• algorithm development

MATLAB: high powerful scripting language

8

MATLAB and Simulink and …

Simulink: simulation and model based design

• integration multifield environment

• optimized model development

• grid design/integration

• code generation

Transforming the way Engineers work.

9

Schematic Physical Modeling

• multi-physics problem

• simple visualization

• learning oriented

• self-defined equations

Simscape: Physical Modeling

MATLAB and Simulink and Simscape and …

𝐹ext = 𝑚 ሷ𝑥 + 𝑏 ሶ𝑥 + 𝑘𝑥

10

MATLAB and much more…https://www.mathworks.com/products.html

Toolboxes and Add-Ons:

▪ more than 350 by MathWorks

▪ more than 40k by Community

Control System Toolbox - design and analyze control systems

Automated Driving Toolbox – design, simulate and test ADAS

11

The Impact of AI in the near future

12

Artificial Intelligence Index Report 2021 - HAI - Stanford University https://aiindex.stanford.edu/wp-content/uploads/2021/03/2021-AI-Index-Report_Master.pdf

Easily integrate Artificial Intelligence in your researchAI skills for next generation students

13

learn complex non-linear relationships

Solution is too complex for handwritten rules or equations

Speech Recognition Object Recognition Predictive Maintenance

update as more data becomes available

Solution needs to adapt with changing data

Weather Forecasting Energy Load Forecasting Stock Market Prediction

learn efficiently from very large data sets

Solution needs to scale

IoT Analytics Taxi Availability Airline Flight Delays

Easily integrate Artificial Intelligence in your researchAI is everywhere

14

learn complex non-linear relationships

Solution is too complex for handwritten rules or equations

Speech Recognition Object Recognition Predictive Maintenance

update as more data becomes available

Solution needs to adapt with changing data

Weather Forecasting Energy Load Forecasting Stock Market Prediction

learn efficiently from very large data sets

Solution needs to scale

IoT Analytics Taxi Availability Airline Flight Delays

Easily integrate Artificial Intelligence in your researchAI is everywhere

15© 2021 The MathWorks, Inc.

16

Why perform maintenance?

▪ Example:Faulty braking system → wind turbine disaster

▪ Wind turbines cost millions of dollars

▪ Failures can be dangerous

▪ Maintenance → expensive and dangerous

17

Types of Maintenance

Reactive Maintain once there is a problem

ScheduledMaintain at a regular rate

Predictive Forecast when problems will arise

Example Replace car’s battery after a problem

Example Change car’s oil every 10,000 km

Example Act on the specific equipment (e.g.

battery) when a failure is predicted

Issue Unexpected failures can be expensive

and potentially dangerous

Issue Unnecessary maintenance can be

wasteful

Issue Difficult to make accurate forecasts for

complex equipment

18

Example Change car’s oil every 10,000 km

Types of Maintenance

Reactive Maintain once there is a problem

ScheduledMaintain at a regular rate

Predictive Forecast when problems will arise

Example Replace car’s battery after a problem

Example Act on the specific equipment (e.g.

battery) when a failure is predicted

Issue Unexpected failures can be expensive

and potentially dangerous

Issue Unnecessary maintenance can be

wasteful

Issue Difficult to make accurate forecasts for

complex equipment

19

Why perform Predictive Maintenance?

✓ Reduced maintenance costs

✓ Reduced equipment failures

✓ Reduced downtime for repairs

✓ Increased service life of parts

✓ Increased equipment safety

✓ Increased overall profitability Image: Tensor Systems

20

Examples of Predictive Maintenance across industries

Online engine health monitoring

▪ Real-time analytics integrated with enterprise

service systems

▪ Predict sub-system performance:

oil, fuel, liftoff, mechanical health, controls

Pump Health Monitoring System

▪ Spectral analysis and filtering on binary sensor

data and neural network model prediction

▪ More than $10 million projected savings

Production machinery failure warning

▪ Reduce waste and machine downtime

▪ MATLAB based HMI warns operators of potential

failures

▪ > 200,000 € savings per year

21

Examples of Predictive Maintenance across industries

22

The challenges associated with predictive maintenance

Hard to get started

Too many options for

machine learning,

feature extraction, etc.

Lack of failure data

Integrating algorithms

with existing

infrastructure

23

Solutions

24

MathWorks provides a complete workflow solution

▪ Get started using reference examples for different machines

▪ Consulting & Training can support the process

▪ Use MATLAB to explore machine learning techniques

▪ Use Predictive Maintenance Toolbox to design different models

▪ Use Simulink to create a Digital Twin of the equipment

▪ Generate failure data directly from the simulated model

▪ Deploy on embedded devices through C/C++ code generation

▪ Integrate with enterprise IT systems with MATLAB Production Server

25© 2021 The MathWorks, Inc.

26

How does Predictive Maintenance work?

27

Ingredients

28

ALGORITHM

Failure in

20 ± 2 days

DATA

INFRASTRUCTURE

DEPLOY

Ingredients

29

Acquire

Data• Sensor

• Synthetic

Preprocess

Data

Identify

Condition

Indicators

Train

Model

Deploy &

Integrate

Algorithm development workflow

30

Preprocess

Data

Identify

Condition

Indicators

Train

Model

Deploy &

Integrate

Algorithm development workflow

Acquire

Data• Sensor

• Synthetic

• Digital-twin for generating faulty data

o no failures on the actual system

o failures injected on relevant components

Time

Sensor data Flow pack machine

Time

Sensor data

Inject faults• Motor winding – phase • Bearing – damping coef. • Gear box – efficiency Digital twin

of the flow pack machine

Refine model

Time

Synthetic data

31

Identify

Condition

Indicators

Train

Model

Deploy &

Integrate

Algorithm development workflow

Acquire

Data• Sensor

• Synthetic

• Data preprocessing

o signals and time-series prepared for the next step

o filtering, smoothing, labeling, etc.

Preprocess

Data

Source: Andrej Karpathy slide from TrainAI 2018

32

Train

Model

Preprocess

Data

Deploy &

Integrate

Algorithm development workflow

Acquire

Data• Sensor

• Synthetic

Identify

Condition

Indicators

• Condition indicators

o Analyze data & extract features

o Select the most appropriate one

o Diagnostic Feature Designer App by MathWorks

33

Preprocess

Data

Deploy &

Integrate

Algorithm development workflow

Acquire

Data• Sensor

• Synthetic

Identify

Condition

Indicators

• Condition monitoring ( “is my machine healthy? Is it failing? What’s failing exactly?” ):

o assess machine’s current condition, detect and diagnose faults

o Classification Learner App by MathWorks

Machine Learning

Train

Model

34

Preprocess

Data

Deploy &

Integrate

Algorithm development workflow

Acquire

Data• Sensor

• Synthetic

Identify

Condition

Indicators

• Prognostics ( “how much time do I have before my machine fails?” ):

o forecast when a failure will happen based on the current and past state of the machine,

o estimate machine's remaining useful life (RUL) or time-to-failure

Machine Learning

Train

Model

35

Train

Model

Preprocess

Data

Algorithm development workflow

Acquire

Data• Sensor

• Synthetic

Identify

Condition

Indicators

• Deployment & Integration:

o developed algorithms can be distributed on different targets: edge, embedded devices, etc.

o models and functions designed in MATLAB can be integrated with IT/Enterprise existing services

Deploy &

Integrate

36© 2021 The MathWorks, Inc.

37

Case Study: flow pack machine

38

❑ Data gatheringAcquisition/generation and preprocessing

❑ Condition monitoringDetect and diagnose failures on servomotor and gearbox

❑ PrognosticsPredict RUL for bearing system

Case Study: flow pack machine

39

Case Study: flow pack machine

Simscape™

Run

Simulations

40

Handle data efficiently: Ensemble Datastores and Tall Arrays

41

❑ Data gatheringAcquisition/generation and preprocessing

❑ Condition monitoringDetect and diagnose failures on servomotor and gearbox

❑ PrognosticsPredict RUL for bearing system

Case Study: flow pack machine Machine Learning

42

Poll 2

Machine Learning?

Scan me!

https://forms.office.com/r/ZcmYprvLMm

43

What’s Machine Learning about?An easy explanation

44

Machine Learning uses data and produces a program to perform a task

Task: detect and diagnose fault in the system

Classification

Model

‘DANGEROUS’

‘BAD’

‘WARNING’

‘GOOD’

Features

Machine Learning for Predictive Maintenance

Sensor/simulation data

STEP 1 STEP 2

Statistics and Machine Learning Toolbox™Predictive Maintenance Toolbox™

45

Machine Learning uses data and produces a program to perform a task

Task: detect and diagnose fault in the system

Classification

Model

‘DANGEROUS’

‘BAD’

‘WARNING’

‘GOOD’

Features

Machine Learning for Predictive Maintenance

Sensor/simulation data

STEP 1 STEP 2

Statistics and Machine Learning Toolbox™Predictive Maintenance Toolbox™

46

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

47

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

48

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

49

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

50

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

General Parameter

Pro

ba

bili

ty

Fault 0 Fault 1 Fault 2 Fault 3 Fault 4 Fault 5

51

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

52

1. Import Data

2. Data Visual Inspection

3. Feature CalculationVisual inspection

4. Feature Ranking

5. Export Code and Results

STEP 1: Feature extraction – Diagnostic Feature Designer

53

Machine Learning uses data and produces a program to perform a task

Task: detect and diagnose fault in the system

Classification

Model

‘DANGEROUS’

‘BAD’

‘WARNING’

‘GOOD’

Features

Machine Learning for Predictive Maintenance

Sensor/simulation data

STEP 1 STEP 2

Statistics and Machine Learning Toolbox™Predictive Maintenance Toolbox™

54

1. Import Data

2. Training

3. Result Evaluation

4. Model Refinement

5. Export Code and Results

STEP 2: Train a classification model – Classification Learner

55

1. Import Data

2. Training

3. Result Evaluation

4. Model Refinement

5. Export Code and Results

STEP 2: Train a classification model – Classification Learner

56

1. Import Data

2. Training

3. Result Evaluation

4. Model Refinement

5. Export Code and Results

STEP 2: Train a classification model – Classification Learner

57

1. Import Data

2. Training

3. Result Evaluation

4. Model Refinement

5. Export Code and Results

STEP 2: Train a classification model – Classification Learner

58

1. Import Data

2. Training

3. Result Evaluation

4. Model Refinement

5. Export Code and Results

STEP 2: Train a classification model – Classification Learner

59

1. Import Data

2. Training

3. Result Evaluation

4. Model Refinement

5. Export Code and Results

STEP 2: Train a classification model – Classification Learner

60

❑ Data gatheringAcquisition/generation and preprocessing

❑ Condition monitoringDetect and diagnose failures on servomotor and gearbox

❑ PrognosticsPredict RUL for bearing system

Case Study: flow pack machine Machine Learning

61

Prediction model – RUL estimation

Healthy state Failure

RUL Estimator Models

Similarity model Degradation model

Survival model

Safety

threshold

Healthy state Failure

Healthy state Failure

Check out: RUL Estimation Using RUL Estimator Models

62

RUL estimation: results

MATLAB App Designer

63

Integrate analytics with systems

MATLAB

Runtime

C, C++ HDL PLC

Embedded Hardware

StandaloneApplication Python

MATLABProduction

ServerC/C++ ++

ExcelAdd-in Java

Hadoop/

Spark.NET

Enterprise Systems

PythonStandaloneApplication

(.exe)

Web App

64© 2021 The MathWorks, Inc.

65

Key Takeaways

❑ Frequent maintenance + unexpected failures

→ expensive and dangerous

❑ Predictive Maintenance:

▪ Costs

▪ Reliability and safety of equipment

▪ Opportunities for new services

▪ Algorithm complexity

▪ Initial investment

❑ MATLAB → Predictive Maintenance programs

▪ Systematic and integrated approach

▪ Quick & easy

▪ and…

Predictive Maintenance Toolbox™

Statistics and Machine Learning Toolbox™

66

is a Leader in the 2021 Gartner

Magic Quadrant for Data Science

and Machine Learning Platforms for

the Second Year in a Row

Gartner Magic Quadrant for Data Science and Machine Learning Platforms, Peter Krensky, Carlie Idoine, Erick Brethenoux, Pieter den Hamer, Farhan Choudhary, Afraz Jaffri, Shubhangi Vashisth,1st March 2021.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from MathWorks.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research

publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any

warranties of merchantability or fitness for a particular purpose.

67

MathWorks can help you get started TODAY

Stateflow OnrampDeep Learning

OnrampSimulink Onramp

12 hours of FREE content –

available for everyone

Over 80 hours of comprehensive MATLAB learning content

Get started

Core MATLAB

Data Science

MATLAB Onramp

FREE FREE FREE FREE

MATLAB

Fundamentals

MATLAB

Programming

Techniques

MATLAB for Data

Processing and

Visualization

Machine Learning

with MATLAB

Deep Learning with

MATLAB

• Interactive learning environment

• Automated assessment and feedback

• Measurable progress report and completion certificate

• 24/7 availability

• No cancellation or travel required

Machine Learning

Onramp

FREE

www.mathworks.com/services/training/

68

MathWorks can help you get started TODAY

❑ MATLAB for Predictive Maintenance

❑ 3 things you beed to know

❑ Predictive Maintenance Video Series

❑ Predictive Maintenance with MATLAB (e-book)

❑ Four Common Obstacles

❑ Digital Twins for Predictive Maintenance

69© 2021 The MathWorks, Inc.

Thanks. Questions ?Poll 3

https://forms.office.com/r/Zr1rPB1Fkg

Scan me!

70

Customer Success Stories

‒ Atlas Copco Minimizes Cost of Ownership Using Simulation and Digital Twins

‒ Baker Hughes Develops Predictive Maintenance Software for Gas and Oil Extraction Equipment Using

Data Analytics and Machine Learning

‒ Krones Develops Package-Handling Robot Digital Twin

‒ Lockheed Martin Builds Discrete-Event Models to Predict F-35 Fleet Performance

‒ Metro de Madrid Adopts Machine Learning for Predictive Maintenance in Tunnels

‒ Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance for Manufacturing

Processes with Machine Learning

‒ Siemens Develops Health Monitoring System for Distribution Transformers