Data analysis and Predictive Maintenance with MATLAB and ...
Transcript of Data analysis and Predictive Maintenance with MATLAB and ...
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1© 2021 The MathWorks, Inc.
Data analysis and Predictive Maintenance with
MATLAB and Simulink
Dr.-Ing. Marco RossiEdu Customer Success Engineer, The MathWorks
[email protected] 26th July 2021
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Conclusions
Case Study: flow pack machine
Predictive Maintenance workflow
Predictive Maintenance and what to expect from it
Event outline
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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
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Our software is used to design the products we rely on every day
Commercial Aircraft
Smartphones
Automobiles
Consumer Goods
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And the breakthroughs transforming how we live, learn, and work
Advanced ProstheticsReusable Rockets
Covid-19 Research Ecology
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Pool 1
Let’s test your knowledge
https://forms.office.com/r/Yueu8cuEFx
Scan me!
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MATLAB and …
Math. Graphics. Programming.
• data analysis
• optimization
• predictive modelling
• algorithm development
MATLAB: high powerful scripting language
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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.
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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
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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
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The Impact of AI in the near future
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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
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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
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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
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15© 2021 The MathWorks, Inc.
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Why perform maintenance?
▪ Example:Faulty braking system → wind turbine disaster
▪ Wind turbines cost millions of dollars
▪ Failures can be dangerous
▪ Maintenance → expensive and dangerous
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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
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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
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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
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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
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Examples of Predictive Maintenance across industries
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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
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Solutions
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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
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25© 2021 The MathWorks, Inc.
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How does Predictive Maintenance work?
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Ingredients
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ALGORITHM
Failure in
20 ± 2 days
DATA
INFRASTRUCTURE
DEPLOY
Ingredients
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Acquire
Data• Sensor
• Synthetic
Preprocess
Data
Identify
Condition
Indicators
Train
Model
Deploy &
Integrate
Algorithm development workflow
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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
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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
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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
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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
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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
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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
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36© 2021 The MathWorks, Inc.
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Case Study: flow pack machine
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❑ Data gatheringAcquisition/generation and preprocessing
❑ Condition monitoringDetect and diagnose failures on servomotor and gearbox
❑ PrognosticsPredict RUL for bearing system
Case Study: flow pack machine
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Case Study: flow pack machine
Simscape™
Run
Simulations
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Handle data efficiently: Ensemble Datastores and Tall Arrays
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❑ 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
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Poll 2
Machine Learning?
Scan me!
https://forms.office.com/r/ZcmYprvLMm
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What’s Machine Learning about?An easy explanation
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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™
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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™
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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
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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
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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
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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
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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
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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
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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
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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™
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1. Import Data
2. Training
3. Result Evaluation
4. Model Refinement
5. Export Code and Results
STEP 2: Train a classification model – Classification Learner
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1. Import Data
2. Training
3. Result Evaluation
4. Model Refinement
5. Export Code and Results
STEP 2: Train a classification model – Classification Learner
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1. Import Data
2. Training
3. Result Evaluation
4. Model Refinement
5. Export Code and Results
STEP 2: Train a classification model – Classification Learner
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1. Import Data
2. Training
3. Result Evaluation
4. Model Refinement
5. Export Code and Results
STEP 2: Train a classification model – Classification Learner
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1. Import Data
2. Training
3. Result Evaluation
4. Model Refinement
5. Export Code and Results
STEP 2: Train a classification model – Classification Learner
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1. Import Data
2. Training
3. Result Evaluation
4. Model Refinement
5. Export Code and Results
STEP 2: Train a classification model – Classification Learner
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❑ 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
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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
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RUL estimation: results
MATLAB App Designer
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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
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64© 2021 The MathWorks, Inc.
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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™
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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.
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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
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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