1© 2019 The MathWorks, Inc.

Mallipohjaisen suunittelun ja koneoppimisen

hyödyntäminen ennakoivassa huollossa

Markus Orpana

Application Engineer

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Key Takeaways

1. Data Analytics in Industrial Automation and Machinery

2. Integrated Matlab/Simulink Platform

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Increasing amount of Data

Increasing amount of

▪ Business Data & Engineering Data

▪ Cheaper sensors, communication

and data storage

▪ Powerful algorithms (e.g. machine

learning, deep learning)

▪ Applications: Machine health

monitoring and predictive

maintenance

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ChallengeReduce waste and machine downtime in plastics

manufacturing plants

SolutionUse MATLAB to develop and deploy monitoring and

predictive maintenance software that uses

machine learning algorithms to predict machine

failures

Results▪ More than 50,000 euros saved per year

▪ Prototype completed in six months

▪ Production software run 24/7

Mondi Implements Statistics-Based Health

Monitoring and Predictive Maintenance for

Manufacturing Processes with Machine

Learning

Link to user story

“MathWorks Consulting’s support

is among the best I’ve seen; the

consultants are fast and

exceptionally knowledgeable.

We’ve already seen a positive

return on investment from cost

savings, and now we have more

budget and time to complete

more machine learning projects

that will provide similar benefits.”

Dr. Michael Kohlert

Mondi

One of Mondi Gronau’s plastic production

machines, which deliver about 18 million tons of

plastic and thin film products annually.

Link to video

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What is Predictive Maintenance?

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Pump - detected

I need help.

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Pump - detected

I need help. One of my

cylinders is blocked. I will

shut down your line in 15

hours

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Fault Classification Algorithms Allow You to Identify the Root

Cause of Anomalous Behavior

▪ Three-phase pump commonly used

for drilling and servicing oil wells

– Three plungers try to ensure a uniform

flow

▪ Condition monitoring to detect:

– Seal leak

– Inlet blockage

– Bearing degradation

Component

Failure

Crankshaft

Outlet

Inlet

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Fault Classification Algorithms Allow You to Identify the Root

Cause of Anomalous Behavior

Component

Failure

Crankshaft

Outlet

Pressure & FlowSensor

Inlet

▪ Three-phase pump commonly used

for drilling and servicing oil wells

– Three plungers try to ensure a uniform

flow

▪ Condition monitoring to detect:

– Seal leak

– Inlet blockage

– Bearing degradation

▪ Identify fault present in system using

only pressure and flow sensor data

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Generate Synthetic Failure Data from Simulink Models if Real

Failure Data is Unavailable

▪ Model failure modes

– Work with domain experts and the data

available

– Vary model parameters or components Simulink Model

Build

model

Inject Failures

Incorporate

failure modes

Generated

Failure Data

Run

simulations

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Workflow -- Predictive Maintenance with Synthetic Data

1. Modelling

2. Data Generation and Pre-processing

3. Feature Extraction

4. Classification

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Simscape Pump Model

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Feature Extraction - Obtaining Distinctive Features

Time Domain Features

Frequency Domain Features

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Feature Extraction

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Classification

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Condition

Monitoring

What have we achieved?

Why is my

machine behaving

abnormally?

Anomaly

Detection

Is my machine

operating

normally?

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Key Takeaways

1. Using simulation model/digital twin for generating synthetic failure data

2. Interactive tools for maintenance/machine learning algorithm development

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Kiitos