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Introducing Predictive Maintenance Qt World Summit 2016
by
Predictive maintenance The What
3
What is predictive maintenance?
Corrective maintenance • Wait for something to go wrong (spoiler: it will !) • Easiest, but no planning, bad perceived quality
Preventive maintenance • Guess when it will go wrong • Easy planning, extra cost, requires consistent behavior
Predictive maintenance • Be alerted before it goes (too) wrong • Easy planning, optimal interventions
Moving from devices to smart connected devices
4
Is it for me?
Failures are acceptable (for operations & perceived quality) • Corrective maintenance
No budget to work at it or no signs before a failure • Preventive maintenance
Requirements ? It depends!
Failures are easily predicted • Condition based predictive maintenance
Failures are harder to predict • Model based predictive maintenance
5
Predictive maintenance
• For simple cases • Use conditions to trigger an alert
• When motor’s current is above 1A • When CPU temperature is above 80°C • When vibrations occur
+ Easy to implement - Limited
Condition based
6
Predictive maintenance
• Fits the more complex cases • Use a set of data to learn (predict) when a failure will occur
• Machine learning • Supervised learning requires a learning data set • Preferrably experienced engineer or data scientist (or find some books !)
+ Can cover more complex cases - More work to implement and maintain
Model based condition monitoring
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When to plan for it ?
Prototyping & Hardware design • Identify signs occuring before a failure • Integrate the appropriate sensors (luminosity, vibration, temperature, …)
System software architecture • Monitor sensors, notify changes • Create a model manually or train one w/ machine learning • Integrate model and prediction (web API, library or complete solution)
Impact on design
Predictive maintenance The How
9
The right tools
CMMS: big commercial solutions (IBM Maximo, MVP Plant, …) • More or less easy to integrate • Usually best for large scale, complex operations • Less technical knowledge needed
Custom solutions from open tools and technologies (like Qt !)
• Tailored to your context and tools • Requires technical skills
Existing and custom solutions
10
• Connected device reporting usage stats • Statistics driven automated
maintenance: “If… then”
• Allows increased lifespan and uptime • Fixing issues before seeing damages
• Why should we need the cloud ?
• Evolutivity • Connectivity with other services
Basic Predictive maintenance Statistics driven
Nb of cup served
Qty coffee grounded
Qty milk used
Usability
Condition based
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Basic Predictive maintenance Connecting simple tools
DB Web API
Supervision
website
Smart
device
Cloud infrastructure Local devices
Mobile and Desktop
HTTPS
HTTPS
Device • Qt application Web API • ElasticStack (or NodeJS, PHP, …) • Email and/or ticket on event
Supervision website • Jira (or redmine, custom, …)
12
On the device
Monitor QTimer, QThread QFileWatcher
Serialize / Log QJson classes QLoggingCategory, msg handler
Notify HTTPS, AMQP or MQTT
(Qt) Application’s role
void Device::pollSensors() { QFile file("/sys/class/mysensor/value"); […] int value = QString::number(file.readAll()); QNetworkAccessManager manager; QJsonDocument jsonDoc; QJsonObject jsonObject; jsonObject["mysensor"] = value; […] qCDebug(sensorsLogCat) << jsonData; manager.post(QUrl("http://monitor.domain.com"), jsonData); QTimer::singleshot(60*1000, pollSensor); }
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On the Web server
Parse LogStash, NodeJS
Store ElasticSearch, MariaDB (MySQL)
Essential to build a dataset
Alert Watcher, NodeJS Email, Jira, Redmine
Cloud business intelligence
"actions": { "send_email": { "email": { "to": "[email protected]", "subject": "Please check me !", "body": "You should probably check machine {{ctx.payload.hits.0.fields.name}}, something seems wrong on the espresso motor !", "attachments": { "machine_report": { "http": { "content_type": "application/pdf" , "request": {"url": "http://localhost/report[...]} } } }
14
That’s it ! Wait …
Isn’t that just a bunch of « if » ?
15
• Bring in Machine Learning
• Intelligence driven automated maintenance
• Optimized maintenance costs • Self improving solution, efficiency
increases with data consolidation
• How do you do that … ?
Intelligence driven
ML
Advanced Predictive maintenance
Nb of cup served
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Usability
Model based
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The right tools
Choose your Machine learning toolbox • The « good old » way Dedicated tool
• Matlab, R • Machine learning OpenSource frameworks Library
• Shark (C++), Encog (Java), scikit-learn (python) • Machine learning cloud APIs Online
• Google prediction API, Seldon, MS Azure Machine Learning, BigML
Machine Learning
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Advanced Predictive maintenance Architecture
Message
broker
DB
Web API
Technical
backend
Smart
device
Cloud infrastructure Local devices
Mobile and Desktop
Machine
Learning
API
HTTPS
MQTT
Message Broker • AMQP: QAmqp for Qt • RabbitMQ server • Disconnection msg, queues
Machine Learning • MS Machine Learning
18
Advanced Predictive maintenance Learning and testing
In 4 steps • Choose your output metric
• Remaining useful life, failure probability or maintenance needed
• Build a complete dataset of values and failures (hard part !) • Generate a model using Machine learning and test it • Integrate the model in your system
Failure probability
Excel
Call to a Web API
19
Machine learning Dataset & Learning (MS Machine Learning Studio)
Dataset (failure probability) Model for prediction & learning
20
Machine learning Web API and integration
Production model Testing the webservice
21
Going a bit further Full system supervision
• Example with Kibana
• Visual overview • Helps identify visually trends & anomalies
22
A real leverage for a better business Sum up: Added value
And … • Know your users: Predict their preferences, actions • Security: Alert potentially fraudulous actions, from unsual
behavior
+ Equipment lifespan thanks to anticipation + Better uptime and user satisfaction + Optimized maintenance + Possible new services and commercial models
• Plan to integrate sensors • Define the machine learning output • Make sure you can update the prediction
• Enjoy presenting the result to your customers ! • … Put a sensor in that fuel tank !
Key points
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Witekio, System Software Integrator
Technical software expert
Embedded system expert System software
integrator
Automobile &
Navigation
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Medical & Healthcare
Smart Object
Integration
Industry & Energy
Witekio helps customers to develop and integrate all the software layers from the hardware to the cloud
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