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Witekio introducing-predictive-maintenance

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Witekio presented an introduction to predictive maintenance allowed by software systems embedded into smart connected devices. The session covers definitions, when to plan for it, what tools and technologies to choose (existing, custom, machine learning). From basic to advanced predictive maintenance it gives hints about how to do and what choices have to be made.

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Witekio introducing-predictive-maintenance

  1. 1. Introducing Predictive Maintenance Qt World Summit 2016 by
  2. 2. Predictive maintenance The What
  3. 3. 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. 4. 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. 5. 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. 6. 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
  7. 7. 7 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
  8. 8. Predictive maintenance The How
  9. 9. 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. 10. 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
  11. 11. 11 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. 12. 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); }
  13. 13. 13 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": "operator@customer.com", "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. 14. 14 That’s it ! Wait … Isn’t that just a bunch of « if » ?
  15. 15. 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 Qty coffee grounded Qty milk used Usability Model based
  16. 16. 16 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
  17. 17. 17 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. 18. 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. 19. 19 Machine learning Dataset & Learning (MS Machine Learning Studio) Dataset (failure probability) Model for prediction & learning
  20. 20. 20 Machine learning Web API and integration Production model Testing the webservice
  21. 21. 21 Going a bit further Full system supervision • Example with Kibana • Visual overview • Helps identify visually trends & anomalies
  22. 22. 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
  23. 23. • 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
  24. 24. 24 Witekio, System Software Integrator Technical software expert Embedded system expertSystem software integrator Automobile & Navigation Handheld & Mobility Medical & Healthcare Smart Object Integration Industry & Energy Witekio helps customers to develop and integrate all the software layers from the hardware to the cloud
  25. 25. Witekio France 4, chemin du ruisseau 69134, Ecully France Phone : + 33 4 49 26 25 39 sales.emea@witekio.com Witekio USA 3150 Richards Roads Suite 210 Bellevue, WA, 98005, USA Phone : + 1 425 749 4335 sales.amer@witekio.com Witekio Germany Am Wartfeld- 61169 Friedberg, Germany Phone : + 49 6031 693 7070 sales.dach@witekio.com Witekio Asia C/O 14F-3, No. 57, Fuxing Nth Rd, Songshan District, Taipei, 10595, Taiwan Phone : +886 2 2740 0394 sales.asia@witekio.com ©2016 Witekio & Subsidiaries. All Rights Reserved. This document and the information it contains is confidential and remains the property of our company. It may not be copied or communicated to a third party or used for any purpose other than that for which it is supplied without the prior written consent of our company. Thank you Witekio UK/EmbeddedBits Hollywood Mansion, Hollywood Lane, Bristol BS10 7TW, UK Phone : + 44(0) 117 369 0930 sales.uk@witekio.com

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