Introducing Predictive Maintenance Qt World Summit 2016

by

Predictive maintenance The What

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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

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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

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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

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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

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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

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• 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

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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, …)

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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": "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[...]} } } }

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That’s it ! Wait …

Isn’t that just a bunch of « if » ?

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• 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

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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

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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

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Machine learning Dataset & Learning (MS Machine Learning Studio)

Dataset (failure probability) Model for prediction & learning

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Machine learning Web API and integration

Production model Testing the webservice

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Going a bit further Full system supervision

• Example with Kibana

• Visual overview • Helps identify visually trends & anomalies

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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

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Navigation

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Integration

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Witekio helps customers to develop and integrate all the software layers from the hardware to the cloud

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