Few are unfamiliar with AI and machine learning. Implementing predictive maintenance and automatic quality inspection can be huge for your business. But getting started with AI and machine learning requires specific knowledge, skills and a plan. Otherwise, there is a good chance that you will get caught up in an endless web of data and algorithms. Provided it is used correctly, you can predict maintenance and reduce maintenance costs by structuring data well and building the suitable models. How do you start implementing AI and machine learning in an industrial setting? Check out the articles and talk to one of our experts.

Business Development Manager

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From unstructured data to business value

Smarter through patterns

Rotterdam hosts the largest refinery in Europe and therefore also one of the biggest in the world. On the 550-hectare site (1000 football pitches) there are around sixty factories in which oil is processed in all sorts of ways. In addition to the factories, there is also an immense amount of pipelines to transport the oil. In total this involves 160,000 kilometres of pipeline -enough pipe to span the globe four times. To regularly check for rust and wear, among other things, is a colossal challenge. However, this will soon become a lot easier.

### STCA
For a ‘solution’ you’ll have to move north. To Amsterdam to be precise. At a huge campus teams are working dedicated on research and development day and night, including predictive maintenance through the use of AI and Machine Learning. As a result, maintaining the many thousands of kilometres of pipelines is becoming easier, cheaper and safer.

The best way to achieve that is by predicting maintenance using big data. The more and better the data, the better the predictive power of the underlying algorithm and the more reliable the result.

### LINKIT collaboration
Data engineers Anis Boudih and William Geuns from LINKIT have been spending the last 6 months working on various predictive maintenance projects. Due to the increasing interest in AI and Machine Learning, there is always a need for extra talent, which is also the basis for the collaboration with LINKIT. After their first period they are certainly willing to tell something about their special work.
 
### Pipeline traffic light
Anis is the first to talk about the project he is working on: a near real-time dashboard that provides field engineers with useful information based on more than 200 sensors and a statistical model. “We have developed a kind of traffic light dashboard that displays the current situation based on the continuous flow of data. If the process goes beyond certain limits, an email is immediately sent to the engineers. This is then an indication that an engineer must go there for inspection."

### Data tracker
Data tracking might sound like a simple process, but as Anis explains, it’s not all so straight-forward. “Firstly, new data is constantly being received that needs to be processed as quickly as possible. In addition, different processes work side by side in the background, such as a machine learning model that makes a 'prediction' based on the latest data, the database where part of our data is stored as well as the backend of our dashboard. As a result, the development of this product is complex and it is sometimes difficult to discover where something went wrong. In the next phase of the project we want to make everything even better, faster and more accurate so that the field engineers can take action in better time.”

### Photo inspection
William is working on a very different project that requires an introduction. “For as long as they have existed inspectors routinely go along the pipelines to check them for rust, for example, and to check whether the valves still work well enough. This is very labour intensive and because of the enormous amount of pipelines it’s a huge challenge to constantly monitor. What we already have is an algorithm that can independently assess photos taken by the inspector of a section of a pipeline. It detects where rust is and detects common deviations. For the inspector, this works as an aid in case he himself overlooks something."

### Drones
This algorithm was the starting point for a much larger project that William is currently working on. “By taking a lot of photos with drones and having them automatically assessed by an algorithm, we hope to map complete areas and be able to assess them continuously. That is of course a huge job. They are certainly not normal photos either. The idea is to stick them on a virtual 3D model of Moerdijk or Pernis so we can see very precisely where risks arise.”

### Tag a hundred thousand photos
But once you have all the photos you still haven’t nearly solved the problem. Because in order for the algorithm to work properly, it must first be fed with the correct data. In this case, the existing photos that were taken in the past. "We now have more than one hundred thousand photos that all have to be tagged". A monster job where people manually indicate for each photo where the error is. In this manner, you ‘feed’ the algorithm, as it were, with more and better data, making it increasingly smarter and better able to detect errors independently. The algorithm has now reached the point where it can recognize the material in the photo, assess it and display it in multiple layers. The next phase is actually testing."

### Learning how to ‘drive’ 
"You can reasonably compare that phase with how Tesla teaches its cars to drive independently," William explains. "In the beginning it makes all kinds of mistakes and does not ‘see’ things. But by correcting the errors in the data and, above all, by collecting a lot of data, you continue to work towards the point at which the car can truly take to the road independently. The same applies to us. Hopefully at some point our algorithm will become so good that it can independently detect possible integrity problems at sites. That would be a huge step forward."

### Steep learning curve
For William himself it is an exciting project. Especially since he has only been in the field for six months. He attended the LINKIT boot camp in 2019, after which he started working as a data engineer. “In six months my knowledge of data engineering has increased enormously. A very steep learning curve in Backend development, Python, Cloud (Azure and AWS), Docker, Scrum, you name it! I am still learning every day and as this project enters new phases, I am faced with new, educational challenges every time. An ideal start to my career as a data engineer!”

*Do you want to know more about the ‘how’ of implementing AI and machine learning in an industrial environment? You can read all about it in our whitepaper ‘From unstructured data tot business value for manufacturing’. Or you can ask one of our experts for more information.*


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Creating a Predictive Maintenance solution in the world of pipelines

Automated search for a needle in a haystack 

From unstructured data to business value for manufacturing

The largest refinery in Europe is located in Rotterdam, making it one of the largest in the world. On the 550 hectare site (1000 football fields) there are about sixty factories in which oil is processed in all kinds of ways. Besides the factories, there is also an immense amount of pipelines to transport the products. In total, this involves 160,000 kilometers of pipeline, which is four times around the earth. To check it regularly for rust and wear, among other things, is an enormous challenge. And artificial intelligence and machine learning offer an answer to that challange. Predictive maintenance using AI and ML should ensure that the maintenance of those many thousands of kilometers of pipeline must become easier, cheaper and safer.

Automated search for a needle in a haystack

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Transavia

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ASML

Our clients

Every factory is obliged to perform regular quality inspections. On one hand that’s because of legislation and ISO-norms. On the other, it’s because you simply need to in order to stay operational -especially when working with delicate production like food or plastics. For every factory, it’s a nightmare scenario when items have to be withdrawn from shops or even from consumers, because of poor production. Quality inspection is key in avoiding this, and with AI combined with machine learning you can make this inspection more robust.

### Sample inspection

There are many ways how to perform quality inspections. For instance, one of the basic ways is sample inspection. With this method, you select at random e a few pieces of chicken, fries, metal plates or car toys and see if they satisfy the requirements.

### Costly way of inspection

This method has it’s merits. If there is a fault in the production there is a fair chance you will find this by doing a sample test. However, once you’ve discovered it, you probably have to discard the whole production from the last test on. This is not only costly because a lot of production can’t be sold, but also storing and / or demolishing the production is a pricey affair.

### How can automated quality inspection make a difference?

With real-time quality inspection based on AI and machine learning, this can be hugely improved. It works as followed:

- According to the type of production, it will be inspected with camera’s, infrared light and/or other sensors.
- Trained on your historical data an algorithm will be able to spot defects on your production line in real-time
- Once this is discovered, the whole production line Will be automatically shut down, so your production line will not deliver defected or damaged goods anymore.

### Fourth industrial revolution

Industrialists are calling the use of AI and machine learning the fourth industrial revolution. It radically changes the way in which factories are producing goods. It increases the need for digital know-how and ICT and reduces the need for low skilled labour.

### Not easy to implement

Because of this ‘revolution’, it can be difficult to find the right people to do this job. Next to this it is a relatively young field which is still in rapid development. And finally, it’s not always easy to implement real-time quality inspection in such a way that it really works and adds value.

### Setting the data right

An algorithm needs data of both good and poor production to be able to decide what’s right and wrong. However, over 99 percent of everything you gather is probably good quality. Data of poor production is scarce and needs a specific approach to make it useable. To make it worse, it can also easily be polluted, leading the algorithm to give a distorted outcome.

### Building IT together

Implementing real-time quality inspection the right way is like crafting a Swiss watch. This means you will need a ‘Swiss craftsman’ to design, plan and implement it to make real-time quality inspection work for your situation at your company. At LINKIT we follow a pragmatic approach in which we try to cooperate with your business staff and IT-department as much as possible. Combined with our own ‘eco-system’ of over 1000 highly experienced IT-experts and our agile way of working, this will result in straight forward solutions with maximum flexibility.

*Do you want to know more about implementing automated quality inspection in your factory? And how it is done? You can read it in our free to download whitepaper. Or call one of our experts for more information.*


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How real-time quality inspection can make a huge difference 

With the introduction of industrial IoT, a production line nowadays is far more than just a mechanical piece of engineering. Having sensors to measure temperature, humidity, or sometimes even an infrared scanner, a manufacturing machine suddenly becomes a source of big data. Maybe this is not your first thought when you were considering buying a new machine, but having a strategy in place to use your machine’s data can be vital for the decisions you make in the end.

### Why your data counts

Gathering data within manufacturing is important for many reasons. The most crucial aspect however is the ability to analyse it for predictive maintenance. A joint study by Emerson and Wall Street Journal found that unplanned downtime costs industrial manufacturers an estimated $50 Billion per year. Equipment failure is the cause of 42% of this unplanned downtime.

### Increased productivity

There is a lot to gain in this field. Predictive maintenance is by far the best option to reduce costs even further. Of course, situations are widely different, but on average, introducing predictive maintenance can increase your productivity by an estimated 30 percent.

### Human inspection

Currently, many factories rely on manual human-machine inspection to keep their expensive machinery up and working. Though this is a good thing to do, it has its limits. Human inspection is always a snapshot and only catches so much of the complete situation. Furthermore, good inspection personnel is scarce and it is pricey to carry out.

### Benefits predictive maintenance

Automated predictive maintenance is in many ways the opposite. It’s real-time and based on the history of your data it can predict when and which part of the machine needs inspection. This will not only reduce the amount of downtime but also extends the lifespan of the machine since it reduces the chance of a breakdown.

### Difficulties

As simple as it may sound, implementing predictive maintenance is definitely not easy. There are several reasons:

- No factory is the same. Every kind of production needs it own specified approach. This means that ground expertise is vital in finding the right solution.
- Data can easily be ‘polluted’. Even the slightest changes in your equipment surroundings can mean a big difference to the algorithm that currently collects data.
- In order to provide an accurate prediction, you need a lot of (historical) data

### Strategy before buying

This third point is exactly the reason why you have to consider predictive maintenance before you start buying a new machine or setting up a new production line.

For example: what are you going to do with all the data you gather? Saving data about temperature or humidity might sound trivial, but it’s not. When a sensor measures something every 5 seconds, and does this 24 hour a day, a machine sensor can easily count up to two or three gigabytes per day. Are you going to store all of this? And where? And is this really necessary?

Even just to answer these questions it is important to at least consider the option of predictive maintenance beforehand. Even if you are not going to use it right now, it’s important to save the data when you want to introduce it later on. Also making sure you are collecting ‘clean’ data is something you should ensure right from the start.

*At LINKIT we know all about predictive maintenance and the choices that are coming with it. So, if you need advice to setup a data strategy for your machinery, you can always get in touch with one or our experts for more information. Or download our free whitepaper to discover more about increasing your business value by introducing predictive maintenance and/or quality inspection.*


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Hurray, a new production line is ready! But what about my data?

By now we’ve all heard about AI and machine learning. Many already know the huge benefits that come with implementing predictive maintenance and automated product inspection. But we still need to be careful upon entering, since we are still navigating a place with little rules and many unknowns, but with huge potential. Welcome to the wild west of Artificial Intelligence.

### Forget about logic 

In almost every case IT follows a specific order. If you fill in this, or do this, then this will happen, appear or move. AI works completely different. Forget the logic, AI revolves around patterns. Investigate this and see what happens. If you’ll find a pattern, you may have gold. But if not, you have to try again.

### What are you trying to solve?

This trial and error way of working can be highly labour intensive and therefore expensive. In order to make the life of our data experts easier, it is really important for them to have the right specifications. What kind of problem are you trying to solve? What do you want to achieve? And, very importantly, is AI really the answer you need?

### Just do something, ok?

If a data expert will start working with only your requirements it often goes wrong. This is like your boss asking you to write a story. Just a story. Of course you will be delighted to write him or her a story, but in order to do so you need the right information. What should be the subject? How long should it be. What style should it be? And so on. Without that it’s almost impossible to achieve the right result. The same counts for successfully implementing an AI solution for your factory or industry.

### In order to increase your change of success, there are a few tips that may help.

- Ask yourself the right questions. Is AI really what you need? Are the potential benefits worth enough to risk implementing AI? Like in the real wild west there are no guaranties for success. At least not at the beginning.
- Work iterative. Start with your strategy and a concept, then, continue with creating a Proof of Concept to check if your plans are realistic (trial and error) and only then push the button and go for an MVP (Minimum Viable Product).
- To lower your risk even further the data team should work as closely as possible with the (key) users.

### Close collaboration is key

To explain this last point: at LINKIT we use the slogan ‘building it together’. This is especially important when implementing an AI solution. Remember the part about just writing a story. With AI there are always numerous factors which have to be taken in account. And to make it even more difficult, they are different for each situation. Things like weather, lighting, machine specifications and even how people are using them will seriously effect the outcome of the underlying algorithm. 

Getting all the information you need is very important as a data scientist or engineer. Therefore, close collaboration with machine operators, among others, is key for achieving the right result.

*Do you want to know more about the ‘how’ of implementing AI and machine learning in an industrial environment? You can read all about it in our whitepaper ‘From unstructured data tot business value for manufacturing’. Or you can ask one of our experts for more information.*



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How to implement Artificial Intelligence in your environment? Discover why factors like iterative development and involving the end-user paramount to success!

Welcome to the wild west world of Artificial Intelligence

Articles to get you started

Principal Data Solutions Engineer

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Well done! Your curiosity led you to get a sneak-peak into the exciting expeditions of the LINKIT Data Engineering & AI-squad! Subscribe to our Talent-community below and receive quarterly updates on who’s who, learn more about our goals, projects, and expertise, and discover how LINKIT enables you to become the ultimate explorer.

Like what you see? Check our upcoming events to meet us face to face or contact [thijs-jan.dijk@linkit.nl](mailto:thijs-jan.dijk@linkit.nl) to find out if you're Explorer material!

Data Engineer

Data engineering & AI

Hey, Data & AI explorer!

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Our AI & ML heroes

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IT in manufacturing: a technological and cultural shift 

Manufacturing

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Get value out of your data with the help of a cloud platform

Cloud data platform

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LINKIT v3.0 - Event versie EN met subs.mp4

Manage your Internet of Things devices in a blink

Managing IoT

Business themes and markets

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Axual

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Azure

Solution Advisor data

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On September 14 & 15 september we will join this external event at Jaarbeurs Utrecht. This is a day full of great Data & AI presentations and more.

**Big Data Expo:**
“Big Data Expo is het eerste platform waar vraag en aanbod uit de big datasector elkaar ontmoeten. Het is het enige evenement in de Benelux dat zich puur en alleen richt op alle facetten van datamanagement. Daarom kunnen wij met trots zeggen dat dit hét event is als het gaat om big data.

Wij focussen enkel op het inzetten en laten renderen van big data. Je vindt exposanten die elke dag weer, op verschillende niveaus, met producten en diensten op het gebied van collecteren, genereren, analyseren, optimaliseren en/ of toepassen van big data bezig zijn. Dit zorgt er voor dat we diepgaande informatie kunnen bieden."

**LINKIT @Big Data Expo**
Wij delen deze dag graag ook onze kennis, volg ons in de volgende sessie:

**Journey to the cloud: Best practices for Data and AI Scaling within Enterprise**  
Deze werk sessie wordt gegeven door *Alyona Galyeva*, Principal AI & Data Engineer.

Follow the cloud journey with us.

*Short-haul flight*  
We depart from basic analytics and arrive at predictive maintenance
Learn how to harness the power of data, give data analysts an opportunity of making valuable dashboards, support data scientists with necessary tools for amplifying predictive maintenance with AI. All of this to prevent UGT (unplanned ground time) which leads to huge cost savings.

*Long-haul flight*  
We depart from basic advanced analytics and arrive at boosting operational decisions making
Learn how to scale Data and AI efforts to tackle the most complex decisions for fleet, crew, ground services for each flight almost near real-time. All of this to save costs, improve customer experience and innovate.

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Improving quality and preventing costs by using the power of data

Hogere kwaliteit en lagere kosten door de kracht van data

AI & ML Engineering

Knowledge is the key ingredient for success

Big Data Expo

Solution Advisor

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Op 15 september zijn we aanwezig bij dit externe event georganiseerd door AG Connect. Dit is een dag vol toffe Low Code en No Code presentaties en werk sessies. 

**AG Connect:**
"Low code en no code stellen meer mensen in de organisatie in staat om eigen apps te bouwen, wat de druk op IT kan verlagen. Bovendien kunnen apps met low code en no code sneller ontwikkeld worden. Belangrijk is wel dat er goed wordt nagedacht over de plek van de technologie in het IT-landschap. Een nauwere samenwerking tussen business en IT is daarbij noodzakelijk."

**LINKIT @LCNCD**
Wij delen deze dag graag ook onze kennis, volg ons in de volgende sessies:

**Een complex engineerings- en calculatieproces in een gebruiksvriendelijke applicatie: van design sprint naar low code app**  
Deze sessie wordt je aangeboden door onze experts;* Jonathan Wigmans*, UX Designer & *Maaike Stoops*, Solution Advisor

Bij de ontwikkeling of verduurzaming van een gebouw komt veel kijken. Welke systemen komen erin, en in welke combinatie? Miljoenen combinaties van mogelijkheden maken het ontwerp en de configuratie een tijdrovende en foutgevoelige klus. ModuleMakers klopte daarom bij LINKIT aan voor de automatisering van hun engineeringsproces. In een gezamenlijk team ontwikkelden zij de tool die het hele traject, van product engineer tot salesafdeling, stroomlijnt. Een design sprint gaf de nodige inzichten en bood het ideale startpunt om met behulp van low-codeplatform OutSystems in 10 weken een prototype en applicatie te ontwikkelen, die aansloot bij de behoefte van de gebruikers en die het engineeringsproces 20x sneller maakte.

Maaike Stoops en Jonathan Wigmans vertellen over de uitdagingen in dit project, de learnings van de gekozen aanpak en waarom de combinatie van design en low code zorgt voor ijzersterke en razendsnelle oplossingen voor (op het eerste gezicht) complexe processen. Je leert hoe je in een week van business probleem naar gevalideerd prototype komt.


**Supercharged app development met OutSystems**  
Deze werk sessie wordt gegeven door *Mohammed Ezzarfani*, OutSystems Developer  
Bouw een to-do app in 30 minuten

In deze demosessie laten we je ervaren hoe makkelijk en snel je apps kan bouwen in OutSystems, met weinig tot geen kennis over softwareontwikkeling. Als demonstratie van hoe eenvoudig dit wordt gedaan, gaan we samen met jouw input een ‘To Do’-app maken, waar allerlei taken worden toegevoegd en afgerond. Dit laat dus zien hoe je snel en simpel kan innoveren. Jij bepaalt samen met ons het resultaat van deze app. De sessie is interactief en we bouwen samen op basis van jouw input. De kennis die je tijdens deze demo opdoet, kun je zelf meenemen naar jouw bedrijf en laat een deel van het potentieel van OutSystems zien.

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Deliver 50% faster results in application development compared with traditional development

Applicaties ontwikkelen in 50% van de tijd ten opzichte van traditionele methoden 

Low-code

Van design sprint naar low code app

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Creating business value through design thinking

Toegevoegde waarde creëren met design thinking

Design thinking

Low Code No Code Day

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Op 23 & 24 september zijn we aanwezig bij dit externe event georganiseerd door Mendix. Dit is een dag vol toffe Low Code presentaties en werk sessies. 

**Mendix:**
"MxHacks 2022 daagt de Mendix-makersgemeenschap uit om oplossingen te bouwen voor non-profitorganisaties die een positieve impact hebben in de wereld. Stel je team van vier samen en bereid je voor op de grootste low-code hackathon ter wereld, die plaatsvindt in Boston, Londen, Rotterdam en Singapore, of op afstand vanuit je eigen geheime schuilplaats voor hackers."

**LINKIT @Mx Hacks**
Meer informatie volgt snel!

Join the Hack

Mx Hacks

Director OutSystems

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Op 29 september zijn we aanwezig bij dit externe event georganiseerd door AG Connect. Dit is een dag vol toffe presentaties en werk sessies.

**AG Connect:**
"AG TechFest is het nieuwe zakelijke tech-festival van Nederland, waar innovatie, nieuwe inzichten en creativiteit samenkomen. Een inspirerend event voor ambitieuze IT-professionals."

**LINKIT @AG Tech Fest**
Wij delen deze dag graag ook onze kennis, volg ons in de volgende sessies:

**Supercharged app development met OutSystems**  
Deze werk sessie wordt gegeven door *Mohammed Ezzarfani*, OutSystems Developer  
Bouw een to-do app in 30 minuten

In deze demosessie laten we je ervaren hoe makkelijk en snel je apps kan bouwen in OutSystems, met weinig tot geen kennis over softwareontwikkeling. Als demonstratie van hoe eenvoudig dit wordt gedaan, gaan we samen met jouw input een ‘To Do’-app maken, waar allerlei taken worden toegevoegd en afgerond. Dit laat dus zien hoe je snel en simpel kan innoveren. Jij bepaalt samen met ons het resultaat van deze app. De sessie is interactief en we bouwen samen op basis van jouw input. De kennis die je tijdens deze demo opdoet, kun je zelf meenemen naar jouw bedrijf en laat een deel van het potentieel van OutSystems zien.


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A cost-efficient and smart supply chain 

Kostenbesparing en slimmer op de plek van bestemming met IT

Logistics & Transport

Logistiek & Transport

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