A practical approach for Industry 4.0

Industry 4.0 translates into several components, of which the platform to analyze and manage your data is the key to a good solution. A good platform makes it possible to translate your data into insights and useful information for your operations. This can go from monitoring to production planning, there are many use cases you can think about.

Now, the question is not, what is a good platform? The main question is how you go from your raw data to usable information. This is the road that you will have to go to come to the correct solution for your IIoT challenge. The platform plays a role but is only part of the solution.

How to Build your Industry 4.0 solution?

In these modern times, it is all about data and the way to transform this data into information. Information is also the key concept of Industry 4.0 and the use of this information to improve your processes, whether production or administrative.

The steps to go from data collection to value delivery are:

  • Collecting data (from plc’s, different databases, applications, logfiles…) from heterogeneous sources
  • Normalizing and enriching data (integration of data sources)
  • Storing data in a time series database either locally or in the cloud
  • Processing / analyzing large sets of data points in real time and build custom AI models for the data
  • Visualize the outcome in an easy-to-understand way

Data collection

This might seem the easiest step, but often it is a question of finding the best way to collect the data. This can be direct from the IIoT devices or via the means of gateways or via standard communication platforms like OPC (Open Platform Communications) or OPC UA. If the data is available via a database, the data can also be collected from those sources.

Data transformation

When data comes from one source or multiple sources, it might be that the data is not directly usable in its original form. Sometimes data is delivered in a format that must be transformed to be usable, other times the data is delivered as individual records of a string of data patterns and so on. Due to the nature of the data and the different data sources, it might be that data must be normalized. It might also be that some data needs to be enriched to be usable. This step in the process is the data transformation stage. The content stays the same, formats are adapted, and data is readied for storage and usage.

Storing the data

Storing the data can be done in the cloud or on-site, depending on the capabilities permitting the transfer of the data to the cloud. Depending on the nature of the data, we prefer to store most data in time series databases. As the name implies, a time series database (TSDB) makes it possible to add, process, and track massive quantities of real-time data with lightning speed and precision efficiently and continuously. A time series database stores data as pairs of time(s) and value(s). By storing data this way, it makes it easy to analyze time series, or a sequence of points recorded in order over time.

The internet of things (IoT) concept and its associated sensors that constantly collect and stream data underlie several modern workloads such as powering industrial applications, predicting sales demand, analyzing temperature readings, and providing medical information from wearable devices. As you can imagine, the data produced is staggering... and a time series database is more adapted to this kind of data.

Analyzing the data

Data analysis happens more and more using Data models and Artificial Intelligence. Data analytics and artificial intelligence make it possible to link data to gain insights on processes, grow the business, and optimize the speed and quality of production.

Data are necessary to feed algorithms, but you need to be able to transform any data you collect into useful information, otherwise it is more likely to just waste resources and add even more complexity. A useful process to draw causal inference from big data could be divided into the following stages:

  • Find interesting patterns in the data
  • Explain those patterns (possibly using experimental manipulation to understand the link between cause and effects)
  • Use those patterns and explanations.

Using those stages, we build models that can provide the necessary insight in the delivered data and provide the information required to do the job.

Visualize the information

Once the result is known, we visualize it. We use different kinds of dashboards to present the information in a format that is ready to use in an industrial environment. This can go from simple dashboards to complex presentations of the information to use.

What are the next steps?

Once you know how to do it, you either need to find a platform that can help you build your case, or you can look for a partner that can assist you through the process. With a background in monitoring and collections of large data sets, we believe that we have the necessary knowledge and expertise to help you in the following domains:

Data services

  • Collect your data (from heterogeneous sources)
  • Normalize your data
  • Store your data either on-site or in the cloud
  • Analyze your data using statistical and AI models
  • Visualize your information

AI services

  • We build your custom AI models
  • We train, maintain, and improve our AI models
  • We provide monitoring, alerting, and reporting

Integration services

  • Integrated monitoring of OT/IT infrastructure and applications
  • Own time-based visualisation platform and agent technology for real time data capture, automatic discovery, alerting & notifications, and local actions

When you need help, we are here to assist you with our expert team.

Interested? Contact Us