Do You Know the Value of Your Machine Data?

WAGO Analytics

When it comes to optimizing your own machine or system, the challenge is to increase and quantify process understanding and integrate it into the process. WAGO Analytics supports you in data acquisition and analysis and creates intuitive visualizations of the dependencies in your equipment. The connections that are uncovered are integrated into your processes and make it possible to fully exploit your potential for optimization.

Your Data Is Valuable – Seize Its Potential
Do you want to optimize your machine or system? Use your sensors and actuators to get to know your system better. Benefit from the potential of your data.

Your Benefits with WAGO Analytics

  • Identifying potential optimization
  • Improving quality levels
  • Saving energy and resources
  • Increasing efficiency and reducing costs
  • Improving process stability

The Steps from Data Acquisition
to Data Analysis

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What is necessary for successful implementation?

Acquiring Existing Machine and Sensor Data

To capture machine and sensor data, you need different pieces of hardware that can provide you with the appropriate database. WAGO offers you a wide product portfolio of different components. The corresponding WAGO products support all standard interfaces as well as most industrial protocols. In addition to the WAGO I/O System 750, the PFC family of controllers or modules for current measurement, the WAGO IoT Boxes are also available for measurement and sensor data acquisition. The WAGO IoT Box is incredibly versatile and ideal for machine and system connections. WAGO's IoT Box is pre-assembled and can send the first data to WAGO Cloud within minutes.

Data Acquisition from Machines and Systems

WAGO Cloud gives you the option of collecting data from various machines and systems, centralizing it and analyzing it. Furthermore, you can manage and monitor all WAGO controllers – including your data and application – on a PC, in a conference room or on a tablet while traveling. With simple, user-friendly operation, the WAGO Cloud was developed so that people without extensive IT experience can use it. Thanks to the app structure, WAGO Cloud is intuitive to use. Many standard functions such as visualization, remote maintenance and firmware update are already set up. Your WAGO Cloud is ready after just a few mouse clicks.

WAGO Cloud is based on the established Microsoft Azure cloud platform. This has numerous advantages for you: Microsoft Azure is highly scalable in terms of computing power, data storage, transactions, availability and security standards – it represents a future-proof solution.

Data Analysis – Centralized or Decentralized

The collected machine and system data can be used for analysis both centrally and decentrally. To centrally analyze your data, all you need to do is bring the data to a cloud environment and then analyze the data there. The difference to the decentralized approach here is that all data is in the cloud and can be accessed at any time from anywhere in the world. By contrast, with the decentralized solution approach, you can, for example, analyze the machine and system data directly in the system. For example, you can use WAGO's Docker technology to implement the analysis application on the controller. WAGO's PFC200 Series Controllers are already Docker-ready. So you can start using modern software and numerous applications on the PFC200.

WAGO Helps You Use
Your Data Profitably

We help you understand your processes and guide you in successfully performing the following steps:

1. Gathering raw data from various data sources

In the first step, the various interfaces of the machines and systems are read out independently of the respective protocol and values are taken directly from the controller. The data is aggregated in one location.

2. Processing the data

There is time synchronization of the data. The relevant information is extracted and decoded in a uniform format. Irrelevant data is filtered out and removed.

3. Continuous data acquisition

An individual data logger for the machine or system is put into operation. The data is stored and used for in-depth analysis.

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4. Explorative data analysis and selection of the right representation

In offline analyses, dependencies and relationships are extracted, interpreted and visualized. Rare events are revealed. With the involvement of process experts, the application potentials are identified and validated.

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5. Integration into the operating process

The analyses and visualizations optimized for the machine or system are integrated into the operating process. They are now always available and can be operated intuitively.

6. Use correlations and optimization potentials

The analyses available in live operation are now used to exploit optimization potentials. Furthermore, live visualizations allow machine and system operators to verify and expand their understanding of the process as well as to identify further relationships and application potentials of the data.

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FAQ – Analytics

General Questions

Do I need data scientists in my company to perform data analysis of my system?

My system has very few sensors and actuators, what can I do?

Do I always need a cloud?

Which use cases does WAGO Analytics cover?

Are my machine and system data safe?

Is the analysis performed in real time?

How do I monitor my analytics application during operation?

Can I also transfer the analysis to other machines and systems?

Can I also perform independent analyses afterwards?

How much time do I need for an analytics project?

In which cases does WAGO Analytics help me save more than technical changes?

How many data or data records do I need when starting an analytics project?

Does the file size/quantity affect the result and the duration?

How can WAGO Analytics benefit my business?

Processes and Methods

Is WAGO Analytics about data science, machine learning or artificial intelligence?

What restrictions are there?

Which best practices are available in the field of analytics?

What is the difference between data analytics and statistics?

How is Machine Learning different from Big Data and Data Science?

Approach and Procedure during the Analysis

What are the first steps?

How can I imagine an analytics project with my company and WAGO?

Why is data cleansing so important?

How do I choose the right plant for a pilot project?