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Do you know the value of your machine data?

WAGO Analytics

When it comes to optimizing your own machines or systems, the challenge is usually to improve and quantify process knowledge and to transfer the results back into the process. WAGO Analytics, a Minden-based company, supports users on the path from data acquisition to analysis and provides intuitive visualization of the dependencies within the systems. The connections that are uncovered are integrated into the processes, allowing you to fully exploit the potential for optimization.

In joint projects, WAGO works closely with customers to develop tailored solutions for the profitable data use within the specific application.

The Benefits for You:

  • Identification of optimization potential
  • Improved quality level
  • A tailored analytics solution
  • Greater efficiency and lower costs
  • Improved process stability

Six Steps from Data Acquisition
to Profitable Use

1

Step 1

Step 1: Gathering Raw Data from Various Data Sources

2

Step 2

Step 2: Processing the Data

3

Step 3

Step 3: Continuous Data Acquisition

4

Step 4

Step 4: Explorative Data Analysis and Selection of the Right Representation

5

Step 5

Step 5: Integration into the Operating Process

6

Step 6

Step 6: Leveraging Correlations and Optimization Potential

WAGO Helps You Use
Your Data Profitably

We help you get the tailored analytics solution you need.

1. Gathering Raw Data from Various Data Sources

In the first step, the relevant data sources are identified together with the relevant domain expert. Read-out from the various machine and system interfaces is independent of the specific protocol. Values are accessed from the controller directly, and additional sensors are installed if necessary. The objective is to integrate the analytics solution into the existing controller, so the automation engineering responsible for the system is consulted on the data acquisition setup.

2. Processing the Data

In the second step, the data is time-synchronized. The relevant information is extracted and decoded in a uniform format. Irrelevant data is filtered out and removed. In addition, relevant metrics are calculated on an ongoing basis. This step is particularly important, because a clean database is the basis for the success of an analytics project.

3. Continuous Data Acquisition

Then, in the third step, a custom data logger for the machine or system is put into operation. The data is stored and used for in-depth analysis. A variety of useful data is generated through continuous data acquisition. This can be implemented in the form of test plans, together with the domain expert. Depending on the use case, it may also be sufficient to let the machine run for a longer period of time.

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4. Explorative Data Analysis and Selection of the Right Representation

The fourth step involves exploratory data analysis and selection of the right representations. In offline analyses, dependencies and relationships are extracted, interpreted and visualized. Rare events are revealed. The close cooperation between the data scientist and the domain expert achieves the first successful identification of potential for optimization. Complex algorithms are often unnecessary. However, the exploratory data analysis also involves evaluating algorithms from machine learning and AI for different use cases in offline analyses. If the desired use case cannot be represented with the data from the existing database, either new sensors are installed, or the test plans are adapted.

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5. Integration into the Operating Process

In the fifth step, the analyses and visualizations that have been optimized for the machine or system are integrated into the operating process. Once again, the automation engineer is consulted about the integration into the control system.

6: Leveraging Correlations and Optimization Potential

In the sixth step, the customer exploits the interrelationships and potential for optimization, benefiting from the advantages of a tailored analytics solution. If necessary, the analytics solution can be expanded in a further iteration for the next use case.

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Integrated Analytics for Industry

from Data Acquisition to Data Analysis

FAQ – Analytics

General Questions

The potential offered by analytics is immense. Common applications include optimizing control variables, reducing downtime and minimizing quality and process fluctuations. With tailored approaches, we help you exploit potential for optimization and increase the effectiveness of your processes. Data analytics can also be used to implement new business models.

Working together, we develop a solution to incorporate the existing database. All relevant protocols and interfaces can be used. If necessary, WAGO offers a comprehensive portfolio for installing additional sensors.

Data acquisition and recording are part of an analytics project. Therefore, you can start the project without an existing database. If you already have data, it will be integrated into the evaluation. If the solution to your problem requires precise detection of rare events, longer data collection periods are usually needed. The aim is to generate a useful variety of data. As the amount of data increases, the precision of data analysis increases.

Depending on the application, a local solution (centralized solution) may be possible.

We provide individual dashboards and reporting functions to help you keep an eye on your analytics application and perform analyses on your own.

The range of proven methods is wide. Depending on the use case, supervised or unsupervised learning algorithms may be used. Model-based methods that integrate expert knowledge are also used. For some use cases, visualizations of the live data can also add value.

In the first step, it makes sense to focus on a part of your system where you see potential for optimization. After the results have been successfully integrated into your process, the next use case can then be identified and processed.

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