Custom Solutions for AI, Machine Learning and Data Monitoring
WAGO Analytics

When it comes to optimizing a system, the challenges lie in improving and quantifying knowledge of the process and incorporating the results back into the process. WAGO Analytics supports you from data acquisition to analysis and creates intuitive visualizations of dependencies in the systems. The interrelationships it uncovers can be incorporated into the processes using AI and machine learning, allowing you to exploit potential for optimization.

In joint projects, WAGO works closely with customers to develop tailored solutions to make profitable use of data 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

Easy Integration of Machine Learning for Predictive Maintenance

Go to the full video

Six Steps from Data Acquisition
to Profitable Use

MM-32100 _Iso_Grafik_Analytics_Process_6_Steps_B_2000x1125.jpg

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. The various interfaces are read out independently of the respective protocol. Values are accessed from the controller directly, and additional sensors are installed if necessary. The analytics solution is integrated into the existing control system, 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

In the following third step, an custom data logger 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 run the data recording over a longer period of time.

4. Explorative Data Analysis and Selection of the Right Representation

The fourth step involves exploratory data analysis and selection of the right forms of representation. In offline analyses, dependencies and relationships are extracted, interpreted and visualized. Rare events are revealed. A close cooperation between the data scientist and domain expert identifies initial areas with optimization potential. However, exploratory data analysis 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.

5. Integration into the Operating Process

In the fifth step, the AI and machine learning algorithms and visualizations that have been optimized for the 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.

Starter Kits: The Easy Way to Get Started