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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 that back into the process. WAGO Analytics, the Minden-based company, supports users in data acquisition to analysis and provides intuitive visualizations of the dependencies in the systems. The connections that are uncovered are integrated into the processes and make it possible to fully exploit your potential for optimization. In a joint project, WAGO works closely with the customer to develop an individual solution for the profitable use of data for the respective application.

In a joint project, WAGO works closely with the customer to develop an individual solution for the profitable use of data for the respective application.

The Benefits for You:

  • Identifying potential optimization
  • Improving quality levels
  • Tailored analytics solution
  • Increasing efficiency and reducing costs
  • Improving process stability

Six Steps of Data Acquisition
for Profitable Use

1

Acquiring existing machine and sensor data. WAGO offers all the components needed for this, e.g., the WAGO I/O System 750 and PFC Controllers.

2

The data is recorded from the machine and stored in a uniform structure, either in remote storage or a cloud (e.g., WAGO Cloud).

3

Analyzing and illustrating the information hidden in the existing data makes potential applications and connections visible. This gives you a greater understanding of your equipment and its processes.

Acquiring existing machine and sensor data. WAGO offers all the components needed for this, e.g., the WAGO I/O System 750 and PFC Controllers.

The data is recorded from the machine and stored in a uniform structure, either in remote storage or a cloud (e.g., WAGO Cloud).

Analyzing and illustrating the information hidden in the existing data makes potential applications and connections visible. This gives you a greater understanding of your equipment and its processes.

What is necessary for successful implementation?

Hardware

Raw data from various data sources

To capture machine and sensor data, users need different hardware products that provide the corresponding database. WAGO offers a wide range of components for this purpose. The corresponding devices support all standard interfaces as well as established industrial protocols. In addition to the WAGO I/O System 750 with the controllers from the PFC family and various modules for measurement and sensor data acquisition, users have access to IoT Boxes from WAGO for retrofitting. They are incredibly versatile and ideal for simple machine and system connections. The data can be forwarded to a cloud or to an Edge computer. The WAGO product portfolio also covers these areas.

Software Platform

Data Analysis – Centralized or Decentralized

The machine and system data that has been collected can then be used either centrally or decentrally for analysis. The difference to the decentralized approach s that all data is in the cloud and can be accessed at any time from anywhere in the world. By contrast, with the decentralized approach, the machine and system data can be analyzed, for example, in the system directly. This is where the advantages of Docker® technology come into play. The PFC200 Series Controller and the new Edge devices are already Docker®-ready. Modern software and numerous applications can be used in the individual analytics solution.

WAGO Helps You Use
Your Data Profitably

We help you get to your tailored analytics solution.

1. Gathering raw data from various data sources

In the first step, the relevant data sources are identified in exchange with the respective domain expert. The various machine and system interfaces are read out independently of the respective protocol. Values are taken from the controller directly and additional sensors are installed if necessary. The analytics solution should be integrated into the existing controller. Therefore, data is acquired in consultation with the responsible automation engineer.

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 key figures 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, an individual data logger for the machine or system is put into operation. The data is stored and used for in-depth analysis. A meaningful variety of 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

As the fourth step, the exploratory data is analyzed and the right presentation formats selected. In offline analyses, dependencies and relationships are extracted, interpreted and visualized. Rare events are revealed. In close communication between the data scientist and the domain expert, the first potential for optimization becomes clear. Complex algorithms are often unnecessary. However, part of the exploratory data analysis is also to evaluate algorithms from machine learning and AI for different use cases in offline analyses. If the required application cannot be mapped from the existing database, either new sensors are installed or the test plans adapted.

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5. Integration into the operating process

In the fifth step, the analyses and visualizations optimized for the machine or system are integrated into the operating process. Integration into the controller occurs again in consultation with the automation engineer.

6. Use correlations and optimization potentials

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

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COMPLETE ANALYTICS FOR INDUSTRY – FROM DATA COLLECTION TO DATA ANALYSIS

FAQ – Analytics

General Questions

The first step, and the first benefit for you, is quantifying your processes. According to experience, further application possibilities are identified.

We will work out a solution with you to capture the existing database. All relevant protocols and interfaces can be used. If required, WAGO offers a comprehensive portfolio for installing additional sensors.

For us, an analytics project starts with the first data collected. This is followed by a data analysis to quantify your process understanding. Usually, no data records are necessary on your part. If the solution to your problem requires precise recognition of rare events, longer data collection periods are usually needed.

Depending on the application, a local solution (central solution) is possible.

We make individual dashboards available and reporting functions to keep an eye on your analytics application and carry out analyses independently.

The range of proven methods is great. Depending on the use case, algorithms from monitored and unsupervised learning are used. Model-based procedures that integrate expert knowledge are also used. For some use cases, visualizations of the live data also bring added value.

In the first step, it makes sense to focus on a sub-area 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.

Processes and Methods

Data science, machine learning and artificial intelligence are intersecting. WAGO Analytics describes the provision and use of the methods of this intersection in the field of automation technology.

Data analysis grows in your computing time with the amount of data examined. Not every analysis method is therefore suitable for live applications.

The range of proven methods is great. Currently neural networks are receiving a lot of attention. However, these are suitable, for example, only for the recognition of events. This means you can not get a complete picture of your machine or system.

WAGO Analytics describes the integration of your analyses into live operation. On the other hand, statistics mean a study of historical data.

Big Data describes large amounts of data. Data Science involves the analysis of data, which can be Big Data, but can also be the live data of your machine or system. Machine Learning is a class of methods for generating new information from data and is part of Data Science.

Approach and Procedure during the Analysis

First, there is a discussion and evaluation of the use case. Data is recorded and your understanding of the process is quantified. Thereafter, further steps with your process experts will be planned and re-evaluated.

After a discussion and evaluation of the application, commissioning of a data logger of the various data sources and preparation of the data takes place. In offline analyzes, the application possibilities are worked out and evaluated in exchange with your process experts. If you are convinced of the results, an integration into the operating process follows. In further steps, we are available to you as a partner.

Not all data is relevant and consistent. Data cleansing is designed to transform the data into a high consistency and information density format. As a result, no unnecessary data is transported, visualized or forwarded to the data analysis algorithm. This allows for efficient operation of your analytics solution.

Focus on the smallest possible area of your system that you want to investigate. Formulate a concrete question and use your process understanding to assess which data provide important information.

Innovation & Technology

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