Weidmüller Automated Machine Learning Tool For Machinery And Plant Engineering
With Weidmüller's automated machine learning tool for machinery and plant engineering, users will be able to create and further develop models themselves without having to rely on the assistance of a data scientist or an external cooperation partner.
This ensures that the existing knowledge of processes and machinery stays within the company, as the engineers can update their domain knowledge themselves. The “auto ML tool” is essentially democratising the use of artificial intelligence (AI), as the methods are being made accessible to a wide audience.
The new machine learning tool provides the basis for more efficient production processes and new data-based business models. Within this context, it is no longer particular machine types that are the top selling factors, but instead the availability of the machines or a guaranteed number of parts that can be produced with the machines.
The maximisation of production times, as a result of industrial analytics, constitutes directly measurable added value, which is reflected in a “return on investment” time of just a few months.
The currently available machine learning tools and their features are proving extremely demanding for traditional automation and machinery construction experts, who usually do not have the expertise required to develop corresponding models.
Data analysis and model design are therefore carried out by data scientists. Their expert knowledge is required in order to apply the methods of artificial intelligence or machine learning to the data and to develop models that can recognise anomalies or predict errors, for example.
The data scientist will of course work closely with the mechanical engineer or machine operator to interpret the correlations detected in the data from an engineering perspective.
Weidmüller's automated machine learning software guides the user through the model development process, which reduces complexity and enables the user to focus on his
or her knowledge of machine and process behaviour. Machine and system experts can drive forward the creation and further development of the models themselves without having to be data scientists and without any special knowledge in the field of artificial intelligence.
This ensures that the existing knowledge of processes, machinery and error patterns stays within the company, as the engineers can update their domain knowledge themselves and incorporate it into the model design steps.
The software helps the company to translate and archive the complex application knowledge into a reliable machine learning application. The tool also provides the necessary software components for the implementation of artificial intelligence, meaning that the user does not need to have any special IT expertise to operate the models.
In order to optimally integrate the domain knowledge of machine and process experts but at the same time automate model design steps, “unsupervised” and “supervised” machine learning have been expertly combined.
Unwanted machine behaviour is detected using an anomaly detection process, which is an example of “unsupervised” machine learning. An algorithm learns the typical data patterns of normal machine behaviour based on historical data.
Deviations from these patterns can be identified during runtime. The detected anomalies may be inefficiencies, minor malfunctions or more serious errors.
The system is able to detect errors the very first time they occur, even if these errors were previously completely unknown. In order to then assign any suspicious machine behaviour to a certain (error) class, classification processes are used, which are examples of “supervised” machine learning.
To carry out this assignment, the algorithm needs to have access to a sufficient number of representative examples from the historical data for all of the different classes. The time ranges for the examples must be marked in the data.
If a particular error then occurs again, it will be recognised by the system and correctly assigned to a class straight away based on its typical data pattern. The algorithms can be continually improved using new data and expanded upon to include new error classes.
The corresponding information, such as the error classes, is introduced by the user as part of model creation and model development via a process referred to as “tagging”.
Weidmüller UK
- +44 1162 823470
- http://www.weidmueller.com
- Klippon House, Centurion Court Office Park, Meridian East, Meridian Business Park Leicester, Leicestershire LE19 1TP GB
About us
Be it automobile manufacturing, electricity production or water management - hardly any of today’s industries can do without electronics and electrical connectivity. In this internationalised, technologically changing world, the complexity of requirements is rapidly increasing due to the emergence of new markets. New, more varied challenges have to be overcome, and the solutions to them will not be found in high-tech products alone. Connectivity is the key, whether it involves power, signals and data, demands and solutions or theory and practice. Industrial Connectivity needs connections. And that’s precisely what we stand for.
Our Industries
Our industrial environment is full of connections that need to be connected, controlled and optimised. We are absolutely committed to always providing the best connection possible. This is not only demonstrated in our products but also in the human connections we maintain: We develop solutions in close cooperation with our customers which meet the full measure of all requirements of their particular industrial environment.
Industry 4.0 and Digitalisation
In the light of Industry 4.0, customised, highly flexible and self-controlling production units often still seem to be a vision of the future. As a progressive thinker and trailblazer, Weidmüller already offers concrete solutions that allow producing companies to prepare themselves for the “Industrial Internet of Things” and for safe production control from the Cloud - without the need to modernise their entire range of machinery.
Where we supply to
UK Ireland
Industries we supply to
Chemicals, Energy and Power, Pharmaceutical Cosmetics Toiletries, Water and Wastewater
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