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u-mation - automation and digitalisation portfolio

09 April 2019

Weidmüller‘s u-mation automation and digitalisation portfolio combines modular automation hardware, as well as innovative engineering and visualisation tools with sophisticated digitalisation solutions. The portfolio also features smart analytics modules and machine learning modules

The analytics modules enable a detailed evaluation of all relevant machine and process data. Deviations and anomalies can be detected at an early stage during ongoing processes. The machine learning tool provides a future-proof basis for more efficient production concepts. In the latest version, mechanical and plant engineers can drive forward the development of analysis models themselves, without having to be trained data scientists. This ensures that existing knowledge of processes and machinery stays within the company, as engineers can update their domain knowledge themselves. In this way, Weidmüller has made its analytics solutions accessible to conventional mechanical engineers and machine operators. Indeed, with these types of concepts, 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. 

u-mation – industrial analytics and machine learning 
One key factor in ensuring increased efficiency and cost control in a system's life cycle is the profitable usage of machine and process data. Thanks to state-of-the-art sensor systems and digital networking, u-mation is able to extract the relevant measured values and use these to carry out intelligent analyses. The machine learning services are innovative analytics solutions that enable the targeted initiation of maintenance work, thereby keeping unnecessary downtimes to a minimum. Predictive maintenance means that service intervals can be accurately planned in accordance with specific needs. Thanks to the continual monitoring of sensor, status and process data, it is possible to make reliable statements regarding the quality of the products (predictive quality). The analytics modules learn from the machine data and so become increasingly accurate over time. The machine learning models provide a future-proof basis for more efficient production concepts. 

The Weidmüller industrial analytics solution is platform-independent and adapts to the existing conditions wherever it is being used. This means that the solution can be used without any problems if a customer is already using a Microsoft Azure, IBM or AWS infrastructure for data management. Whatever the platform, the Data Scientists at Weidmüller can still work with the collected data. 

The continuing digitalisation of industry is opening up numerous benefits that can help to save both time and money. New business models can also be tapped into as a result of digitalisation. Smart analytics modules allow for the detailed evaluation of all relevant machine and process data, and mean that any deviations and anomalies can be detected early during ongoing processes. This significantly reduces downtimes and the amount of reject parts. The Weidmüller machine learning concepts optimise the performance of machines and systems – simply and individually. As a component of u-mation, the machine learning concept features a number of integrated industrial analytics functions that open up a wide range of possibilities: from the analysis and optimisation of the existing infrastructure to the recording and collation of measured values, right through to the development of intelligent analysis models and data-related services. Machine learning and industrial analytics optimise the entire process chain while simultaneously reducing running costs. 

Visualisation - all relevant data at a glance
Clear visualisation is a key factor for success when it comes to having an overview of all relevant data. The visualisation solution u-view, with associated software u-create visu, allows for a customised layout and flexible adaptation of the recorded data, machine and process workflows. Users can use profiles to select which users receive which information. This is a practical solution that makes it possible to allocate a particular function to the relevant data. 

Anomaly detection - identification and classification of unwanted deviations
The analytics solutions from Weidmüller detect deviations from measured values during ongoing operation. Comparing this data with automatically learned models based on real-time data, anomalies are detected and classified early before they have any impact on the process – these minor deviations are not usually detected by rules-based systems. This enables the user to respond precisely to potential problems before they impact on the performance of the machine or system. 

Anomaly classification - quick error localisation and rectification
With industrial analytics, deviations that are registered by the system are categorised based on their relevance (important and unimportant). The most relevant anomalies are assigned to a cause, which removes the need for time-consuming searches for the source of the error, which in turn significantly reduces downtimes. The result: optimised production output and reduced costs.

Predictive maintenance - optimal, needs-based maintenance planning
The analysis models from Weidmüller learn from the machine data, thereby making it possible to see into the future to a certain extent: With industrial analytics, the maintenance of machines or systems is always planned on an as-needed basis. Maintenance is no longer scheduled based on numbers of units or running times, and there are no reactive repairs, which helps keep servicing and operating costs to a minimum.

Predictive quality - continual product inspection and optimisation
Rejects are an important cost factor in any manufacturing company. The continual monitoring of sensor, status and process data allows for predictions to be made regarding expected product quality even beyond the current production step. Adapting the production parameters accordingly helps to significantly reduce the amount of reject parts, which in turn ensures that maximum revenue is generated from the processes.

For further information visit https://www.u-mation.com


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