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sts.components.contact.mr.placeholder Sebastian Webel
Mr. Sebastian Webel

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Pictures of the Future
The Magazine for Research and Innovation
 

The Future of Manufacturing — Inside Siemens’ Labs

From Semantic Technology to More Efficient Factories

Cloud-based remote condition monitoring and data analytic solutions developed by Siemens Corporate Technology are helping a major Chinese producer of automotive assembly lines to automatically generate documentation and configuration files from semantic models.

A major Chinese producer of automotive assembly lines is testing a new Siemens technology that automatically generates documentation and configuration files from a semantic model, thus opening the door to a significant reduction in engineering time and faster deployment of cloud-based data analytic solutions.

At Siemens Corporate Technology, experts are putting the Internet of Things (IoT) within reach of local small and medium enterprises. Dr. Yuan Yong, a senior key research scientist at Siemens China, is leading a group of researchers and engineers who are using semantic technology to add meaning and context to data. This greatly simplifies the deployment of data acquisition systems for cloud-based remote condition monitoring.

A pilot project with Miracle Automation Engineering, a leading manufacturer of automotive assembly lines in Wuxi, a major city near Shanghai, is now providing valuable test results for this technology.

Over the last few years, Miracle has installed automotive assembly lines in factories all over China and thus also provides maintenance service for these lines. The company is therefore highly interested in remotely monitoring the status of its critical components in order to be able to replace worn or degraded parts before they fail and cause expensive downtime and production losses. Cloud-based remote condition monitoring and data analytic solutions developed by Siemens Corporate Technology can address Miracle’s needs and help them provide better service to their customers.

Bringing the Internet of Things (IoT) within reach of local, small and medium enterprises, Dr. Yuan Yong (left) and Zhang Haitao are using semantic technology to add meaning and context to data.

Approach for Reducing the Engineering Time

In cooperation with Siemens’ Digital Factory Division, Dr. Yuan and his team are studying how to get relevant data from the field to the cloud in an efficient, consistent and user-friendly way.

“The biggest challenge encountered in the Miracle project lies in the engineering effort required for deploying and configuring a suitable data acquisition system based on existing hardware and software components,” says Yuan, who explains that it can take an experienced engineer several weeks to lay out a system structure, map sensors to I/O ports, set up a communication network, configure hardware and software components, and document the assignment of variable names to individual mechanical components in an assembly line.

Yuan and his team are studying how to get relevant data from the field to the cloud in an efficient, consistent and user-friendly way.

Semantic technology – managing data together with a standardized description of its meaning and context – is Dr. Yuan’s approach for reducing the engineering time needed to meet this challenge. A modeling tool developed by his team allows even inexperienced engineers to conveniently input a single logical description – the semantic model - of an assembly line and its data acquisition points. “Instead of editing cryptic parameters in countless configuration files and copying address and variable definitions between Excel sheets, drawings and engineering tools,” says Yuan, “all the necessary configuration and documentation files can be automatically generated and deployed from the semantic model.”  

Moreover, once the data has been collected and sent to the cloud, the semantic model can be the basis for accessing the data by context and for performing automatic root cause analysis of complex defects.The usefulness of the modeling tool is now being investigated by a major automotive manufacturer in China that recently installed an assembly line from Miracle. Dr. Yuan and his team are confident that their tool will be able to significantly reduce engineering time and help Miracle improve and grow its service business.

Yuan Yong und Matthias Lampe