Use Case

Machbase solution Architecture

Company Profile

Mando Brose is a company specializing in automotive motors, founded in 2011 as a joint venture between Korea’s Mando Corporation and Germany’s Brose, a global auto parts company.

It supplies ‘brushless electric motors’ for intelligent chassis systems in the global automotive industry and supplies motors for EPS (electric power steering) to major Korean automakers.

Challenges

The company wanted to build a facility status monitoring system by collecting data from production facilities in real time, and needed to collect multiple PLC data for each production line and monitor the status of each line. In addition, alarms and process progress data needed to be communicated through an interface with the existing MES system and displayed on the MES screen.

Reason for Selection

Machbase has Edge Master, an edge computing solution based on a time series database, so we were able to build a data collection infrastructure in a short period of time by simply installing the package without any additional development. We also proposed a solution to prevent data loss even if there is a failure such as a network disconnection because the time series database can be embedded in the edge device to collect and store data in the first place.

They were using Mensch and Mitsubishi PLCs, and Edge Master has its own data collection application that supports those protocols, so it was easy to set up and collect PLC data.

Solution

A total of seven edge devices were built with Machbase time series DB and deployed for each production line. The data collected by the edge devices was automatically transmitted to the central server in real time for storage. Alarms and progress data required by the MES were transmitted from the edge devices through an interface with MS-SQL.

Application Result

With alarms and progress data available in the MES system, process status can be monitored in real time. In addition, facility data can be analyzed in big data, laying the foundation for quality analysis and facility predictive maintenance.