TE Connectivity Case Studies IIC Smart Manufacturing Connectivity for Brown-field Sensors
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IIC Smart Manufacturing Connectivity for Brown-field Sensors

TE Connectivity
IIC Smart Manufacturing Connectivity for Brown-field Sensors - TE Connectivity Industrial IoT Case Study
Functional Applications - Enterprise Resource Planning Systems (ERP)
Infrastructure as a Service (IaaS)
Networks & Connectivity - Gateways
Discrete Manufacturing
Manufacturing System Automation
The discrete manufacturing domain is characterized by a strictly hierarchical structure of the automation systems, commonly referred to as the automation pyramid. Data acquired by a sensor typically flows through an IO-module into a Programmable Logic Controller (PLC) which manages the local real-time control system. As all process data are concentrated in the PLC, re-programming the PLC and thus, implementing interfaces to access these data appear to be the natural choice to transfer them to the IT system. However, for brownfield installations this choice has proven impracticable for the following two reasons:

In brownfield facilities, PLC usually operate within a once-specified environment and are rarely re-programmed. That is why the active staff is often not familiar with the code and lacks of the competence to modify the existing implementation in a reasonable amount of time.

Furthermore, for cost reasons, any PLC was selected to exactly match the requirements of the environment within which it was intended to operate. That is why it cannot be assumed that a PLC will be able to support additional tasks such as communicating data through additional interfaces.
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*This is an IIC testbed currently in progress.*

TE Connectivity, SAP SE

ifm, OPC Foundation

Discrete manufacturing

This testbed implements an alternative solution by substituting IO-modules that connect the sensors with the real-time automation system by a gateway that extracts the sensor data and transfers them to the IT system through an additional communication channel via OPC UA (IEC 62541). This “Y-Gateway” re-uses existing physical connectivity and supports the easy integration of an IO-Link sensor with the IT by using a common device model based on an open standard (the IO Device Description which is based on ISO 15745-1) and thus enables the remote configuration of the sensor. This common device model is implemented through Manufacturing Data Objects in SAP Manufacturing Integration and Intelligence (SAP MII).

This testbed is essentially about implementing a sensor’s virtual representation at platform tier level by

1. A hardware component that establishes a separate OT/IT communication to deliver sensor data to the IT systems and to receive configuration data.
2. The implementation of a common device model based on an open standard that enables the control and manipulation of the physical device from within the IT systems.

This testbed uses open standards for the OT/IT communication, the sensor devices and the common device model:

• IO-Link is standardized as IEC 61131-9:2013 Programmable controllers - Part 9: Single-drop digital communication interface for small sensors and actuators (SDCI).
• OPC UA is standardized within the IEC 62541 OPC Unified Architecture series.
• The IO Device Description (IODD) is based on ISO 15745-1:2003 Industrial automation systems and integration – Open systems application integration framework – Part 1: Generic reference description

As IO-Link is also based on the IODD, there is a consistent device description from the IT to the sensor level – supported through the semantics-independent data transfer provided by OPC UA – that allows for the easy configuration of a sensor and the interoperability with a large range of devices and analytic services.
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[Cost Reduction - Installation]
Retrofit-able hardware solution reduces the costs of the physical installation.
[Efficiency Improvement - Integration]
Definition and implementation of common device model enables the easy integration with IT systems.
[Data Management - Real Time Data]
Easy access to a high volume of near real-time data enables the improvement of current analytics and the development of innovative applications
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