In a distribution center, warehouse or packaging line a bar code reader gets as physically close to a material handling process as possible. Often bar code scanners are reading hundreds of bar codes a minute on parcels that are moving through high speed material handling applications such as in UPS or FedEx facilities. In the framework of Industrial IIoT the bar code scanner is considered on the “edge”. Edge sensors, such as the bar code scanner are generally perceived to have limited processing capability and generally rely on direct integration with automation controllers. However, Leuze (www.leuze.com/), recently launched the BCL 300i barcode scanner that fundamentally contradicts the notion of dumb sensor. By adding multiple communication channels over a single ethernet cable the simple bar code scanner implements three protocols Ethercat/Profinet, web based interface and Cloud connectivity. The Ethercat/Profinet and the web based protocols have been available for quite some time on previous releases of the bar code scanner, however Cloud connectivity is a new addition.
Leuze has implemented the OPC-UA Advanced Message Queuing Protocol (AMQP) on this sensor. Data from the sensor can be moved directly into Microsoft’s Azure Cloud. Initially, my reaction was that Leuze was proposing to move potentially hundreds of bar code scans per minute into Cloud storage using a heavy weight AMQP protocol. Why didn’t the scanner implement the MQTT lightweight protocol and how were they going to handle loss of connection when storage on this device had to be limited? The truth is that the scanner added to its functionality rather than replacing functionality. The information available over the OPC-UA (AMQP) channel is specifically related to sensor health, configuration data, statistical trends on the printed bar code quality and the reader clarity which requires much slower frequency updates. Employing the OPC UA’s recently released (https://opcfoundation.org/news/opc-foundation-news/opc-foundation-announces-support-of-publish-subscribe-for-opc-ua/publish/subscribe) capability allows the sensor to provide a uniform model for distributing data to Microsoft’s Azure Cloud, although it should be viable to connect to other Cloud systems. Once the information from the sensor is in the Cloud, the Microsoft’s Cortana analytics suite can be applied to interpret the data. Using health data from the device can be applied to predictive maintenance algorithms in the facility. Trends in the data will be used to identify dirty lenses or deteriorating printers.
The automation topology has not changed with the introduction of the BCL 300i, but another information dimension has been added that would normally require application development in the automation controller. Cloud connected edge devices such as the BCL 300i allow OEM machine builders and end users to develop predictive maintenance algorithms without having to modify existing automation equipment or even new installations. These edge devices are defying the general assumption that a gateway will always be necessary in the IIoT factory infrastructure.
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About the Author:
Research Director, Discrete Automation, ARC Advisory Group
Sal’s focus areas include General Motion Control, Material Handling, Machine Safeguarding, Computer Numerical Controllers, Robotics, and Servo Drives.