Analog Devices, Inc’s (ADI) will demonstrate the precision of RADAR sensing technology it obtained with its recent acquisition of Symeo; and the application of Machine Learning (ML) techniques to vibration analysis and machine health monitoring. Visitors to the ADI stand, number H23 in Hall 9, will see the breadth of the company’s offerings for industrial automation applications, spanning front-end sensors to analog and digital signal processing to system software. ADI application engineers will be on hand to answer engineers’ questions about industrial automation, including topics centered on the Industrial Internet of Things (IIoT), and the Smart Factory of the future.
Featured demonstrations at Analog Devices’ stand will include Software-defined I/O. ADI will exhibit its comprehensive coverage of software-defined I/O technology that provides equipment OEMs and customers with a flexible means of bridging the gap between an Ethernet infrastructure and real-world signals ‘at the edge’.
Smart and highly accurate distance measurement and object detection by Symeo (now an Analog Devices company) will feature the latest solution using RADAR technology to provide millimetre-accurate ranging as well as parameters such as velocity and acceleration. The demonstration will illustrate how the solution is applicable across sectors ranging from machine control and process monitoring through to transportation and logistics, providing users with reliable measurements in the harshest environments.
Motion Control for Industry 4.0
Employing a latest-generation, industry-standard programmable logic controller (PLC), tightly coupled to a motion controller via ADI’s real-time, multi-protocol network – in this case, implementing EtherCAT. This servo/robotics demonstration will show bidirectional flow of motion - commands in one direction and reporting of operating parameters to the logic controller.
Condition Monitoring in the Smart Factory
Condition-based monitoring is expected to be a cornerstone of Industry 4.0. ADI will show how its comprehensive range of technologies can be used to build a complete condition-monitoring solution. The demonstration will show vibration data from an electric motor being processed in real time, with results displayed on a PC. An advanced MEMS-based sensor captures the data, which is analyzed using FFT signal processing running on an ultra-low-power, ARM-core-based ADI microcontroller. The data is networked wirelessly to the host PC via the robust, field-proven SmartMesh radio platform.
Contactless Adaptive Condition Monitoring
Machine Learning is attracting great interest within the industry. This demonstration of an analysis system based on profiling captured sounds, will illustrate how the technique can enhance condition-monitoring solutions by learning what is normal, identifying anomalies and spotting developing problems. The technology has potential applications in all forms of equipment monitoring, and beyond, extending to functions such as detection of anomalous events in the Smart City. Visitors will be shown ML’s ability to detect transient signals and how its adaptive learning capability can identify meaningful events.
Smoke and Aerosol Detection
Airborne particle and aerosol detection is the subject of this novel sensing demonstration. Employing ADI technology that integrates an LED detector with an analog front-end, the solution enables air quality/contamination monitoring, including the long-sought capability of reliably differentiating smoke (particulate) from steam (aerosol).
Security: Trusted Data from Sensor to Cloud
Industry 4.0 and the Industrial IoT depend on robust security and trusted data. Identity for IoT devices is traditionally achieved by generation of a unique, individual key value. The core management problem of this approach is how to generate a large number of these unique private keys, inject them into devices and keep them secured throughout the life cycle of the device. ADI will demonstrate how its SiOMetrics platform addresses this issue, resulting in a more secure supply chain.