Sensors Converge 2021

Industries across the world are leveraging IIoT technologies and the resulting transformation is ushering in a new era of how companies run their operations.    However, for companies to realize the full potential of IIoT enablement, they need smarter equipment to make well-informed decisions autonomously and independently.  Over the coming years, smart sensors embedded with the latest advancements in AI technologies will enable companies to extend their IIoT initiatives to cover applications and use-cases that have proven to be elusive using existing IT/OT architectures.

Semiconductor manufacturing requires an unwavering commitment to process reliability and yield. This session will explore how we are using digital twins to model scheduling, visualize factory layout and flow, simulate manufacturing operations, and tailor complex processes that meet both critical production requirements and customer expectations. MES, SPC, and even sensor data is used in simulations and fed back into the real-time systems optimize the manufacturing process.

Digital transformation initiatives often fail due to the neglect of a key piece of information: location-based data on moving things, which make up about 99% of all assets on the shop floor. Precise location data from sources such as UWB, BLE, or GPS can be transformed into unprecedented insights for operational excellence, supply chain resilience, and increased production quality through the implementation of a location-based automation platform. From asset tracking to tool control and yard management, location-based automation is the foundation for higher productivity, flexibility, and sustainability.

Anomaly detection is a hot topic among those working on Industry 4.0 and digital twin initiatives, but its very hard to do well. In this talk, we will discuss some new methods for using sensor data to drive anomaly detection, and illustrate them with a demonstration of Reality AI's new Anomaly Detection system, built with our partner Advantech. We'll discuss: 

• How to select the best sensor 
• Machine learning approaches for anomaly detection 
• Using anomaly detection to bootstrap models for Remaining Useful Life and Condition Monitoring 
• Connectivity and integration challenges