Issue link: https://resources.mouser.com/i/1538449
C h a p t e r 3 | S e n s i n g a n d Pe r c e p t i o n AI enables more efficient analysis of the vast volumes of information, helping extract meaningful insights while minimizing the amount of data that must be transmitted and the processing power required throughout the system. The rise of distributed intelligence eases the computational burden by using edge AI to process information close to the sensor itself. Decisions can be made faster when processing is moved to the edge, closer to where the data originate, and the burden on central processors is reduced. Modern sensors are even equipped to process data themselves using technologies like FSMs, machine learning cores (MLCs), and intelligent sensor processing units (ISPUs). With these technologies, sensors execute initial analysis and apply logic to the data before transmission. This creates smart sensors. For complex use cases such as predictive maintenance, data from simple measurements like vibration and distance can help detect early wear and tear. Coupled with vision sensor data, it can be used to analyze failures and inform the maintenance staff to correct the problem." Nguyen (Darrion) Tuan Dat Regional Manager, Smart Factory, SEA Pepperl+Fuchs Groups The Benefits of Smart Sensing Embedding intelligence into the sensor delivers measurable gains. By filtering or summarizing data within the sensor, the volume of data sent to higher-level controllers drops, easing network traffic and saving power and bandwidth for other processes. When decisions are made locally, systems can respond faster. In safety-critical applications, instant reaction makes all the difference. By processing data locally, sensors also conserve energy that would otherwise power continuous data streaming. In applications that rely on battery power, edge AI actually extends operational life while reducing costs. 21 Autonomy Meets Intelligence: Enabling the Future of Factory Automation