Edge computing processes data locally, transforming sensor data into decision-making inference close to the source. It delivers lower latency, higher accuracy, and faster decision-making for real-time industrial analytics at the edge. Embedded monitoring systems comprise sensors, DC/DC converters, and other signal-conditioning circuits, as well as microcontrollers (MCUs) or processors and communication interfaces. Edge computing provides several advantages over a centralized control system: Scalability: Shifting decision-making into distributed nodes close to the machine or system enables easier scalability. Large plants with hundreds of machines face large communications overhead that can be harder to scale with a centralized system. Low latency: Fault detection and a corresponding response can be made more quickly, which is critical in maintaining system robustness and reliability. Continuous, real-time monitoring enables fast detection of abnormal behavior with deterministic control-loop timings. Machine monitoring can involve high-frequency data such as vibration signals, motor currents, and temperature, and continuous transmission of these signals on a centralized industrial network can overload its bandwidth and increase system latency. Greater immunity to network outages: Local decision-making allows the system to be less reliant on the network for fault processing and less dependent on internet connectivity. Offline operation can be maintained for a longer time. Lower-bandwidth networks: Because only high-level information is communicated on the central network, its bandwidth and storage requirements can be much lower. Enhanced privacy and security: By keeping sensitive information at the local device, edge computing can reduce the likelihood of data breaches and cyber threats. Edge AI computing The addition of AI model-based fault detection and inference can greatly enhance the system's robustness. Models can be trained for high-accuracy inference over a wide range of fault conditions. AI models can be run in the cloud or at the edge. Historically, cloud-based AI computing is the more common choice. Larger models need high computing capabilities, restricting their operation to servers. However, edge AI is seeing strong success due to the development of smaller, more efficient models, as well as the increased performance of embedded MCUs and processors. Unlike cloud servers, edge AI hardware runs continuously under harsh environments such as vibration and high temperatures. Hardware selection can involve a tradeoff between speed, low-power capability, size, memory capacity, and computational performance. AI-enabled MCUs have the advantage of deterministic real-time operation that ensures predictable timing for sampling and control loops. Another approach is the use of smart sensors that incorporate AI chips. These devices can monitor sensor data and apply embedded AI algorithms to diagnose and generate fault alerts. AI used in embedded applications is typically based on machine learning (ML). Models are trained to identify patterns and make inferences to accurately detect anomalies. Some common ML models are decision trees, support-vector machines, and random forests. A subset of machine learning is deep learning (DL), in which artificial neural networks are used. These can be used for complex patterns and require greater computational and memory resources. Let's look at two examples of embedded edge AI analytics. Microchip's dsPIC33A DSC for arc fault detection Arc faults can occur from power spikes, insulation degradation in the power transmission path, degradation in semiconductor devices or passive components used for power conversion, or failures in the load. They create safety hazards for personnel and equipment. Arc faults in different applications have their own unique signature, e.g., arcs from an electric-vehicle battery disconnect versus those from a solar inverter. DC power transmission has seen significant