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Predictive maintenance is a key pillar of Industry 4.0, allowing for early detection of faults in everything from pumps propelling liquid-cooling systems and motors driving industrial automation to giant turbines for generating power. Traditional forms of condition monitoring rely on centralized processing, where raw sensor data streams into a host system, located in either a gateway or the cloud. While effective, this approach imposes significant constraints on network bandwidth, latency, and power.

Implementing an edge-AI-based sensor with local inference capabilities can help address these challenges by processing data closer to the motor itself. By reducing the communication overhead, these sensors can reduce latency, improve overall system efficiency, and extend battery life.