Why Industrial Predictive Maintenance Works Better at the Edge Than the Cloud

Predictive maintenance uses sensor data — such as vibration, temperature, and current draw — to forecast equipment failures before they occur, replacing reactive or schedule-based approaches. A key metric is Remaining Useful Life (RUL) estimation, which predicts how long a component will function before failing. Sending this sensor data to the cloud introduces latency that can be harmless for periodic reports but dangerous when a machine is seconds from failure. Edge computing addresses this by processing data locally at the source, reducing response times, bandwidth costs, and network exposure of sensitive data. However, deploying machine learning models on edge hardware requires significant optimization effort, as these devices operate under tight power and compute constraints compared to data centers.