When the AI model runs on the sensor, anomaly detection happens without a cloud round-trip. At IMTS 2026, Tim Gicewicz of TDK SensEI explained how edgeRX collects vibration and temperature data from motors, pumps, and gearboxes,…
This article is part of an awareness campaign. Editorially Independent, Sponsored by TDK SensEI. Manufacturers testing AI agents for maintenance face a dependency most teams overlook: the agent is only as reliable as the prediction feeding…
Sensors, cloud infrastructure, and algorithms keep improving, but the hardest input to capture for any manufacturing AI system is the knowledge held by a maintenance engineer who has been watching, listening to, and repairing the same…
Monitoring one pump with a vibration sensor and catching a failure two weeks early is straightforward. Doing the same thing across hundreds of assets in multiple plants, with different machine types, different maintenance histories, and different…
Manufacturing AI deployments often fail when all intelligence sits in a single layer, either pushed entirely to the cloud or crammed into the sensor level. Intelligence needs to sit where it can operate reliably, respond in…
A predictive maintenance system flags that a motor will fail in three weeks. What happens next usually involves three to four people, spans up to two weeks, and touches the CMMS, ERP, workforce scheduling, and production…
During the "Edge AI: Driving Smarter Machine Health Monitoring for Energy Infrastructure" panel at IIoT World Energy Day 2026, poll results showed that most energy operators are just beginning to explore AI for asset monitoring. Some…
Edge AI enables energy companies to monitor aging brownfield infrastructure by processing sensor data locally, replacing the diagnostic expertise lost as experienced engineers retire. At IIoT World Energy Day, panelists from TDK SensEI, HiveMQ, PrivacyChain, and…