2ndSightAI is developing an analytics platform that uses position, velocity, and current from existing servo systems to help identify changes in machine behavior and support maintenance investigations.
Illustrative image, AI-generated.
Q-Connect and the cloud workflow are in development. The diagrams below illustrate the planned system.
In development
Q-Connect is being developed to read position, velocity, and current through supported servo service or configuration interfaces. Planned edge processing combines existing 2ndSight analytics with micro-clustering of incoming PVC data.
Planned workflow
The planned Q-Connect workflow will send selected analytics, cluster summaries, and device status to the 2ndSightAI cloud platform using MQTT over TLS.
Dashboard in development
The cloud dashboard is being developed to flag machines with anomalous trends to help engineers investigate potential issues before they happen.
The existing 2ndSight foundation uses patented 3D position–velocity–current modeling to identify deviations from learned motion behavior. We plan to extend it with edge stream clustering on Q-Connect, using compact micro-clusters to summarize incoming PVC data and compare motion patterns in their operating context.
Q-Connect is being designed to work with servo drives from multiple manufacturers through their service or configuration interfaces. Compatibility will be confirmed for each drive model and available data interface.
The planned workflow reads position, velocity, and current through supported service or configuration interfaces.
RS-232 · RS-485 · USB · EthernetPlanned edge analytics and micro-clustering of streaming PVC data.
In developmentThe planned cloud workflow receives selected analytics, cluster summaries, and device status from the edge.
In development AWS · MQTT over TLSOrganize devices, inspect models, review health trends, and investigate changes.
In developmentPlanned data path. Q-Connect is designed to use service or configuration interfaces; it does not tap the EtherCAT or CANopen motion-control bus. Each drive model requires integration and validation. Available signals, sampling rate, and timing determine which analyses are practical.
We are bringing together motion engineering, machine learning, and cloud software to develop a practical motion analytics platform. Join the founding team or help shape the product through a pilot discussion.