AI-models for Condition Monitoring on Texas Instruments MCUs

Unexpected failure in motors and other kinds of machines can be very costly. Being able to detect when problems arise or when a machine is about to break down allows operators to plan ahead and ensure that the machine only stops at a non-critical time. Machine learning can help in this regard by providing additional, crucial information that allows operators to better understand if the machine is experiencing some issues and/or plan when maintenance needs to be performed.

Utilising machine learning for condition monitoring and fault/anomaly detection was previously thought to only be feasible with the help of cloud computing. Imagimob, together with Texas Instruments, has shown that this can be achieved also on the Edge: Imagimob machine learning models are able to run locally on a MCU board, like the TI Sitara AM243x or AM64x, placed in the vicinity of the machine. This means that the sensor signals from the machine are gathered and then processed directly on the board and the AI-model running on it gives real-time feedback to the operator. Such a setup can be even hooked up to a PLC system to automate, for instance, critical functionalities.

Sign up to get all the content of a demo example of an Imagimob AI-model for condition monitoring to be run on a TI Sitara AM243x board.