MSPM0 SDK edge AI examples: how can a bearing model be evaluated on another machine?

Match the input pipeline first: sensor scale, axes, sample rate, window length and preprocessing. A model receives numbers with a particular meaning. Changing the accelerometer mounting or acquisition bandwidth can change that meaning even if the input tensor has the expected dimensions.

Build an evaluation set from the intended operating conditions and split data by run or machine where appropriate. Randomly splitting overlapping windows from the same recording can make test performance look stronger than deployment performance. Report missed faults and false alarms separately.

Measure memory, execution time and acquisition continuity on the selected MSPM0 device. Treat the example as a starting point for condition monitoring, with decisions reviewed against measured evidence. A successful inference call or a high confidence score does not validate the diagnosis.