I am working on an Industrial Predictive Maintenance application, where the goal is to classify healthy vs faulty machine vibration conditions using data collected from the Modulino Movement Sensor (accelerometer-based vibration source).
I have already completed the following steps:
Collected vibration/motion data using the Modulino Movement Sensor
Uploaded the dataset to Edge Impulse
Trained the machine learning model
Generated the deployment file: model.eim
However, I am facing issues with the procedure to upload or integrate the .eim file into the Industrial Predictive Maintenance application environment. The documentation does not clearly specify:
Where to place or upload the .eim file within the application structure
Whether the model should be integrated through
File system upload
Backend/cloud interface
Local configuration screen
Or compiled directly into the application/firmware
Any required directory path, model naming format, or version compatibility
Whether the application expects sensor parameters (sampling rate, axis orientation, scale) to match the Modulino Movement Sensor configuration
Any necessary post-upload steps like model initialization, restart, or sensor-to-model binding
Hi @marcpous ,Thanks for the quick updates— yes, I followed the same upload procedure. There’s just a slight difference on my side because I’m working with a time-series project:
I copied the Real-Time Time Series application from App Lab as my base.
I placed the generated .eim model file in the project and referenced it via app.yaml.
I updated the model variable to point to my asset by changing:
C
#define EI_MOTION_DETECTION_MODEL ei_model_bytes
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(or the equivalent) to my time-series model symbol. name: Vibration monitoring
description: "Vibration monitoring for industrial health and fault detection "
ports: []
bricks:
arduino:video_object_detection: {
variables: {
EI_MOTION_DETECTION_MODEL: /home/arduino/.arduino-bricks/ei-models/<name of your model>.eim
}
}