Industrial device monitoring and predictive maintenance are becoming crucial aspects of industries relying on heavy machinery and equipment. Predictive maintenance, in particular, has emerged as a critical approach to optimizing maintenance strategies and minimizing costly equipment failures. By leveraging IoT sensors and machine learning on the edge, the landscape of predictive maintenance has undergone significant transformation. This shift allows for real-time data collection, analysis, and decision-making at the edge, enabling faster response times and proactive maintenance actions. Unlike traditional predictive maintenance techniques, which often rely on periodic inspections or time-based schedules, this new framework takes advantage of continuous monitoring, anomaly detection, and predictive analytics to anticipate and even prevent equipment failures, resulting in increased operational efficiency, reduced downtime, and significant cost savings. The market for IoT devices dedicated to predictive maintenance faces a gap of efficient devices that are able to collect multiple streams of sensor data, enable efficient data analysis and incorporate decision-making capabilities all in one. Edge Impulse has partnered with ReLoc to design our first industrial reference design device - the BrickML. BrickML is small form-factor device powered by a Renesas RA6M5, designed specifically to operate in industrial environments.Documentation Index
Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt
Use this file to discover all available pages before exploring further.
Installing dependencies
To start using the BrickML with the Edge Impulse studio, no additional software is required. Simply install the Edge Impulse CLI, create a project on the Edge Impulse Studio, and you’re ready to go.Connecting to Edge Impulse
1. Connect your Brick to the daemon
Connect the BrickML to your computer and start the Edge Impulse daemon from a command prompt or terminal:--clean.
If prompted to select a device, choose BRICKML:
2. Choose your project
Once logged in, the wizard will ask which project the device should be connected to. From this list, choose the project that you created in step one.3. Connect to the Studio
After the project is selected, the daemon will update to let you know that the connection is successful. Enter a name for your device at the prompt, and your device is now connected to the studio. The devices tab in your project on the studio will also indicate successful connection of the BrickML with a green indicator. You can now start collecting your data.\
Next steps: building a machine learning model
With everything set up you can now build your first machine learning model with these tutorials: Looking to connect different sensors? The data forwarder lets you easily send data from any sensor into Edge Impulse. Predictive maintenance powered by IoT sensors and machine learning on the edge, has become a game-changer, empowering businesses to embrace a more proactive and precise approach to asset management. BrickML is an all-in-one approach to predictive maintenance, empowering organizations with accurate insights and enabling proactive asset management.Deploying back to device
Build with Docker
Note: Docker build can be done with MacOs, Windows10 & Windows11 and Linux machines with x86_64 architecture only.If you are building with Docker, you will need to have Docker Desktop installed. You will need to do this is you want to build a wrapper application around your BrickML project while taking advantage of the Edge Impulse provided ingestion and inference libraries.
- Run the Docker Desktop executable, or start the docker daemon from a terminal as shown below:
- From the BrickML firmware directory build the docker container
- Build the firmware as follows and flash your device with your application (as described below)
ei_uploader.py script as follows:
firmware-brickml.bin.signed by default.
The data sheet for the BrickML can be found here:
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