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Examples

This directory contains examples demonstrating how to use the EXAMPLE-SHM package.

Install the Package from Poetry Build

To install the package built using poetry, follow these steps:

Step 1: Build the Package

poetry build

This will create a .whl file in the dist/ directory, e.g., dist/cp_sens-0.1.0-py3-none-any.whl.

Step 2: Create and Activate a Virtual Environment

python -m venv .venv
source .venv/bin/activate        # On Linux/macOS
.\.venv\Scripts\Activate.ps1     # On Windows

Step 3: Install the Built Package

pip install example_shm-<version>-py3-none-any.whl

Replace <version> with the version number found in the .whl filename. (e.g 0.1.0).

Running the Examples

There are 5 examples.

  • acceleration_readings demonstrates the use of Accelerometer class to extract accelerometer measurements from MQTT data stream.

  • aligning_readings demonstrates the use of Aligner class to collect and align accelerometer measurements from multiple MQTT data streams.

  • run_pyoma demonstrates the use of sys_id with 3 cases:

    1. oma-and-plot: plots natural frequencies.
    2. oma-and-print: prints OMA results to console.
    3. oma-and-publish: publishes OMA results via MQTT to the config given under [sysid] config.

  • mode_tracking demonstrates the use of mode_track with 2 cases:

    1. mode-tracking-with-local-sysid: gets the pyOMA results by runing sysid locally, then runs the mode track.
    2. mode-tracking-with-remote-sysid: gets pyOMA results by subscribing, then runs the mode track.

  • updating_paramteres demonstrates the use of model-update. Gets the mode track output, then uses it to run update model and get updated system parameters.

To run the examples with the default config, use:

python .\src\examples\example.py accelerometers
python .\src\examples\example.py align-readings
python .\src\examples\example.py oma-and-print
python .\src\examples\example.py oma-and-plot
python .\src\examples\example.py oma-and-publish
python .\src\examples\example.py mode-tracking-with-local-sysid
python .\src\examples\example.py mode-tracking-with-remote-sysid
python .\src\examples\example.py model-update-local-sysid
python .\src\examples\example.py model-update-remote-sysid

To run the examples with specified config, use

python .\src\examples\example.py --config .path_to\production.json align-readings

Example,

python .\src\examples\example.py --config .\config\production.json align-readings

Distributed Setup Overview

This explains the setup needed to run the distributed version of the example-shm pipeline.

Machine 1: Edge Layer – Raspberry Pi with Accelerometers

This machine connects to ADXL375 sensors and is responsible for acquiring raw sensor data. It performs calibration and continuously publishes sensor data over MQTT.

Step 1: Run calibration to find sensor offsets

poetry run python src/scripts/find_offset.py

Step 2: Start publishing raw accelerometer data

poetry run python src/scripts/publish_samples.py

Machine 2: Fog Layer – Data Alignment and System Identification

This machine subscribes to MQTT topics from Machine 1. It aligns multi-channel data, runs system identification, and publishes pyOMA results.

Run the aligner and system identification pipeline

poetry run python src/examples/example.py oma-and-publish

Machine 3: Cloud Layer – Mode Tracking and Model Update

This machine subscribes to pyOMA results, performs mode tracking and updates the structural model.

Run mode tracking and model update

poetry run python src/examples/example.py model-update-remote-sysid