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User Guide

This guide outlines configuration and execution of the example-shm application. It provides detailed information on the configuration parameters, command-line options, and execution procedures.

There are two ways to run the package, either directly from the repository code as a developer or as a build package. The latter is described further down in this file in section 2.

1. Run package with developer setup.

Step 1.1 Initiate virtual enviroment with poetry

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

pip install poetry               #Specifically install poetry to your system
    # If you have poetry installed globally
    poetry env activate              # shows the command to activate venv
poetry install                   # installs all required python packages

Step 1.2 Setup configuration file

config/ consist of a set of standard configuration files. production.json.template For general digital twin purposes replay.json.template For recording and replaying data replay_production.json.template For running function blocks with replayed data

Copy the needed config files and remove .template to use them.

Add MQTT connection information, credentials and topics. For production purposes the MQTT topics structure follows the pattern:

cpsens/DAQ_ID/MODULE_ID/CH_ID/PHYSICS/ANALYSIS/DATA_TYPE

Where DATA_TYPE could be metadata or plain data.

Step 1.3 Change settings

There are some settings that can be changed for the specific use case.

  • Sample time This is how many samples should be used for sysid. The value uses the time in minutes and the sample frequency, fs, to calculate the correct ammount of samples.

Inside examples/run_sysid.py, examples/run_mode_clustering.py, examples/run_mode_tracking.py, examples/run_model_update.py the sample time can be changed: number_of_minutes.

  • Parameters Inside method/constants.py the parameters for system identification, mode clustering, mode tracking and model updating can be changed.

  • Model A digital YAFEM model can be added to models/<your_model>. Inside method/constants.py the model paramters can be set together with the paths to the model folder and the model function file.

Step 1.4 Run examples

The following experiments can be run using the package.

  • 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.

  • sysid demonstrates the use of sysid with four cases:

    1. sysid-and-plot: plots natural frequencies.
    2. sysid-and-print: prints sysid output to console.
    3. sysid-and-publish: publishes one set of sysid output via MQTT to the config given under [sysid] config.
    4. live-sysid-and-publish: Continuously publishes sysid output via MQTT to the config given under [sysid] config.

  • Clustering demonstrates the use of clustering with three cases:

    1. clustering-with-local-sysid: gets the sysid output by runing sysid locally, then runs the mode clustering.
    2. clustering-with-remote-sysid: gets sysid output by subscribing, then runs the mode clustering. This is a one time operation.
    3. live-clustering-with-remote-sysid: gets sysid output by subscribing, then runs the mode clustering. This operation runs in loop.
    4. live-clustering-with-remote-sysid-and-publish: gets sysid output by subscribing, then runs the mode clustering. The cluster results are published. This operation runs in loop.

  • mode-tracking demonstrates the use of mode_tracking with three cases:

    1. mode-tracking-with-local-sysid: gets the sysid output by runing sysid locally, then runs mode clustering and mode tracking.
    2. mode-tracking-with-remote-sysid: gets sysid output by subscribing, then runs mode clustering and mode tracking. This is a one time operation.
    3. live-mode-tracking-with-remote-sysid: gets sysid output by subscribing, then runs mode clustering and mode tracking. This operation runs in loop.

  • model-update demonstrates the use of model_update with two cases:

    1. model-update-local-sysid: gets the sysid output, then uses it to run update model and get updated system parameters.
    2. live-model-update-with-remote-sysid: gets the sysid output by subscribing to MQTT topic, then runs mode clustering to run update model and get updated system parameters.
    3. live-model-update-with-remote-clustering: gets the mode clustering output by subscribing to MQTT topic, then uses the mode clustering output to run update model and get updated system parameters.
python .\src\examples\example.py align-readings  # run an experiment with real data (Needs "production.json" Config)

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

2. Install the Package from Poetry Build

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

Step 2.1: Build the Package

poetry build

This will create a .whl file in the dist/ directory, e.g., dist/cp_sens-0.6.1-py3-none-any.whl.

Step 2.2: Create and Activate a Virtual Environment

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

Step 2.3: 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 0.6.1).

Step 2.4: Create Configuration

The package requires a json configuration file and access to MQTT broker. The format of configuration file is,

{
    "sysid": {
        "host": "",
        "port": 0,
        "userId": "",
        "password": "",
        "ClientID": "NOT_NEEDED",
        "QoS": 1,
        "MetadataToSubscribe":["sensors/1/acc/raw/metadata"],
        "TopicsToSubscribe": [
          "sensors/1/acc/raw/data",
          "sensors/2/acc/raw/data",
          "sensors/3/acc/raw/data",
          "sensors/4/acc/raw/data"
        ],
        "TopicsToPublish": ["sensors/1/acc/sysid/data"]
      },

    "mode_cluster": {
      "host": "",
      "port": 0,
      "userId": "",
        "password": "",
      "ClientID": "NOT_NEEDED",
      "QoS": 2,
      "TopicsToSubscribe": ["sensors/1/acc/sysid/data"],
      "TopicsToPublish": ["sensors/1/acc/mode_cluster/data"]
    },

    "model_update": {
      "host": "",
      "port": 0,
      "userId": "",
        "password": "",
      "ClientID": "NOT_NEEDED",
      "QoS": 2,
      "TopicsToSubscribe": ["sensors/1/acc/mode_cluster/data"],
      "TopicsToPublish": ["sensors/1/acc/model_update/data"]
    }
}

Where the MQTT topic structure follows the pattern: PROJECT/CH_ID/PHYSICS/ANALYSIS/DATA_TYPE Where DATA_TYPE could be metadata or plain data.

The file needs to be saved. The application looks for configuration in config/production.json.

Step 2.5: Use

Launch the bridge with the default configuration:

$example-shm

The file needs to be saved. The application looks for configuration in config/production.json. A different configuration can be provided by using

example-shm --config <config-file>

You can find the available experiments by running the program

$example-shm
Usage: example-shm [OPTIONS] COMMAND [ARGS]...

Options:
  --config TEXT  Path to config file
  --help         Show this message and exit.

Commands:
  accelerometers
  align-readings
  clustering-with-local-sysid
  clustering-with-remote-sysid
  live-clustering-with-remote-sysid
  live-clustering-with-remote-sysid-and-publish
  live-mode-tracking-with-remote-sysid
  live-model-update-with-remote-clustering
  live-model-update-with-remote-sysid
  live-sysid-publish
  mode-tracking-with-local-sysid
  mode-tracking-with-remote-sysid
  model-update-with-local-sysid
  sysid-and-plot
  sysid-and-print
  sysid-and-publish

To run the examples with the default config (config/production.json), use:

$example-shm
Usage: example-shm accelerometers

To run the examples with a custom config, use:

$example-shm
Usage: example-shm  --config <config-file> accelerometers

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

Record and replay Record and replay data is also possible. Here a config/replay.json config file must be defined before hand.

Inside record/record.py some parameters must be specified:

config_path = "config/replay.json"
config = load_config(config_path)
MQTT_CONFIG = config["MQTT"]

RECORDINGS_DIR = "record/mqtt_recordings"
FILE_NAME = "recording2.jsonl"

DURATION_SECONDS = 20 # This is how many seconds of data that are recorded

The same goes for record/replay.py:

# MQTT Configuration
CONFIG_PATH = "config/replay.json"

RECORDINGS_DIR = "record/mqtt_recordings"
FILE_NAME = "recording.jsonl"

REPLAY_SPEED = 1  # Multiplier for replay speed

At the bottom the number of times to loop the replay function can be stated: replay_mqtt_messages(loop=10) # Times to loop

The files can then be run with:

poetry run python record/record.py
poetry run python record/replay.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 the pyOMA output.

Run the aligner and system identification pipeline

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

Machine 3: Cloud Layer – Mode Tracking and Model Update

This machine subscribes to the pyOMA output, performs mode clustering and updates the structural model.

Run mode clustering and model update

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