Phenomenological model

Python implementation of phenomenological bearing model. See docs/procedure.md for an high level explanation of what the code is doing.

Usage

• Edit the simulation_properties.yml file to change the simulation properties.
• See the make_example_dataset.py file in notebooks directory for example usage of generating an entire dataset
• See the html file generated by make_plots_that_illustrate_functionality.py to understand the various parameters that govern the model response.

You can also obtain a simple signal by running making small changes to the default properties:

pbd = PyBearingDataset()

non_default_properties = {"t_duration": 1,
                        "n_measurements": 1,
                        "measurement_noise_standard_deviation": 0.0,
                        "fault_type": "outer"}

m = pbd.make_measurements_for_condition(properties_to_modify=non_default_properties)

This produces a dictionary with different fault modes, different fault severities. For each fault mode and severity, a numpy array is produced where each row is a measurement and each column is a time point.

Installation

• clone the repository from Github: git clone https://github.com/DouwMarx/phenomenological_model.git

• create a new environment using conda or virtual environment: conda create --name phenomenological_model

• Install the packaged required to run the code: pip install -r requirements.txt

Installing the local python package in a different environment (example)

pip install -e ~/projects/PhD/code/phenomenological_model/

If the above installation procedure does not work, you should be OK with an environment with the following packages: - numpy - scipy - plotly - tqdm - pyyaml