Erdre

Erroneous data repair for Industry 4.0.

This is a project using DVC for setting up a flexible and robust pipeline for machine learning experiments.

For a quick look at an example result, go to Evalutation.

Installation and setup

Developed using Python3.8. You can install the required modules by creating a virtual environment and install the requirements.txt-file (run these commands from the main folder):

mkdir venv
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

As an alternative you can install the required modules by running:

pip3 install numpy pandas pandas_profiling matplotlib tensorflow sklearn plotly pyyaml dvc

To get a plot of the neural network architecture, the following software needs to be installed: Graphviz.

Add data

The data files should be placed in the folder assets/data/raw/. The scripts look for .csv-files, so all .csv-files placed in this folder will be considered as a part of the data set. If one or several of the files are not in the expected format (and have the expected columns), the scripts will most likely return an error message.

Example:

assets/
├── data/
|   └── raw/
|       ├── experiment_01.csv
|       ├── experiment_02.csv
|       ├── experiment_03.csv
|       ├── ...
|       ├── experiment_17.csv
|       ├── experiment_18.csv
|       └── README.md
├── metrics/
├── plots/
└── profiling/

If you want to keep data in separate subfolders, make a subfolder in Erdre/assets/data/raw and enter the subfolder name as the parameter featurize.raw_subfolder in params.yaml.

Example with a subfolder called experiment1:

assets/
├── data/
|   └── raw/
|       ├── experiment1/
|       |   ├── experiment_01.csv
|       |   ├── experiment_02.csv
|       |   ├── experiment_03.csv
|       |   ├── ...
|       |   ├── experiment_17.csv
|       |   └── experiment_18.csv
|       └─── README.md
├── metrics/
├── plots/
└── profiling/

And then set the subfolder name in params.yaml:

...

featurize:
  raw_subfolder: experiment1
  features:
    - ...
    - ...

...

Usage

Running experiments

Run experiments by executing this command on the main directory:

dvc repro

On some systems, dvc cannot be run as a standalone command, in which case you will need to run:

python3 -m dvc repro

To run single stages of the pipeline, run:

dvc repro [STAGE NAME]

For example:

dvc repro profile     # will only run the profiling stage
dvc repro featurize   # will only run the featurize stage

Adjusting parameters

Adjust the parameters in params.yaml to customize the machine learning model.

• featurize.features: List of the features you want to use as input to the model. By default these are the names of the columns in the data files. See available features below.
• featurize.target: What the target variable should be.
• split.train_split: Fraction of data set to use for training.
• scale.method: Which scaling method to use.
• sequentialize.hist_size: How many time steps of input to use for prediction.
• sequentialize.target_size: How large sequence (number of time steps) to predict per input sample.
• train.net: What type of neural network to use.
• train.n_epochs: Number of training epochs.
• train.batch_size: Batch size per epoch.
• train.kernel_size: Kernel size (only applicable when convolutional layers are used).

Evaluation

When the trained model is evaluated, the program produces two plots that visualizes the predictions on the test set. These plots are placed on the folder assets/plots/, and show the following data:

• prediction.html: The true target values compared to the predicted target values. Only the first value of each target sequence is used in the plot, and all of this values are connected and plotted as a line. The features used in the model are also plotted.

• prediction_individuals.html: Individual predicted target sequences are plotted against the true values. The predicted sequences are shown in different colors to easily distinguish between them. Only a subset of the predicted target sequences are shown, in order to avoid overlapping and make the plot easier to interpret.

Available features

Raw features:

• X1_ActualPosition: actual x position of part (mm)
• X1_ActualVelocity: actual x velocity of part (mm/s)
• X1_ActualAcceleration: actual x acceleration of part (mm/s/s)
• X1_CommandPosition: reference x position of part (mm)
• X1_CommandVelocity: reference x velocity of part (mm/s)
• X1_CommandAcceleration: reference x acceleration of part (mm/s/s)
• X1_CurrentFeedback: current (A)
• X1_DCBusVoltage: voltage (V)
• X1_OutputCurrent: current (A)
• X1_OutputVoltage: voltage (V)
• X1_OutputPower: power (kW)
• Y1_ActualPosition: actual y position of part (mm)
• Y1_ActualVelocity: actual y velocity of part (mm/s)
• Y1_ActualAcceleration: actual y acceleration of part (mm/s/s)
• Y1_CommandPosition: reference y position of part (mm)
• Y1_CommandVelocity: reference y velocity of part (mm/s)
• Y1_CommandAcceleration: reference y acceleration of part (mm/s/s)
• Y1_CurrentFeedback: current (A)
• Y1_DCBusVoltage: voltage (V)
• Y1_OutputCurrent: current (A)
• Y1_OutputVoltage: voltage (V)
• Y1_OutputPower: power (kW)
• Z1_ActualPosition: actual z position of part (mm)
• Z1_ActualVelocity: actual z velocity of part (mm/s)
• Z1_ActualAcceleration: actual z acceleration of part (mm/s/s)
• Z1_CommandPosition: reference z position of part (mm)
• Z1_CommandVelocity: reference z velocity of part (mm/s)
• Z1_CommandAcceleration: reference z acceleration of part (mm/s/s)
• Z1_CurrentFeedback: current (A)
• Z1_DCBusVoltage: voltage (V)
• Z1_OutputCurrent: current (A)
• Z1_OutputVoltage: voltage (V)
• S1_ActualPosition: actual position of spindle (mm)
• S1_ActualVelocity: actual velocity of spindle (mm/s)
• S1_ActualAcceleration: actual acceleration of spindle (mm/s/s)
• S1_CommandPosition: reference position of spindle (mm)
• S1_CommandVelocity: reference velocity of spindle (mm/s)
• S1_CommandAcceleration: reference acceleration of spindle (mm/s/s)
• S1_CurrentFeedback: current (A)
• S1_DCBusVoltage: voltage (V)
• S1_OutputCurrent: current (A)
• S1_OutputVoltage: voltage (V)
• S1_OutputPower: current (A)
• S1_SystemInertia: torque inertia (kg*m^2)
• M1_CURRENT_PROGRAM_NUMBER: number the program is listed under on the CNC
• M1_sequence_number: line of G-code being executed
• M1_CURRENT_FEEDRATE: instantaneous feed rate of spindle