Machine Failure Prediction

This project aims to build a predictive model to detect machine failures in advance using sensor data. By analyzing readings such as temperature, air quality, RPM, and electrical current, the model identifies patterns that often precede failures. This predictive maintenance approach helps prevent unexpected downtimes, enhancing operational efficiency and reducing costs associated with machine breakdowns. The project leverages historical sensor data to train machine learning models for accurate and timely failure prediction.

Installation guide

Please read install.md for details on how to set up this project.

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── install.md         <- Detailed instructions to set up this project.
├── data
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, eg.
│                         `1.0-jqp-initial-data-exploration`.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
├── requirements.txt   <- The pip requirements file for reproducing the environment.
│
├── test               <- Unit and integration tests for the project.
│   ├── __init__.py
│   └── test_model.py  <- Example of a test script.
│
├── .here              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py           <- Makes project pip installable (pip install -e .)
│                         so machine_failure_prediction can be imported.
│
└── machine_failure_prediction   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes machine_failure_prediction a Python module.
    │
    ├── config.py               <- Store useful variables and configuration.
    │
    ├── dataset.py              <- Scripts to download or generate data.
    │
    ├── features.py             <- Code to create features for modeling.
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models.
    │   └── train.py            <- Code to train models.
    │
    ├── utils                   <- Scripts to help with common tasks.
    │   └── paths.py            <- Helper functions for relative file referencing across the project.        
    │
    └── plots.py                <- Code to create visualizations.

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Project based on the cookiecutter conda data science project template.