Skip to content
@survml

SurvML

README.md

Slogan

Welcome to our organization's GitHub repository! I am a PhD candidate and data scientist dedicated to developing machine learning models for survival analysis of equipment. My mission is to advance the state-of-the-art in predicting time-to-failure outcomes using data from various industries, with the ultimate goal of improving reliability and efficiency of equipment.

Recommended by ChatGPT, the pronunciation of SurvML should be: "Based on the combination of "surv" and "ML," the most common pronunciation for "survml" would be "surv-em-el." The "em" sound in "em-el" represents the "M" in "ML," which is short for "machine learning."" But... I mean... No big difference.

What is Survival Analysis?

Survival analysis is a branch of statistics that studies the time until an event of interest occurs, such as death, disease recurrence, or failure of a mechanical system. Survival analysis is commonly used in medical research, engineering, and other fields where the time-to-event outcome is of primary interest.

Our Approach

I focus on developing machine learning models that can accurately predict time-to-failure outcomes from equipment data. My models use a variety of techniques, including deep learning, survival trees, and Cox proportional hazards regression. I also explore novel methods for feature selection and data preprocessing to improve model performance.

Repository Contents

This repository contains code for my machine learning models, as well as scripts for data preprocessing and analysis. I also include sample datasets and Jupyter notebooks to demonstrate how to use my models.

Contributing

I welcome contributions from researchers and data scientists interested in advancing the field of survival analysis for equipment. If you have ideas for improving my models or want to contribute to my codebase, please open an issue or submit a pull request.

Contact

If you have any questions about my work, please contact me at jiaxiang.cheng@outlook.com or jiaxiang002@e.ntu.edu.sg. Thank you for your interest in my research!

Pinned

  1. survml-deepsurv survml-deepsurv Public

    PyTorch implementation of DeepSurv, by Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proporti…

    Python 5

  2. survml-transformer-rul-prediction survml-transformer-rul-prediction Public

    Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful l…

    Python 18 1

  3. survml-lstm-rul-prediction survml-lstm-rul-prediction Public

    PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., &…

    Python 9

  4. survml-deepweisurv survml-deepweisurv Public

    PyTorch implementation of DeepWeiSurv, by Bennis, A., Mouysset, S., & Serrurier, M. (2020, May). Estimation of conditional mixture Weibull distribution with right censored data using neural network…

    Python 1 1

Repositories

Showing 7 of 7 repositories
  • auton-survival Public Forked from autonlab/auton-survival

    Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

    Python 0 MIT 73 0 0 Updated Apr 3, 2023
  • .github Public
    0 0 0 0 Updated Apr 3, 2023
  • survml-lstm-rul-prediction Public

    PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.

    Python 9 Apache-2.0 0 0 0 Updated Jan 7, 2023
  • survml-transformer-rul-prediction Public

    Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

    Python 18 Apache-2.0 1 0 0 Updated Jan 7, 2023
  • survml-about Public

    Organization files

    0 0 0 0 Updated Jan 7, 2023
  • survml-deepweisurv Public

    PyTorch implementation of DeepWeiSurv, by Bennis, A., Mouysset, S., & Serrurier, M. (2020, May). Estimation of conditional mixture Weibull distribution with right censored data using neural network for time-to-event analysis. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 687-698). Springer, Cham.

    Python 1 1 1 0 Updated Jan 4, 2023
  • survml-deepsurv Public

    PyTorch implementation of DeepSurv, by Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18(1), 1-12.

    Python 5 Apache-2.0 0 0 0 Updated Jan 4, 2023

People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages

Loading…

Most used topics

Loading…