{"id":26944917,"url":"https://github.com/ozogxyz/cmapss","last_synced_at":"2025-06-16T05:09:53.491Z","repository":{"id":271780382,"uuid":"585469027","full_name":"ozogxyz/cmapss","owner":"ozogxyz","description":"Novel algorithms to predict Remaining Useful Life (RUL) on NASA’s benchmark dataset, CMAPSS turbofan engine degradation simulation.","archived":false,"fork":false,"pushed_at":"2023-11-08T09:28:46.000Z","size":3798,"stargazers_count":8,"open_issues_count":3,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-04-01T00:44:49.992Z","etag":null,"topics":["deep-learning","forecasting-model","python","pytorch","time-series"],"latest_commit_sha":null,"homepage":"https://ozogxyz.github.io/cmapss","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ozogxyz.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-01-05T08:49:44.000Z","updated_at":"2024-09-02T09:52:15.000Z","dependencies_parsed_at":"2025-01-09T21:12:07.996Z","dependency_job_id":"b849ccaf-4d91-460e-ab85-b01ee5218425","html_url":"https://github.com/ozogxyz/cmapss","commit_stats":null,"previous_names":["ozogxyz/cmapss"],"tags_count":0,"template":false,"template_full_name":"ashleve/lightning-hydra-template","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ozogxyz%2Fcmapss","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ozogxyz%2Fcmapss/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ozogxyz%2Fcmapss/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ozogxyz%2Fcmapss/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ozogxyz","download_url":"https://codeload.github.com/ozogxyz/cmapss/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246866069,"owners_count":20846496,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","forecasting-model","python","pytorch","time-series"],"created_at":"2025-04-02T18:19:23.869Z","updated_at":"2025-04-02T18:19:24.408Z","avatar_url":"https://github.com/ozogxyz.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"______________________________________________________________________\n\n\u003cdiv align=\"center\"\u003e\n\n# Hybrid Deep Learning Algorithms for Multivariate Time Series Forecasting \u003cbr\u003e (NASA C-MAPSS Benchmark)\u003c/br\u003e\n\n\u003ca href=\"https://pytorch.org/get-started/locally/\"\u003e\u003cimg alt=\"PyTorch\" src=\"https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch\u0026logoColor=white\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pytorchlightning.ai/\"\u003e\u003cimg alt=\"Lightning\" src=\"https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning\u0026logoColor=white\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hydra.cc/\"\u003e\u003cimg alt=\"Config: Hydra\" src=\"https://img.shields.io/badge/Config-Hydra-89b8cd\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/ashleve/lightning-hydra-template\"\u003e\u003cimg alt=\"Template\" src=\"https://img.shields.io/badge/-Lightning--Hydra--Template-017F2F?style=flat\u0026logo=github\u0026labelColor=gray\"\u003e\u003c/a\u003e\u003cbr\u003e\n\n\u003c!-- [![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539) --\u003e\n\n\u003c/div\u003e\n\n______________________________________________________________________\n\n## Description\n\nNovel algorithms to predict Remaining Useful Life (RUL) on NASA’s benchmark dataset, CMAPSS turbofan engine degradation simulation.\n\n## Dataset\n\n\u003cdiv align=\"justify\"\u003e\n\nBenchmark source: NASA Intelligent Systems Division: Prognostics Center of Excellence - Prognostic Health Management, Predictive Maintenance of Turbofan Engines.\n\nThe generation of data-driven prognostics models requires the availability of data sets with run-to-failure trajectories. To contribute to the development of these methods, the data set provides a new realistic data set of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic data set builds on the modeling strategy from previous work. The data set was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. The data set has been provided by the NASA Prognostics Center of Excellence (PCoE) in collaboration with ETH Zurich and PARC.\n\n\u003c/div\u003e\n\n[Link to the dataset](https://www.nasa.gov/intelligent-systems-division/)\n\nDownload Mirror: https://phm-datasets.s3.amazonaws.com/NASA/17.+Turbofan+Engine+Degradation+Simulation+Data+Set+2.zip\n\n\u003cdiv align=\"justify\"\u003e\nData Set Citation: M. Chao, C.Kulkarni, K. Goebel and O. Fink (2021). \"Aircraft Engine Run-to-Failure Dataset under real flight conditions\",\nNASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA\n\u003c/div\u003e\n\n______________________________________________________________________\n\n## Project Description\n\n\u003cdiv align=\"justify\"\u003e\n\nThis project uses PyTorch Lightning powered `rul-datasets` to generate data sets. Hydra is an open source powerful utility to configure\nexperiments and generate configuration files. The template has many loggers to choose from including popular MLFlow and Weights and Biases.\n\n\u003c/div\u003e\n\n______________________________________________________________________\n\n## How to run\n\nInstall dependencies\n\n```bash\n# clone project\ngit clone https://github.com/ozogxyz/cmapss\ncd cmapss\n\n# [OPTIONAL] create conda environment\nconda create -n myenv python=3.9\nconda activate myenv\n\n# install pytorch according to instructions\n# https://pytorch.org/get-started/\n\n# install requirements\npip install -r requirements.txt\n\n# rul-datasets library requires the environment variable RUL_DATASETS_DATA_ROOT to be set.\nexport RUL_DATASETS_DATA_ROOT={path-to-project}/data\n```\n\nTrain model with default configuration\n\n```bash\n# train on CPU\npython src/train.py trainer=cpu\n\n# train on GPU\npython src/train.py trainer=gpu\n```\n\nTrain model with chosen experiment configuration from [configs/experiment/](configs/experiment/)\n\n```bash\npython src/train.py experiment=experiment_name.yaml\n```\n\nYou can override any parameter from command line like this\n\n```bash\npython src/train.py trainer.max_epochs=20 datamodule.batch_size=64\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fozogxyz%2Fcmapss","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fozogxyz%2Fcmapss","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fozogxyz%2Fcmapss/lists"}