{"id":18753534,"url":"https://github.com/lucadellalib/bayesian-deep-rul","last_synced_at":"2025-04-13T00:31:52.139Z","repository":{"id":67677950,"uuid":"216925432","full_name":"lucadellalib/bayesian-deep-rul","owner":"lucadellalib","description":"Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components","archived":false,"fork":false,"pushed_at":"2022-04-26T11:01:59.000Z","size":5665,"stargazers_count":15,"open_issues_count":1,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-26T18:57:25.989Z","etag":null,"topics":["bayesian-deep-learning","c-mapss","condition-based-maintenance","neural-network","remaining-useful-life","uncertainty"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lucadellalib.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2019-10-22T23:06:11.000Z","updated_at":"2025-03-14T02:01:45.000Z","dependencies_parsed_at":"2023-03-27T21:00:23.463Z","dependency_job_id":null,"html_url":"https://github.com/lucadellalib/bayesian-deep-rul","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucadellalib%2Fbayesian-deep-rul","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucadellalib%2Fbayesian-deep-rul/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucadellalib%2Fbayesian-deep-rul/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucadellalib%2Fbayesian-deep-rul/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lucadellalib","download_url":"https://codeload.github.com/lucadellalib/bayesian-deep-rul/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248650590,"owners_count":21139670,"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":["bayesian-deep-learning","c-mapss","condition-based-maintenance","neural-network","remaining-useful-life","uncertainty"],"created_at":"2024-11-07T17:26:09.864Z","updated_at":"2025-04-13T00:31:52.123Z","avatar_url":"https://github.com/lucadellalib.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components\n\nBayesian and frequentist deep learning models for remaining useful life (RUL) estimation are evaluated on simulated run-to-failure data. Implemented in PyTorch, developed and tested on Ubuntu 18.04 LTS. All the experiments were run on a publicly available Google Compute Engine Deep Learning VM instance with 2 vCPUs, 13 GB RAM, 1 NVIDIA Tesla K80 GPU and *PyTorch 1.2 + fast.ai 1.0 (CUDA 10.0)* framework.\n\n---------------------------------------------------------------------------------------------------------\n\n## Requirements\n\nAnaconda Python \u003e= 3.6.4 (see https://www.anaconda.com/distribution/)\n\n---------------------------------------------------------------------------------------------------------\n\n## Installation\n\nClone or download the repository, open a terminal in the root directory and run the following commands:\n\n```conda env create -f environment.yml```\n\n```conda activate bayesian-deep-rul```\n\nNow the virtual environment *bayesian-deep-rul* is active. To deactivate it, run:\n\n```conda deactivate```\n\nWhen you do not need it anymore, run the following command to remove it:\n\n```conda remove --name bayesian-deep-rul --all```\n\n---------------------------------------------------------------------------------------------------------\n\n## Dataset\n\nThe models were tested on the four simulated turbofan engine degradation subsets in the publicly available *Commercial Modular Aero-Propulsion System Simulation* (C-MAPSS) dataset. Check *datasets/CMAPSS/README.md* for instructions on how to download the dataset.\n\n---------------------------------------------------------------------------------------------------------\n\n## Usage\n\nOpen a terminal in the root directory, activate the virtual environment and run one of the following commands:\n\n*   `sh train.sh` to train the selected model. Parameters can be modified by editing *train.sh*\n\n*   `sh evaluate.sh` to evaluate the selected model. Parameters can be modified by editing *evaluate.sh*\n\n*   `sh run_experiments.sh` to replicate the experiments on the C-MAPSS dataset\n\n---------------------------------------------------------------------------------------------------------\n\n## TensorBoard\n\nOpen a terminal in the root directory, activate the virtual environment and run `tensorboard --logdir .` to monitor the training process with TensorBoard. If you are training on a remote server, connect through SSH and forward a port from the remote server to your local computer (`gcloud compute ssh \u003cyour-vm-name\u003e --zone=\u003cyour-vm-zone\u003e -- -L 6006:localhost:6006` on a Google Compute Engine Deep Learning VM instance).\n\n---------------------------------------------------------------------------------------------------------\n\n## Results\n\nTraining and evaluation logs of the experimental results are provided for verification. Run *results/results.ipynb* in Jupyter Notebook to check the results by yourself.\n\n---------------------------------------------------------------------------------------------------------\n\n## Contact\n\nluca.dellalib@gmail.com\n\n---------------------------------------------------------------------------------------------------------\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucadellalib%2Fbayesian-deep-rul","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucadellalib%2Fbayesian-deep-rul","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucadellalib%2Fbayesian-deep-rul/lists"}