{"id":16569179,"url":"https://github.com/wkirgsn/deep-pmsm","last_synced_at":"2025-08-01T18:04:30.207Z","repository":{"id":139572442,"uuid":"175391661","full_name":"wkirgsn/deep-pmsm","owner":"wkirgsn","description":"Estimate intrinsic Permanent Magnet Synchronous Motor temperatures with deep recurrent and convolutional neural networks.","archived":false,"fork":false,"pushed_at":"2019-10-08T09:11:26.000Z","size":617,"stargazers_count":15,"open_issues_count":0,"forks_count":17,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-01T20:45:01.212Z","etag":null,"topics":["bayesian-optimization","convolutional-neural-networks","deep-learning","electric-motor","electric-vehicles","keras","machine-learning","recurrent-neural-networks","regression","temperature-monitoring","time-series"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wkirgsn.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":"2019-03-13T09:47:20.000Z","updated_at":"2024-10-27T14:24:14.000Z","dependencies_parsed_at":null,"dependency_job_id":"dd5c6652-c075-4de1-95b8-6728b006fd8e","html_url":"https://github.com/wkirgsn/deep-pmsm","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/wkirgsn%2Fdeep-pmsm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wkirgsn%2Fdeep-pmsm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wkirgsn%2Fdeep-pmsm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wkirgsn%2Fdeep-pmsm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wkirgsn","download_url":"https://codeload.github.com/wkirgsn/deep-pmsm/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238751363,"owners_count":19524538,"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-optimization","convolutional-neural-networks","deep-learning","electric-motor","electric-vehicles","keras","machine-learning","recurrent-neural-networks","regression","temperature-monitoring","time-series"],"created_at":"2024-10-11T21:12:37.881Z","updated_at":"2025-02-13T23:31:54.131Z","avatar_url":"https://github.com/wkirgsn.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"scheme.png\" width=\"400\" /\u003e\n\u003c/p\u003e\n\n---\n# Disclaimer: This repo has moved to the UPB-LEA organization site!\nGet the up-to-date code here:\nhttps://github.com/upb-lea/deep-pmsm\n---\n**DEEP learning for Permanent Magnet Synchronous Motor temperatures**. This project aims to estimate temperature sequences inside Permanent Magnet Synchronous Motors from given input sequences, that is, \ncurrents, voltages, coolant and ambient temperatures, and torque as well as motor speed.\nAll sensor data is recorded on a testbench.\n\n*Caution: Dataset is anonymized and incomplete in order to meet confidentiality obligations.*\n\n## Getting Started\nIn order to clone this repo and use as a package in your own python projects, proceed as follows:\n```\nuser@pc:~/projects$ git clone git@github.com:wkirgsn/deep-pmsm.git\nuser@pc:~/projects$ cd deep-pmsm\nuser@pc:~/projects/deep-pmsm$ pip install [-e] .\n```\nUse the \"-e\" flag in case you wish to edit the package. \nAfter installing via pip you can simply import this project in python with\n```py\nimport pmsm\n```\nAlternatively, work with this repo directly if you do not intend to import parts of this project into other projects.\n\n### Dataset\nDownload the dataset here:\nhttps://www.kaggle.com/wkirgsn/electric-motor-temperature\n\nYou can also just click [here](https://www.kaggle.com/wkirgsn/electric-motor-temperature/downloads/electric-motor-temperature.zip/2).\nPlace the unzipped .csv file in pmsm/data/input/.\n\n## Structure\nData must be available in *pmsm/data/input* - all results of trainings and \npredictions are stored in *pmsm/data/output*. Specific paths are editable in \n*pmsm/preprocessing/config.py* though. Data formatting is dealt with in *preprocessing/*, while hyper parameter tuning \nis conducted with utilities from *opt/*.\n\nExecutable python files are located in root package folder *pmsm/*.\n\nMost configurations can be adjusted in *pmsm/preprocessing/config.py*.\n\n## Script files\n\n* **hot_{r,s,c}nn.py**\n  + Train a neural network (Recurrent, Self-Normalizing, or Convolutional} with given hyperparameters from config.py\n* **hp_tune_{r,c}nn.py**\n  + Conduct hyperparameter search via Bayesian Optimization with given hyperparameters from config.py\n* **visualize.py**\n  + Visualize performance of a certain model, given its UID.\n* **hp_vis.py**\n  + Visualize results of a certain hyperparameter search, given its UID.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwkirgsn%2Fdeep-pmsm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwkirgsn%2Fdeep-pmsm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwkirgsn%2Fdeep-pmsm/lists"}