{"repo":"iameminmammadov/dash-predictive-maintenance","free":true,"listed":false,"github":"https://github.com/iameminmammadov/dash-predictive-maintenance","clone":"git clone https://github.com/iameminmammadov/dash-predictive-maintenance.git","description":"Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.","language":"Python","stars":60,"topics":["rul","wind-turbines","xgboost","predict-failures","dashboard"],"license":null,"category":"dashboards-admin","readme_excerpt":"Predictive Maintenance for Wind Turbines Dashboard Introduction dash-predictive-maintenance is a dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. The data covers periods from May, 2014 to January, 2015. To predict the date when equipment will completely fail (RUL), XGBoost is used. The achieved RMSE error is 0.018534 days, which is highly accurate. Screenshots Built With Dash - Main server and interactive components. Dash DAQ - Styled technical components for industrial applications. XGBoost - Machine Learning model that was used to predict the RUL. The model was fine-tuned with RandomSearch. Requirements Clone this repo and create a clean environment: To activate the virtualenv in UNIX: To activate the virtualenv in Windows: To install the libraries, needed to run this dashboard: To run this app: The app will be run on http://127.0.0.1:8050/. The app is currently running @ https://dash-gallery.plotly.host/dash-turbine-maintenance/","default_branch":null,"files":null,"tree":[],"storefront":"/r/iameminmammadov","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/iameminmammadov/dash-predictive-maintenance/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}