iameminmammadov / dash-predictive-maintenanceLinks
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.
☆54Updated 3 years ago
Alternatives and similar repositories for dash-predictive-maintenance
Users that are interested in dash-predictive-maintenance are comparing it to the libraries listed below
Sorting:
- SCADA data pre-processing library for prognostics, health management and fault detection of wind turbines. Successor to https://github.co…☆83Updated 4 years ago
- Wind Turbine Fault Detection. Newer version @ https://github.com/lkev/wtphm☆73Updated 3 years ago
- Deep Learning applied to predictive maintenance use cases☆39Updated 5 years ago
- This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.☆141Updated 4 years ago
- ProFeld: survival analysis, predictive maintenance, churn analysis, and remaining useful life prediction in Python☆22Updated 2 years ago
- Import, clean, and prepare data and conduct machine learning for fault detection in a wind turbine☆17Updated 8 years ago
- In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbo…☆152Updated 3 years ago
- Remaining Useful Life (RUL) prediction for Turbofan Engines☆27Updated 4 years ago
- This is a repository of sample codes and implementation framework for industrial machine predictive maintenance tasks using deep learning…☆30Updated last year
- Machine learning applied to wind turbines incipient fault detection.☆93Updated 4 years ago
- Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as …☆74Updated 4 years ago
- collection of predictive maintenance solutions for NASAs turbofan (CMAPSS) dataset☆138Updated 4 years ago
- Baseline study on the development of predictive maintenance techniques using open data. Two problems are discussed: classifying a vibrati…☆21Updated 4 years ago
- Wind turbine fault detection using one class SVM☆16Updated 3 years ago
- Multiclass bearing fault classification using features learned by a deep neural network.☆36Updated 3 years ago
- remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, con…☆26Updated 4 years ago
- Wind turbine fault prediction using machine learning☆63Updated last week
- This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.☆203Updated 11 months ago
- This project is about predictive maintenance with machine learning. It's a final project of my Computer Science AP degree.☆68Updated 3 years ago
- Code and supplementary material complementing the WES-publication: "Change-point detection in wind turbine SCADA data for robust conditio…☆19Updated 4 years ago
- Code repository for the book 'Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance'☆21Updated last year
- ☆19Updated 4 years ago
- Exploratory Data Analysis of the popular dataset N-CMAPSS (source: https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-…☆14Updated 2 years ago
- Datasets for Predictive Maintenance☆144Updated 7 months ago
- ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification☆29Updated 2 years ago
- This repository contains data and code that implement common machine learning algorithms for machinery condition monitoring task.☆94Updated 11 months ago
- Evolutionary Neural Architecture Search for Remaining Useful Life Prediction☆27Updated 2 years ago
- The objective of the project is to classify steel plates fault into 7 different types. The end goal is to train several machine Learning …☆16Updated 6 years ago
- Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).☆165Updated 2 years ago
- This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Sup…☆26Updated 3 years ago