Tensorflow implementation of a Kalman-DVAE for remaining useful life estimation on the CMAPSS dataset

A PyTorch implimentation of a conditional Dynamical Variational Autoencoder for remaining useful life estimation

A collection of datasets for RUL estimation as Lightning Data Modules.

Conformal Prediction Intervals for Remaining Useful Lifetime Estimation (IJPHM 2023)

Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

The source code of paper: Trend attention fully convolutional network for remaining useful life estimation in the turbofan engine PHM of CMAPSS dataset. Signal selection, Attention mechanism, and Interpretability of deep learning are explored.

A collection of unsupervised domain adaption algorithms for RUL estimation.

Predictive Maintenance project using Python and C-MAPSS NASA Turbofan Engine data.

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)

PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.

collection of predictive maintenance solutions for NASAs turbofan (CMAPSS) dataset

This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.

Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.