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.

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

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

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

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 datasets for RUL estimation as Lightning Data Modules.

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

A collection of unsupervised domain adaption algorithms for RUL estimation.

Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

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

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

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

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