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.

repo for big data analysis final project （rul prediction + fault classification)

Predictive Maintenance on NASA C-MAPSS dataset using LSTM, GRU, and Transformer architectures.

A repository for predictive maintenance models using XGBoost and Random Forest to estimate the Remaining Useful Life (RUL) of turbofan engines from the NASA CMAPSS dataset, featuring an adaptive sliding window approach for real-time degradation trend analysis, which outperforms fixed-window methods in accuracy.