A project focused on the improvement for remaining useful life estimation.

bat_vision

This repository holds the results of a project on Remaining Useful Lifetime estimation of a turbofan engine for a course of Delft University of Technology.

The note taking application for the terminal

Predictive Maintenance System for Digital Factory Automation

Awesome Deep Fault Diagnosis

刀具剩余寿命预测

False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics

Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine

Repositorio de trabajo de memoria de titulación: Pronóstico de fallas para mantenimiento predictivo usando metodologías de aprendizaje profundo supervisado.

ASE2306-Capstone Project [2019/20 T3] - Aircraft Engine Lifetime Prediction with Machine Learning

remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, condition-based maintenance, CBM, predictive maintenance, PdM, prognostics health management, PHM

Prediction of Remaining Useful Life (RUL) of NASA Turbofan Jet Engine using libraries such as Numpy, Matplotlib and Pandas. Prediction is done by training a model using Keras (TensorFlow).

Tool wear prediction by residual CNN

Reproduction of the work by Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. Annals of Applied Statistics, 3(2), 857-879.

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.

PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.

Remaining useful life estimation of NASA turbofan jet engines using data driven approaches which include regression models, LSTM neural networks and hybrid model which is combination of VAR with LSTM

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.