c-mapss

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

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 and sensor data generation by VAE and diffusion model on C-MAPSS dataset.

Bayesian deep learning for remaining useful life estimation via Stein variational gradient descent

Evolutionary Neural Architecture Search for Remaining Useful Life Prediction

Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components

Multi-Objective Optimization of ELM for RUL Prediction

Feature clustering and XIA for RUL estimation

A C library that makes managing and using dynamic maps much easier.

PyTorch implementation of an LSTM-based deep learning model for predicting Remaining Useful Life (RUL) of turbofan engines using the NASA C-MAPSS dataset. Achieves 16.78 RMSE on FD001.

Operational Regime-Aware Degradation Simulation for Mission Planning Support – IEEE IRAI 2026

CNN-LSTM based Remaining Useful Life prediction on NASA C-MAPSS with multi-scale feature engineering, feature selection, and blind-test evaluation.

Closed-loop MLOps system that predicts engine Remaining Useful Life and auto-recovers from sensor distribution shift. On-prem, air-gap-capable, fully reproducible via Ansible. Master's thesis artifact.

Fault Detection Of JET Engine with Reccurent Neural networks

Reproducibility materials for a benchmark-anchored predictive-maintenance decision environment with mechanism-aligned TreeSHAP gating (AI4I, run-to-failure simulator, C-MAPSS)

Airplane Health Management-style predictive-maintenance engine: turbofan Remaining-Useful-Life prognostics on real NASA C-MAPSS (all 4 subsets). RMSE 15 cycles (FD001) with the asymmetric PHM score, early-failure alerts + lead-time; LightGBM + MLflow. Python.

Prognostics and health management on NASA C-MAPSS and spacecraft telemetry: RUL prediction, anomaly detection, a serving API and signed release evidence.