cmapss

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.

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.

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

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.

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)

Reproducible NASA C-MAPSS FD001 RUL experiment with engine-level validation, measured results, and an offline report.

RUL prediction and fault detection on NASA CMAPSS · IEEE TIE/TII benchmarks

NASA CMAPSS 涡扇发动机 RUL 预测基准：相似度匹配/LSTM/GRU + PHM08 不对称损失，FD001–FD004 统一协议评测

基于 NASA C-MAPSS 涡轮发动机退化仿真数据，构建剩余有用寿命（Remaining Useful Life, RUL）预测系统

Reference architecture for predictive maintenance of autonomous delivery robots: edge anomaly detection, cloud RUL, decision layer. Implements my TU Berlin bachelor's thesis.

航空发动机剩余寿命预测 — LSTM + NASA C-MAPSS + 21 传感器

Jet engine remaining-useful-life prediction on NASA C-MAPSS with a from-scratch ANFIS (neuro-fuzzy) in PyTorch, with interpretable plain-English rules.

Remaining Useful Life (RUL) prediction for NASA C-MAPSS turbofan engines. EDA, feature engineering, Random Forest, XGBoost and LSTM (PyTorch). Best FD001 result: RMSE 13.18 (LSTM ensemble). Predictive maintenance for aerospace.

Predictive maintenance for turbofan engines: RUL prediction on NASA C-MAPSS (FD001 RMSE 14.5), FastAPI + live dashboard, model registry, Docker Compose.

Predicting remaining useful life of turbofan engines from multi-sensor telemetry (NASA C-MAPSS), with engine-grouped cross-validation and a reproducible scikit-learn pipeline.

Remaining-useful-life prediction on NASA C-MAPSS: an LSTM vs. LightGBM under one engine-disjoint cross-fitted protocol, with CV+ intervals at nominal 90% coverage, a failure alert, integrated-gradients attributions and an audit that checks every generated number against the committed artifacts.