Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

This is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测)

基于注意力机制的少量样本故障诊断 pytorch

Datasets for Predictive Maintenance

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 Unified Framework for Prognostics and Health Management (PHM) Tasks, enabling streamlined execution of key tasks such as Remaining Useful Life prediction (RUL prediction), fault diagnosis, and anomaly detection.

Hypercomplex Neural Networks with PyTorch

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

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

Algorithms for Battery Mangerment System

Remaining useful life prediction. Degradation path approximation (DPA) is a highly easy-to-understand and brand-new solution way for data-driven RUL prediction. Many research directions on DPA can be further studied.

Feature clustering and XIA for RUL estimation

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

基于自编码器的健康度实时预测模型，并在昇腾Atlas 300I Duo上实现国产化部署

Industrial AI Group @ School of Information and Communication Engineering, BUPT

FusionCore v0 is a research framework for benchmarking risk-aware time-series models in aerospace PHM. It focuses on de-noising complex flight manifolds, evaluating model stability under multi-modal regimes, and ensuring prognostic generalisation through rigorous experimental auditing.

🛰 Monitor rocket system health with point anomaly detection on time series data using simple detectors and evaluate performance metrics.

This repository contains a small, focused **AI-assisted health monitoring** demo for a **liquid-propellant rocket engine**.

Outcomes Framework rebuild for 2025/26 READ THE DOCUMENTATION: