phm

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

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

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

Unified PHM framework for Remaining Useful Life (RUL) prediction, fault diagnosis, fault detection, and anomaly detection for bearings, turbofan engines, and other industrial systems.

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.

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

진동 데이터 기반 PHM 예지보전 분석 프로젝트

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

Detect trends in real time from social data, generate targeted email campaigns with AI, and measure their revenue impact in one platform.

Order-domain Transformer + causal end-of-life smoothing for speed-robust bearing remaining-useful-life (RUL) prediction. Evaluated leave-one-bearing-out without test-set leakage. PHM Korea 2026.

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

Condition-monitoring console for predictive maintenance of industrial robots — five-layer architecture traced to software quality and functional safety standards (KCSE 2026 reference implementation).

Predictive maintenance platform for ion mill equipment using anomaly detection, remaining useful life prediction, fleet monitoring, Django, React, PostgreSQL and Docker.