2026-10-11T06:54:17Z https://data.mendeley.com/oai

oai:data.mendeley.com/xrvr27pjcy.1 2026-10-02T11:23:16Z

Um, Dugan CD-PINODE code and dataset Mendeley Data This repository contains the source code and synthetic dataset supporting the study "CD-PINODE: A Physics-Informed Neural Ordinary Differential Equation Framework for Joint Fault Classification and Remaining Useful Life Estimation in Turbofan Engines." Five prognostics models are implemented and benchmarked on NASA C-MAPSS synthetic sensor data across two co-dependent tasks: (1) binary fault classification (HPC compressor degradation and FAN blade erosion) and (2) continuous Remaining Useful Life (RUL) regression. Each model is evaluated on both an in-distribution test set and a harder out-of-distribution (OOD) test set simulating unseen degradation regimes. The proposed model, CD-PINODE v4, integrates a Neural Ordinary Differential Equation backbone with Brayton-cycle physical constraints, a distance correlation regularizer (dcorr: 0.0471 → 0.0034), and a joint multi-task loss. It achieves in-distribution RUL RMSE of 22.10 and OOD RMSE of 35.05, with OOD HPC balanced accuracy of 84.06% and FAN balanced accuracy of 71.82% using only 61,286 trainable parameters. Four baselines are included for direct comparison: an LSTM Joint Baseline, a Physics-Informed Autoencoder (PI-AE), a CNN-BiLSTM with channel attention, and a GPU-aware Random Forest. All scripts support CUDA acceleration with automatic CPU fallback. Dataset files: - synthetic_train_data.csv — training split (21 sensor channels, HPC/FAN fault labels, RUL targets) - synthetic_test_data.csv — in-distribution test split - synthetic_ood_data_harder.csv — OOD test split (harder degradation regimes) Code files: - cd-pinode_v4_linux.py — proposed CD-PINODE v4 model - lstm_4.py — LSTM baseline - pi_autoencoder_linux.py — PI-Autoencoder baseline - cnn_bilstm_attn_ linux.py — CNN-BiLSTM+Attn baseline - Random_forest_linux.py — GPU-aware Random Forest baseline Computer Science Engineering Dataset doi:10.17632/xrvr27pjcy.1 oai:data.mendeley.com/xrvr27pjcy.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/xrvr27pjcy 2026-10-02T11:23:16Z