hazedt / phmgnnbenchmark

this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.

PHMGNNBenchmark

The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study

Implementation of the paper:

Paper:

@article{PHMGNNBenchmark,
  title={The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study},
  author = {Tianfu Li and Zheng Zhou and Sinan Li and Chuang Sun and Ruqiang Yan and Xuefeng Chen},
  journal={Mechanical Systems and Signal Processing},
  volume = {168},
  pages = {108653},
  year = {2022},
  issn = {0888-3270},
  doi = {https://doi.org/10.1016/j.ymssp.2021.108653},
  url = {https://www.sciencedirect.com/science/article/pii/S0888327021009791},
}

Requirements

• Python 3.8 or newer
• torch-geometric 1.6.1
• pytorch 1.6.0
• pandas 1.0.5
• numpy 1.18.5

Guide

We provide a novel intelligent fault diagnostics and prognostics framework based on GNNs. The framework consists of two branches, that is, the node-level fault diagnostics architecture and graph-level fault diagnostics or regression architecture. In node-level fault diagnosis, each node of a graph is considered as a sample, while the entire graph is considered as a sample in graph-level fault diagnosis.
In this code library, we provide three graph constrcution methods (KnnGraph, RadiusGraph, and PathGraph), and two different input types (Frequency domain and time domain). Besides, seven GNNs and four graph pooling methods are implemented.

Pakages

• datasets contians the data load method for different dataset
• model contians the implemented model for nodel-level task
• model2 contians the implemented model for graph-level rask

Run the code

For fault diagnostic

• Node level fault daignostic
  python ./train_graph_diagnosis.py --model_name GCN --data_name XJTUGearboxRadius --data_dir ./data/XJTUGearbox/XJTUGearboxRadius.pkl --Input_type TD --task Node --checkpoint_dir ./checkpoint
• Graph level fault daignostic
  python ./train_graph_diagnosis.py --model_name GCN --data_name XJTUGearboxRadius --data_dir ./data/XJTUGearbox --Input_type TD --task Graph --pooltype EdgePool --checkpoint_dir ./checkpoint

For prognostic

python ./train_graph_prognosis.py --model_name GCN --pooltype EdgePool --data_name CMAPSS_graph --data_file FD001 --data_dir ./data/CMAPSS/ --checkpoint_dir ./checkpoint/FD001

The data for runing the demo

In order to facilitate your implementation, we give some processed data here for node level-fault diagnosis and graph-level prognosis Data for demo.

Datasets

Fault diagnostic datasets

Self-collected datasets

• XJTUSpurgear Dataset
• XJTUGearbox Dataset

Open source datasets

• CWRU Bearing Dataset
• MFPT Bearing Dataset
• PU Bearing Dataset
• SEU Gearbox Dataset

Prognostic datasets

• CMAPSS dataset
• PHM2010 dataset

Note

This code library is run under the windows operating system. If you run under the linux operating system, you need to delete the ‘/tmp’ before the path in the dataset to avoid path errors.

Related works

• Li et al. Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis, IEEE Transactions on Industrial Electronics
• Li et al. Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions, IEEE Transactions on Instrumentation and Measurement
• Li et al. Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction, Reliability Engineering & System Safety

phmgnnbenchmark's Issues

utils\train_graph_utils.py的bug

作者您好，标题的文件中for data in self.dataloaders[phase]:这行代码报错，原因为RuntimeError: The 'data' object was created by an older version of PyG. If this error occurred while loading an already existing dataset, remove the 'processed/' directory in the dataset's root folder and try again.
请问是PyG的版本太高了吗，我用的PyG版本为2.0.1,盼回复！

图片节点的图分类及传感器的选择问题

作者您好，如果每个节点的输入形式是图片（目前主流算法好像都是默认每个节点的特征都是向量形式的），有没有办法直接对这些图片节点进行类似图卷积的操作，然后再进行graph级别的分类呢？
另外一个问题是如果想要对图上的节点（传感器）进行筛选，这个任务目前有相关办法解决嘛？

Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction

作者您好，拜读完您的相关论文，收获颇丰。想要复现一下《Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction》，但是一些部分还有些问题，不知能否分享一下源码？非常感谢，别无他用，仅用于学术研究！

《The emerging graph neural networks for intelligent fault diagnostics and prognostics》

作者您好，《The emerging graph neural networks for intelligent fault diagnostics and prognostics》这篇文章目前在网上还找不到，如果方便的话可以发一份给我嘛？[email protected]，谢谢。另外，有一些关于图网络在多源传感器融合方向的问题想向您咨询一下，希望能加一下您的微信？我的微信号是13218013195

Self-collected datasets are invalid

Self-collected datasets are invalid, can you reupload this dataset?

train_graph_diagnosis.py

作者您好，代码里缺少了 train_graph_diagnosis.py 文件

预处理CMAPSS数据集

作者您好，想问一下对于CMAPSS寿命预测数据集，如何对其进行预处理得到相应的 edge_index_FD001.npy 和 train_FD001.pkl 文件？有源代码嘛？

about test.py

Hi, Authors！
I wonder if the testing is included in the code. I only saw the training is in the Python file named train_graph_diagnosis.
If the testing is included in the code, could you please tell me which file the corresponding code for testing is in?
Thank you!!

consult testing

Hi, Authors！
I wonder if the testing is included in the code. I only saw the training is in the Python file named train_graph_diagnosis.
If the testing is included in the code, could you please tell me which file the corresponding code for testing is in?
Thank you!!

初学小白不知道如何运行

我在第一次运行的时候train_graph_diagnosis.py 报错
FileNotFoundError: [WinError 3] 系统找不到指定的路径。: './checkpoint\Node_ChebyNet_XJTUGearboxKnn_TD_1102-153115'

运行train_graph_prognosis.py 报错
FileNotFoundError: [Errno 2] No such file or directory: './data/CMAPSS/edge_index_FD001.npy'

多传感器数据运行

您好，如果需要使用多传感器数据集是否需要自己修改代码，在源代码里面没有看见如何处理多传感器数据的，但是文章中提及了将多变量时序数据中每个传感器视为一个节点

请问一下那个如何将原始的数据集转换成图所需要的数据集

Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction

作者您好，这篇论文有参考代码嘛？