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AttnPINN for RUL Estimation

This repository includes the code and data for the paper "Remaining useful life with self-attention assisted physics-informed neural network"

Abstract

Remaining useful life (RUL) prediction as the key technique of prognostics and health management (PHM) has been extensively investigated. The application of data-driven methods in RUL prediction has advanced greatly in recent years. However, a large number of model parameters, low prediction accuracy, and lack of interpretability of prediction results are common problems of current data-driven methods. In this paper, we propose a Physics-Informed Neural Networks (PINNs) with Self-Attention mechanism-based hybrid framework for aircraft engine RUL prognostics. Specifically, the self-attention mechanism is employed to learn the differences and interactions between features, and reasonably map high-dimensional features to low-dimensional spaces. Subsequently, PINN is utilized to regularize the end-to-end prediction network, which maps features to RUL. The RUL prediction framework termed AttnPINN has verified its superiority on the Commercial Modular AeroPropulsion System Simulation (C-MAPSS) dataset. It achieves state-of-the-art prediction performance with a small number of parameters, resulting in computation-light features. Furthermore, its prediction results are highly interpretable and can accurately predict failure modes, thereby enabling precise predictive maintenance.

Configuration

matplotlib==3.3.2
numpy==1.21.6
scikit_learn==1.0.2
torch==1.11.0
torchsummary==1.5.1

If you want to install the required environments one by one, you can copy the following codes:

pip install matplotlib==3.3.2
pip install numpy==1.21.6
pip install scikit_learn==1.0.2
pip install torch==1.11.0
pip install torchsummary==1.5.1

or use this:

pip install -r requirements.txt

Quick Start

Running the project with the following code:

python main.py

In main.py, it includes training, predicting and drawing functions.

By default, only predicting function will run and the output will be:

Test_RMSE: 18.37,   Score: 2058.5

If you want to train the model by yourself:hammer::hammer:, you can uncomment the train function in main.py.

#pinn.train(1000) => pinn.train(1000)

And then, the output will be:

It: 0,   Valid_RUL_RMSE: 100.92
It: 1,   Valid_RUL_RMSE: 99.87
It: 2,   Valid_RUL_RMSE: 40.89
It: 3,   Valid_RUL_RMSE: 40.76
It: 4,   Valid_RUL_RMSE: 40.74
It: 5,   Valid_RUL_RMSE: 40.74
It: 6,   Valid_RUL_RMSE: 35.48
It: 7,   Valid_RUL_RMSE: 20.47
It: 8,   Valid_RUL_RMSE: 18.70
It: 9,   Valid_RUL_RMSE: 18.27
···

Comparisons with State-of-the-art Methods

Method	RMSE	Score	Parameters
DCNN(Li et al., 2018)	23.31	12466	72.7K
RNN-Autoencoder(Yu et al.. 2020)	22.15	2901	378.0K
GCU-Transformer(Mo et al.,2021)	24.86	N/A	399.7K
MCLSTM(Sheng et al., 2021)	23.81	4826	N/A
Double attention-Transformer(Liu et al., 2022)	19.86	1741	N/A
e-RULENet(Natsumeda, 2022)	20.80	1554	32.3K
PDE-PHM(Cofre-Martel et al., 2021)	25.58	N/A	1,066
AttnPINN(proposed framework)	18.37	2059	2,260

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