Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network

Abstract

Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties of the target RUL function, neural networks can result in unreasonable RUL estimates such as going upwards and wrong endings. In the paper, we apply the fundamental principle of Physics-Informed Neural Network (PINN) to enhance Recurrent Neural Network (RNN) based RUL estimation methods. Through formulating proper constraints into the loss function of neural networks, we demonstrate in our experiments with the NASA IGBT dataset that PINN can make the neural networks trained more realistically and thus achieve performance improvements in estimation error and coefficient of determination. Compared to the baseline vanilla RNN, our physics-informed RNN can improve Mean Squared Error (MSE) of out-of-sample estimation on average by 24.7% in training and by 51.3% in testing; Compared to the baseline Long Short Term Memory (LSTM, a variant of RNN), our physics-informed LSTM can improve MSE of out-of-sample estimation on average by 15.3% in training and 13.9% in testing.

Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks

“…Potentially, this approach could curtail the statistical significance and robustness of the outcomes. A similar approach was proposed in [36], where, despite improving the network performance through a physicsinformed approach, the training method further fragmented individual profiles into data to be used for the training and for testing phases.…”

“…By exploiting the memory capability of the bLSTM network, the accuracy of the RUL prediction is improved, allowing to account for the intrinsic statistical distribution of the failure phenomenon. In contrast with the methodology proposed in [35], [36], in this work a sliding window approach is employed to consider the entire onvoltage profile independently of the chosen inputs number. Furthermore, the approach pursued in this work is broader and more comprehensive compared to previous studies.…”

“…𝐺 𝑘 , that has to be added to the LSTM cell's memory. 𝐼 𝑘 and 𝐺 𝑘 components are non-linear functions of xk and hk-1, each one with its respective activation function: sigmoid (𝜎) and tanh [36]:…”