1D-DGAN-PHM

1D-DGAN-PHM: A 1-D denoising GAN for Prognostics and Health Management with an application to turbofan

Lourenço Baptista, M. (TU Delft Air Transport & Operations)
Henriques, Elsa M.P. (Lisbon Technical University)

2022

The performance of prognostics is closely related to the quality of condition monitoring signals (e.g., temperature, pressure, or vibration signals), which reveal the degradation of the system of interest. However, typical condition monitoring signals include noise and outliers. Disentangling noise from these signals is essential to obtain the actual degradation trajectories. Different denoising methods have been proposed in prognostics. Conventional denoising methods have low complexity but usually do not preserve edge information and do not involve physical considerations. A promising deep learning approach is denoising generative models. This approach is popular in Computer Vision, which has been shown to outperform other classical techniques but has seldom been used in prognostics on 1-D signals. In this paper, we propose the 1-D Denoising Generative Adversarial Network for Prognostics and Health Management (1D-DGAN-PHM). The 1D-DGAN-PHM is trained on synthetic data generated by a custom data generator that infuses physics-of-failure knowledge in paired samples of noisy and noise-free trajectories. The network consists of two components, a denoising generator and a discriminator. The denoising generator aims to learn to denoise a 1-D input signal. The discriminator guides the learning by comparing noise-free signals with signals from the denoising generator. Advantages of the 1D-DGAN-PHM include the physics-of-failure information in the synthetic data generator and the model sophistication. In this work, we apply the 1D-DGAN-PHM to denoise the raw signals derived from NASA's C-MAPSS simulator of an aircraft turbofan engine. Baseline methods are Moving Average, Median filter, Savitzky–Golay filter, and a denoising autoencoder. The 1D-DGAN-PHM produces smooth trajectories and preserves the initial linear degradation of the signals. The 1D-DGAN-PHM has the most significant improvement in prognosability (on average, 0.73 to 0.81). Data from the 1D-DGAN-PHM resulted in the best MAE (29 to 25 cycles) and RMSE (score of 39 to 36) for a Random Forest. The code is publicly available at 1D-DGAN-PHM.

Denoising
Edge-preserving method
Failure prognostics
Generative Adversarial Network
Prognostics and Health Management
Signal processing

http://resolver.tudelft.nl/uuid:a55d958b-10a3-4c83-bc35-a27d8de14c45

https://doi.org/10.1016/j.asoc.2022.109785

1568-4946

Applied Soft Computing, 131

Institutional Repository

journal article

© 2022 M. Lourenço Baptista, Elsa M.P. Henriques