Low-rank reconstruction-based autoencoder for robust fault detection

Abstract

Autoencoder (AE) has been widely used in multivariate statistical process monitoring (MSPM) and various AE-based methods have been applied in fault detection. Process data is often contaminated by outliers and measurement noise which may lead to the overfitting problem for AE-based methods. In this paper, a novel feature extraction method called low-rank reconstruction-based autoencoder (LRAE) is proposed for robust fault detection. LRAE decomposes the input into a combination of a low-rank data matrix and a noise matrix. By penalizing the rank of the data matrix, LRAE separates the low-rank clean data from the contaminated process data. Instead of directly reconstructing the loss between the input data and the output data, we design a low-rank reconstruction strategy, i.e. reconstruct the loss between the low-rank clean data and the output of the AE. The proposed LRAE can be trained end-to-end by jointly optimizing an AE and a low-rank approximation. LRAE is a nonlinear method which can tackle the complicated process data better than the linear methods such as principal component analysis (PCA). Moreover, the optimization of the low-rank approximation provides the robustness of LRAE to reconstruct the clean data in the output layer when the input process data is contaminated. After training, the features of the hidden layer can be computed for further fault detection. Extensive experiments demonstrate that LRAE outperforms traditional fault detection methods, including PCA, robust principal component analysis (RPCA), kernel principal component analysis (KPCA), AE, and denoising autoencoder (DAE). Especially, LRAE provides more robust results when the process data suffer from outliers and measurement noise.