Autoencoder-Based Discriminant Locality-Preserving Projections for Fault Diagnosis

In complex industrial production environments, the efficacy of fault diagnostic techniques has become increasingly important and can enhance the reliability and safety of systems. In recent years, the discriminant locality-preserving projection (DLPP) algorithm has shown significant effectiveness in extracting meaningful features from complex industrial data. However, DLPP involves only the operation of projecting high-dimensional data onto a lower dimensional space, which is a one-way mapping process and lacks the verification of whether the low-dimensional data projected can accurately and effectively represent the original data. This might result in the loss of vital information in the original data, consequently limiting the performance of DLPP. In this article, we introduce a novel DLPP approach denoted as autoencoder-based DLPP (DLPP-AE), which is predicated upon the autoencoder. DLPP-AE establishes a bidirectional mapping process: in the encoding stage, DLPP serves as a mapping mechanism that transforms the initial high-dimensional data into a low-dimensional embedded representation; whereas in the decoding stage, the low-dimensional embedded data generated in the encoding stage are remapped back to its original high-dimensional form. This effectively resolves the issue of DLPP’s inability to perform reverse validation on low-dimensional embeddings. To evaluate the performance of the proposed method, we conducted a comprehensive case study using three laboratory datasets and one real industrial dataset. The experimental results confirm the superior fault diagnosis capability of the DLPP-AE method.