Industrial processes often exhibit significant nonlinearity due to complex mechanisms, system integrations, and varying operating conditions. While many dictionary learning algorithms have been developed for fault identification, most rely on linear combinations of dictionary atoms, which fail to capture nonlinear relationships, resulting in suboptimal performance. Advances in multilayer neural networks, particularly autoencoders, offer potential solutions but are hindered by the scarcity of fault samples. To address these challenges, this chapter proposes an Autoencoder Embedded Dictionary Learning (AEDL) method for nonlinear industrial fault identification. The approach employs an autoencoder to map linearly inseparable process data into a high-dimensional space, enabling effective dictionary learning. Two supervised graphs leveraging prior process data information are integrated to enhance robustness under limited training samples. The learned dictionary’s coding coefficients are then used for fault identification via a simple classifier. Experiments on the Tennessee Eastman process demonstrate that AEDL surpasses existing dictionary learning and nonlinear fault identification methods.