Autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis

Abstract Industrial processes usually exhibit great nonlinearity generated from the effects of complex mechanisms, system integrations and multiple working conditions. Although a variety of dictionary learning algorithms have been proposed in recent years for industrial process fault diagnosis, most of them only model the process data via a linear combination of a few dictionary atoms, which cannot effectively characterize the nonlinear relationships among variables and may lead to limited diagnosis performance. Recent improvements in multilayer neural networks, especially the autoencoders, offer opportunities to tackle the nonlinear problem. However, the overall limited availability of fault samples poses great challenges in achieving satisfactory performance. To address the mentioned issues simultaneously, the present study proposes an Autoencoder Embedded Dictionary Learning approach (AEDL) for nonlinear industrial process fault diagnosis. First, an autoencoder is employed to learn a nonlinear mapping that maps the linearly inseparable industrial process data to a high-dimensional space, where a desired dictionary is learned according to the basic dictionary learning algorithm. Next, two supervised graphs, leveraging the priors of industrial process data, are introduced into the learning process to make the proposed approach robust to training samples. After obtaining the dictionary, the coding coefficients of the process data over the dictionary can be used for fault diagnosis via a simple classifier. As revealed from the encouraging experimental results on the Tennessee Eastman process, the developed approach outperforms several dictionary learning approaches and some other nonlinear fault diagnosis methods.