Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.
When new fault occurs, the parameters and structure of fault diagnosis model based on deep learning need to be adjusted for retraining, which is often very time-consuming. For the above problem, a latent representation dual manifold regularization broad learning system (LRDMR-BLS) with incremental learning capability is proposed for process fault diagnosis. This model embeds latent representation learning into feature selection and utilizes the link information between data to guide feature selection. Meanwhile, manifold regularization is introduced in the objective function to preserve the local manifold structure of the original data space. Further, a manifold regularization term is added to the objective function of the broad learning system to preserve the local structure of the features. Finally, the incremental learning capability of the proposed model is given, which enables the proposed model can be updated quickly when new fault occurs. The superiority of the proposed model is demonstrated by two Chemical processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.