Abstract
In order to mine the local behavior and dynamic characteristic of batch process data for effective process monitoring, a two-dimensional localized dynamic support vector data description (TLDSVDD) method is proposed in this article. The main contributions of the proposed method include three aspects. Firstly, considering that batch process variables may behave differently at each operation stage, a two-dimensional localization strategy is designed to mine the local behaviors of process data from the perspective of the variable dimension and the sample dimension. Secondly, for each local data segment, the slow feature analysis is applied to build the local dynamic sub-models, which can monitor the static and dynamic process changes simultaneously. Lastly, the model ensemble strategy based on Bayesian inference is employed and two holistic monitoring statistics are developed to indicate the process running status. The proposed method not only extracts the local process behaviors, but also determines whether the process fault belongs to the dynamic or static change. Finally, one case study on the simulated industrial batch process is carried out to exhibit the method performance.
Highlights
Batch processes are extensively applied in the modern industry for the multi-variety, customized, and high value-added products
Hierarchical clustering based on the distance correlation coefficient is applied in the variable dimension for the variable block division, and the phases of the variable block are divided by spectral clustering based on mutual information as the local information mining of the sample dimension
A two-dimensional localization strategy is designed to improve the support vector data description (SVDD) modeling by combining the variable sub-block division and the phase partition
Summary
Batch processes are extensively applied in the modern industry for the multi-variety, customized, and high value-added products. Multi-phase SVDD methods are developed to monitor the batch process faults. Many multi-block and multi-phase SVDD methods have been discussed to deal with the local data behaviors of batch process, many unsolved issues still exist. Based on the present studies and the investigated problems, a two-dimensional localized dynamic support vector data description (TLDSVDD) method is presented to improve the basic SVDD based batch process monitoring method. In this proposed method, process local behaviors are analyzed by a two-dimensional localization strategy, which involves both the variable and sample dimensions. At the second step, the normalized data is re-arranged along the variable direction for monitoring models development
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
