Monitoring for Nonlinear Multiple Modes Process Based on LL-SVDD-MRDA

作者：Du, Wenli^*; Tian, Ying; Qian, Feng

来源：IEEE Transactions on Automation Science and Engineering, 2014, 11(4): 1133-1148.

DOI：10.1109/TASE.2013.2285571

• Between-mode dynamic process
• lazy learning (LL)
• modified receptor density algorithm (MRDA)
• multiple operation modes
• nonlinear
• support vector data description (SVDD)

This study proposes an online monitoring technique for nonlinear multiple-mode problems in industrial processes. The contributions of the proposed technique are summarized as follows: 1) Lazy learning (LL), a new adaptive local modeling method, is introduced for multiple-mode process monitoring. In this method, multiple modes are separated and accurately modeled online, and the between-mode dynamic process is considered. 2) The modified receptor density algorithm (MRDA) exhibiting superior nonlinear ability is introduced to analyze the residuals between the actual system output and the model-predicted output. The simulation of the Tennessee Eastman process with multiple operation modes shows that compared with other techniques mentioned in this study, the proposed technique performs more accurately and is more suitable for nonlinear processes with multiple operation modes.

• 出版日期2014-10
• 单位华东理工大学