Method for Detecting Disorder of a Nonlinear Dynamic Plant

作     者：Wang, Xuechun Eliseev, Vladimir 

作者机构：Natl Res Univ Dept Control & Intelligent Technol Moscow Power Engn Inst Krasnokazarmennaya 14 Moscow 111250 Russia Infotecs JSC Mishina 56 Moscow 127083 Russia 

出 版 物：《SENSORS》 (Sensors)

年 卷 期：2025年第25卷第4期

页      面：1256-1256页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

主　　题：disorder detection time series dynamic plant cross-correlation function neural network autoencoder cumulative sum algorithm exponentially weighted moving variance pH neutralization reaction 

摘      要：This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input-output relation using a cross-correlation function and neural network autoencoder. The CCF-AE method does not use the reference model of the dynamic object, but only considers real-time behavior changes, given by input and output time series. The proposed method was used to detect disorder in the process of a nonlinear pH neutralization reaction, and was compared with the cumulative sum control chart (CUSUM) and the exponentially weighted moving variance control chart (EWMV). The CCF-AE method demonstrates a better true detection rate and lower false alarm rate than CUSUM and EWMV. Also, CCF-AE has more advantages in detecting disorder of complex nonlinear processes.