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Dong, J., Wang, Q., Wang, M. and Peng, K. (2018) Data-Driven Quality Monitoring Techniques for Distributed Parameter Systems with Application to Hot-Rolled Strip Laminar Cooling Process. IEEE Access, 6, 16646-16654.
https://doi.org/10.1109/ACCESS.2018.2812919

has been cited by the following article:

• TITLE: Backstepping-Based Distributed Abnormality Detection for Nolinear Parabolic Distributed Prameter Systems

  AUTHORS: Lei Chen

  KEYWORDS: Abnormality Detection, Backstepping, Nonlinear Parabolic Systems, Distributed Parameter Systems, Lyapunov Function

  JOURNAL NAME: Engineering, Vol.14 No.7, July 28, 2022

  ABSTRACT: In this paper, we proposed a model-based abnormality detection scheme for a class of nonlinear parabolic distributed parameter systems (DPSs). The proposed methodology consists of the design of an observer and an abnormality detection filter (ADF) based on the backstepping technique and a limited number of in-domain measurements plus one boundary measurement. By taking the difference between the measured and estimated outputs from observer, a residual signal is generated for fault detection. For the detection purpose, the residual is evaluated in a lumped manner and we propose an explicit expression for the time-varying threshold. The convergence properties of the PDE observer and the residual are analyzed by Lyapunov stability theory. Eventually, the proposed abnormality detection scheme is demonstrated on a nonlinear DPS.