An Improved Just-in-Time Learning Scheme for Online Fault Detection of Nonlinear Systems

Abstract

Just-in-time learning (JITL) scheme has been employed as an efficient tool of online soft sensor. It requires current measured data with high accuracy. However, in real industrial environments, it is difficult to ensure that no disturbance is added to the measurement. To solve this problem, this article proposes an improved JITL scheme, which employs leverage calculation to trim the weight of variables and abate the affect of disturbances. On this basis, an online modeling and output prediction algorithm is further presented. The experiment on a typical nonlinear system shows the better robustness and accuracy of the presented algorithm in comparison with conventional JITL-based approaches. Moreover, an online fault detection strategy for nonlinear systems is proposed based on JITL and partial least squares (PLS), for the purpose of simplifying the parameter setting and reducing the computational load of conventional fault detection approaches for nonlinear systems. Four statistic indexes are designed, including conventional <inline-formula><tex-math notation= LaTeX >$T^2$</tex-math></inline-formula> and SPE for fault detection, <inline-formula><tex-math notation= LaTeX >$T^2_h$</tex-math></inline-formula> and <inline-formula><tex-math notation= LaTeX >$T^2_t$</tex-math></inline-formula> of two orthogonal decomposition input subspaces for fault birth subspace observation. A numerical nonlinear system and an industrial benchmark of Tennessee Eastman process are employed for fault detection experiments, testifying the effectiveness of the proposed strategy.