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1. Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems

   https://doi.org/10.1109/TCBB.2018.2880234  

   Bahadorinejad, Arghavan; Imani, Mahdi; Braga-Neto, Ulisses (November 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   We propose a novel methodology for fault detection and diagnosis in partially-observed Boolean dynamical systems (POBDS). These are stochastic, highly nonlinear, and derivative- less systems, rendering difficult the application of classical fault detection and diagnosis methods. The methodology comprises two main approaches. The first addresses the case when the normal mode of operation is known but not the fault modes. It applies an innovations filter (IF) to detect deviations from the nominal normal mode of operation. The second approach is applicable when the set of possible fault models is finite and known, in which case we employ a multiple model adaptive estimation (MMAE) approach based on a likelihood-ratio (LR) statistic. Unknown system parameters are estimated by an adaptive expectation- maximization (EM) algorithm. Particle filtering techniques are used to reduce the computational complexity in the case of systems with large state-spaces. The efficacy of the proposed methodology is demonstrated by numerical experiments with a large gene regulatory network (GRN) with stuck-at faults observed through a single noisy time series of RNA-seq gene expression measurements. 

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2. Fault Detection and Isolation of Uncertain Nonlinear Parabolic PDE Systems

   https://doi.org/https://doi.org/10.48550/arXiv.2203.15850  

   Zhang, Jingting; Yuan, Chengzhi; Zeng, Wei; Wang, Cong. (March 2022, ArXivorg)

   This paper proposes a novel fault detection and isolation (FDI) scheme for distributed parameter systems modeled by a class of parabolic partial differential equations (PDEs) with nonlinear uncertain dynamics. A key feature of the proposed FDI scheme is its capability of dealing with the effects of system uncertainties for accurate FDI. Specifically, an approximate ordinary differential equation (ODE) system is first derived to capture the dominant dynamics of the original PDE system. An adaptive dynamics identification approach using radial basis function neural network is then proposed based on this ODE system, so as to achieve locally-accurate identification of the uncertain system dynamics under normal and faulty modes. A bank of FDI estimators with associated adaptive thresholds are finally designed for real-time FDI decision making. Rigorous analysis on the FDI performance in terms of fault detectability and isolatability is provided. Simulation study on a representative transport-reaction process is conducted to demonstrate the effectiveness and advantage of the proposed approach. 

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3. Functional estimation of perturbed positive real infinite dimensional systems using adaptive compensators

   https://doi.org/10.23919/ACC45564.2020.9147702  

   Demetriou, Michael A. (July 2020, 2020 American Control Conference (ACC))

   This paper extends earlier results on the adaptive estimation of nonlinear terms in finite dimensional systems utilizing a reproducing kernel Hilbert space to a class of positive real infinite dimensional systems. The simplest class of strictly positive real infinite dimensional systems has collocated input and output operators with the state operator being the generator of an exponentially stable C 0 semigroup on the state space X . The parametrization of the nonlinear term is considered in a reproducing kernel Hilbert space Q and together with the adaptive observer, results in an evolution system considered in X × Q. Using Lyapunov-redesign methods, the adaptive laws for the parameter estimates are derived and the well-posedness of the resulting evolution error system is summarized. The adaptive estimate of the unknown nonlinearity is subsequently used to compensate for the nonlinearity. A special case of finite dimensional systems with an embedded reproducing kernel Hilbert space to handle the nonlinear term is also considered and the convergence results are summarized. A numerical example on a one-dimensional diffusion equation is considered. 

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4. Model‐based fault diagnosis for safety‐critical chemical reactors: An experimental study

   https://doi.org/10.1002/aic.18565  

   Du, Pu; Wilhite, Benjamin; Kravaris, Costas (August 2024, AIChE Journal)

   Abstract This article presents an experimental application of fault detection, isolation, and estimation in a chemical reactor system, introducing a functional observer‐based approach without the need for linear approximation. The residual signal generators, functioning as disturbance‐decoupled functional observers, provide fault size estimates and enable fault isolation through multiple generators operating independently. The experimental study focuses on the 3‐Picoline oxidation process, deriving a discrete‐time model, and constructing specific residual generators for coolant inlet temperature and feed concentration faults. Fault diagnosis employs Fast Fourier Transform (FFT) filtering and Generalized Likelihood Ratio (GLR), facilitating on‐the‐fly detection during the experiment. The effectiveness of fault detection, disturbance decoupling, and estimation is experimentally validated. 

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5. Model‐based fault diagnosis in closed‐loop safety‐critical chemical reactors: An experimental study

   https://doi.org/10.1002/aic.18906  

   Du, Pu; Wilhite, Benjamin; Kravaris, Costas (May 2025, AIChE Journal)

   Abstract This experimental study investigates fault detection and estimation in a continuous stirred‐tank reactor (CSTR) system under closed‐loop feedback control, including an analysis of different manipulative inputs for temperature regulation. A novel fault diagnosis approach is proposed, combining residual signal analysis andT²statistic for real‐time fault detection and size estimation. The closed‐loop system demonstrated robust setpoint tracking and fault tolerance across a range of fault magnitudes. Residual signals serve as direct estimators of fault size, critical for adaptive control, while theT²statistic enhances reliability by identifying deviations from normal behavior with fault‐confidence thresholds. As a step towards fault‐tolerant control, the proposed methodology lays the groundwork for advanced control strategies that can ensure safe and efficient operation of chemical reactor systems. 

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