Sensors Incipient Fault Detection and Isolation Using Kalman Filter and Kullback–Leibler Divergence

Gautam, Suryakant; Tamboli, Prakash Kumar; Patankar, V. H.; Roy, Kallol; Duttagupta, Siddhartha P.

doi:10.1109/tns.2019.2907753

Cited by 28 publications

(4 citation statements)

References 26 publications

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“…Extensive use of the UKF has been observed in recent years for the estimation of states and parameters [142][143][144], control [145], multi-agent systems [146][147][148][149], navigation [150,151], and fault diagnosis [152][153][154][155][156][157][158][159][160][161][162]. However, to the best of the author's knowledge, there has been little published work on state estimation problems in multi-agent systems using non-linear filtering methods, specially UKF.…”

Section: Literature Reviewmentioning

confidence: 99%

Nonlinear Filtering and Reinforcement Learning-based Smart Autonomous Multi-agent Systems

Borah

2024

Preprint

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<p>There is growing importance in complex engineering systems to operate autonomously, especially given potential malfunctions that may occur in the system, such as sensors, actuators, components, communication networks, and controllers. Fault detection, isolation, and reconstruction (FDIR) are crucial for autonomous systems. There is significant demand to evolve efficient intelligent systems to detect faults, isolate fault locations and autonomously reconstruct any component of a complex dynamical system. Hence, it is essential to detect, isolate, and reconstruct the faults efficiently and on time when the systems are in operation. This dissertation presents a novel methodology for developing smart autonomous multiagent systems (SAMAS). The SAMAS comprises several agents; some are homogeneous while others are heterogeneous. The proposed method involves developing a multi-agent systems (MAS) model and designing a decentralized smart control system. The MAS model contains homogeneous and heterogeneous agents, communicates among agents through an undirected connected graph, external disturbances, goals, and constraints. The sensor, actuator, communication, and controller faults are also modeled in the MAS model. A decentralized smart control system is designed to create a SAMAS model in the presence of uncertainty in each agents’ dynamics and faults located in the sensors, actuators, communication networks, and controllers. The proposed control method is based on non-linear filtering techniques, deep reinforcement learning, and robust control techniques. A Chebyshev neural network (CNN) is incorporated to learn the uncertain nonlinear functions in the agent dynamics of MAS. Additionally, robust control term using the hyperbolic tangent function is applied to counteract the neural network approximation errors. Meanwhile, a novel algorithm has been proposed which is employed to estimate the uncertain states of the agent dynamics and to train the internal parameters of the neural network given a set of prior measurements. Moreover, an adaptive threshold method has been proposed to detect any kinds of faults present in the system followed by a likelihood-based isolation method to locate each faulty agent. The novel algorithm is known as a reinforced unscented Kalman filter (RUKF). The primary purpose of the RUKF is to detect and isolate the faults, and to adapt the process and measurement noise covariance matrices to reconstruct the faults. To assess the performance of the proposed methodology, we developed a SAMAS consisting of six heterogeneous uncertain agent dynamics. The SAMAS model and the proposed control methodology are numerically simulated using MATLAB. A Monte-Carlo (MC) simulation was carried out to assess the performance of the proposed control methodology in the presence of uncertain agent dynamics and with the sensor, actuator, communication, and controller faults. The fault isolation results are summarized in confusion matrices for each faulty case. The stability of the RUKF, which ran in conjunction with a robust control method, has been proven using the Lyapunov stability approach. Extensive simulations were conducted to evaluate the performance of the proposed method. In this study, the proposed method showed superior performance to the standard unscented Kalman filter (UKF) and adaptive UKF (AUKF). The proposed fault isolation scheme isolated the faulty agents with over 96.68% success rate at the system level. Hence, the results of the numerical simulations show the feasibility of the proposed approach. The proposed RUKF is also computationally less expensive than the standard UKF and AUKF. The proposed approach can be considered a promising tool to evaluate fault detection, isolation, and reconstruction in complex engineering systems. Furthermore, the proposed approach can be extended to other deep space complex systems. The proposed approach can also be used for MAS problems for the financial sector, such as stock market prediction, economic time series, and multi-arm bandit problems.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Nonlinear Filtering and Reinforcement Learning-based Smart Autonomous Multi-agent Systems

Borah

2024

Preprint

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“…Therefore, the system fault detection and faulttolerant control must be robust to prevent accidents and ensure safe and smooth production [31]. Although certain researchers have focus on this aspect [32]- [35], research on fault detection and fault-tolerant control of switching T-S fuzzy stochastic systems is limited owing to the complexity of switching systems. Specifically, due to the particularity of switching system, the fault detection of this kind of system needs to be paid more attention [36].…”

Section: Introductionmentioning

confidence: 99%

Reduced Model-Based Fault Detector and Controller Design for Discrete-Time Switching Fuzzy Systems

Tan

Bing

et al. 2023

IEEE Trans. Fuzzy Syst.

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The reduced model-based coordinated design of fault detectors and controllers for discrete-time switching fuzzy systems is examined. First, the mean-square exponential stabilization of switching Takagi-Sugeno fuzzy systems is performed using the average dwell time method under an arbitrary switching law. Next, using segmented Lyapunov function techniques, a dynamic full-and reduced-order fault detector and controller is designed to ensure that the overall dynamic residual system is mean-square exponentially stable with a balanced H∞ performance level (ξ, β). The solvability conditions for the fault detector and controller are derived using a linearization method, and the relevant parameters can be determined using the mathematical linear matrix solver toolbox. Two examples including a switching Chua's circuit system are presented to demonstrate the effectiveness of the proposed fault detector and controller.

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“…Unfortunately, incipient faults can slowly affect the process behavior and gradually evolve into serious faults, even system failures. Thus, incipient fault detection plays a crucial role in the maintenance activities where timely and effective detection of incipient faults can avoid more serious consequences [18]. Conventional MSPM methods as mentioned above are not sensitive to incipient faults, resulting in a high missed detection rate (MDR) and long detection delay (DD) time.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis

Ferrari

Liu

et al. 2021

IEEE Trans. Ind. Inf.

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Incipient fault detection plays a crucial role in preventing the occurrence of serious faults or failures in industrial processes. In most industrial processes, linear, and nonlinear relationships coexist. To improve fault detection performance, both linear and nonlinear features should be considered simultaneously. In this article, a novel hybrid linear-nonlinear statistical modeling approach for data-driven incipient fault detection is proposed by closely integrating recently developed canonical variate dissimilarity analysis and mixed kernel principal component analysis (MKPCA) using a serial model structure. Specifically, canonical variate analysis (CVA) is first applied to estimate the canonical variables (CVs) from the collected process data. Linear features are extracted from the estimated CVs. Then, the canonical variate dissimilarity (CVD) which quantifies model residuals in the CVA state-subspace is calculated using the estimated CVs. To explore the nonlinear features, the nonlinear principal components are extracted as nonlinear features through performing MKPCA on CVD. Fault detection indices are formed based on Hotelling's T 2 as well as Q statistics from the extracted linear and nonlinear features. Moreover, kernel density estimation is utilized to determine the control limits. The effectiveness of the proposed method is demonstrated by the comparisons with other relevant methods via simulations based on a closed-loop continuous stirred-tank reactor process. Index Terms-Canonical variate analysis (CVA), dissimilarity analysis, incipient fault detection, kernel principal component analysis (KPCA), mixed kernel.

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Sensors Incipient Fault Detection and Isolation Using Kalman Filter and Kullback–Leibler Divergence

Cited by 28 publications

References 26 publications

Nonlinear Filtering and Reinforcement Learning-based Smart Autonomous Multi-agent Systems

Nonlinear Filtering and Reinforcement Learning-based Smart Autonomous Multi-agent Systems

Reduced Model-Based Fault Detector and Controller Design for Discrete-Time Switching Fuzzy Systems

Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis

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