Distributed Fault Detection for Linear Time-Varying Multi-Agent Systems With Relative Output Information

Zou, Peilu; Wang, Ping; Yu, Chengpu

doi:10.1109/access.2021.3066109

Cited by 5 publications

(5 citation statements)

References 46 publications

(55 reference statements)

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“…In [100], Zou et al used a distributed architecture to analyze linear time-varying systems. Unlike other resources that rely on relative state and output information since time-varying systems are being analyzed, it may not be appropriate to use relative information.…”

Section: Fault Diagnosis and Prognosis Methods For Multiagent Systems...mentioning

confidence: 99%

“…An augmented model combines local output measurements in the absence of absolute output measurements. In [100], the diagnosis of a sensor fault consists of two stages, namely the generation of residuals and their interpretation. The residual-generation stage is an optimization problem involving minimizing an indefinite quadratic function.…”

Section: Fault Diagnosis and Prognosis Methods For Multiagent Systems...mentioning

confidence: 99%

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Fault Diagnosis and Prognosis for Satellite Formation Flying: A Survey

Barzegar

Rahimi

2022

IEEE Access

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Section: Fault Diagnosis and Prognosis Methods For Multiagent Systems...mentioning

confidence: 99%

Section: Fault Diagnosis and Prognosis Methods For Multiagent Systems...mentioning

confidence: 99%

Fault Diagnosis and Prognosis for Satellite Formation Flying: A Survey

Barzegar

Rahimi

2022

IEEE Access

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“…Secondly, as reviewed in the literature, a good number of present works consider a team of agents where each agent has a linear time-invariant (LTI) dynamical representation [210,220], whereas most of the practical systems are nonlinear. The major emphasis has been put on the estimation of sensor/actuator faults, but the detection and isolation problem has been neglected in several works [214,[221][222][223].…”

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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“…Accurate fault diagnosis approaches produce time to mitigate the faults, save maintenance costs, and reduce the risk of a breakdown of the whole system, which is crucial for improving the reliability of the system, e.g., [6][7][8][9][10]. In recent years, fault diagnosis of multiagent systems has received extensive attention and undergone rapid development [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Fault misclassification matrix trained by the CNN with batch normalisationIt can be seen from FIGURE21that, after 318 seconds' training, the recognition accuracy of the network has reached 99%, which is a considerable improvement compared with the BP and the traditional CNN. Based on the developed image fusion-based CNN with batch normalisation, the fault classification accuracy of the seven types of faults is satisfactory.Remark 6: The time complexity and space complexity of the neural network can be represented by Equations(11) and (12) as follows: time~Ο(∑ 𝑀 𝑙 2 • 𝐾 𝑙 2 • 𝐶 𝑙−1 • 𝐶 𝑙 𝐷 𝑙=1 ) (11) space~O(∑ 𝐾 𝑙 2 • 𝐶 𝑙−1 • 𝐶 𝑙 + ∑ 𝑀 2 • 𝐶 𝑙 𝐷 𝑙=1 𝐷 𝑙=1…”

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confidence: 99%

A Deep Learning-Based Fault Diagnosis of Leader-Following Systems

Liu

Xin

2022

IEEE Access

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This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in a leader-following multiagent system. First, sequences of one-dimensional data collected from multiple sensors of followers are fused into a two-dimensional image. Then, the image is employed to train a convolution neural network with a batch normalisation layer. The trained network can locate and classify three typical fault types: the actuator limitation fault, the sensor failure and the communication failure. Moreover, faults can exist in both leaders and followers, and the faults in leaders can be identified through data from followers, indicating that the developed deep learning fault diagnosis is distributed. The effectiveness of the deep learning-based fault diagnosis algorithm is demonstrated via Quanser Servo 2 rotating inverted pendulums with a leader-follower protocol. From the experimental results, the fault classification accuracy can reach 98.9%.

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Distributed Fault Detection for Linear Time-Varying Multi-Agent Systems With Relative Output Information

Cited by 5 publications

References 46 publications

Fault Diagnosis and Prognosis for Satellite Formation Flying: A Survey

Fault Diagnosis and Prognosis for Satellite Formation Flying: A Survey

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

A Deep Learning-Based Fault Diagnosis of Leader-Following Systems

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