Verification and Validation of Hierarchical Fault Diagnosis in Satellites Formation Flight

Barua, Alok; Khorasani, K.

doi:10.1109/tsmcc.2012.2187188

Cited by 10 publications

(7 citation statements)

References 14 publications

(56 reference statements)

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“…As a result, in the dynamics, higher-order terms are not ignored in UKF. This filter is built on the fact that "it is easier to approximate a probability distribution than it is to approximate an arbitrary nonlinear function" [113]. In simpler words, it can be said that the linear approximations are not utilized to estimate non-linear functions here; instead, the state's statistical moment is approximated.…”

Section: Unscented Kalman Filtermentioning

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: Unscented Kalman Filtermentioning

confidence: 99%

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

Borah

2024

Preprint

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“…Figure 1.10 illustrates major fault diagnosis methods available in the literature. Various approaches including neural networks [58], fuzzy logic [59], [60], and clustering algorithms [61], [62], as well as hierarchical approaches, have been studied to enhance isolation scheme [59], [63], [64]. However, the main issue with data-driven approaches is the requirement for historical data as well as computational complications.…”

Section: Fault Isolationmentioning

confidence: 99%

“…In addition to the single satellite and actuator fault diagnosis problem, there exists multiple satellites in formation flying and multi-agent cooperative systems where only very few research results are available in the literature. Barua and Khorasani [59], [63] have developed a decomposition hierarchical framework through a Bayesian network-based model called component dependency model. Their model specifies the network parameters using domain experts' beliefs and node fault diagnosis performance data.…”

Section: Fault Isolationmentioning

confidence: 99%

“…This filter is built on the belief that "it is easier to approximate a probability distribution than it is to approximate an arbitrary nonlinear function." [63]. Simply put, it means that the linear approximations are not used to approximate non-linear functions in here; instead, the statistical moment of the state is approximated.…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

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Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

Rahimi¹

2021

Preprint

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Condition-based maintenance (CBM) and prognostics and health management (PHM), as consisting parts of diagnosis, prognosis, and health monitoring (DPHM) framework, have developed over the past decades to remedy the limitations of the traditional maintenance practices for complex systems. In space, where mass and power budget are restricted, application of CBM and PHM has become more vital to the success of a mission. Reaction wheels (RW) and Control Moment Gyros (CMG), as the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location and severity, and estimate the remaining useful life (RUL) of the faulty unit can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this thesis, a model-based DPHM framework is developed and evaluated. Firstly, a novel fault detection algorithm is proposed, using Unscented Kalman filters (UKF) in conjunction with residual and innovation sequences, for detecting agile faults in RW/CMG onboard satellites. Secondly, a novel fault isolation algorithm is proposed, using UKF, Bayes’ probability and interacting multiple models (IMM), to isolate the location of the fault and its severity. Finally, a new fault prognosis approach is proposed, using UKF and particle filters (PF) to estimate the RUL of a faulty unit. Extensive simulations were conducted for each phase of the DPHM to verify advantages of the proposed techniques over the available methods in the literature. Extensive simulations were conducted to evaluate the performance of the proposed methods in each module of the framework. Regarding the proposed fault detection scheme, results showed superior performance of the proposed adaptation technique compared to the original UKF and a previously developed AUKF. The proposed fault isolation scheme was able to successfully isolate the faulty unit at multiple levels of isolation including formation level, system level, and actuator level with over 99% success rate for formation level, over 99% success rate for the RW assembly and for up to 90% success rate for the CMG assembly in the system level. For the CMG assembly, due to direct estimation of the fault parameters, it was possible to determine the severity of the faults as well as their location. Finally, the proposed fault prognosis approach provided RUL estimates with errors as low as 1.5% compared to the actual remaining useful life. Overall, the proposed framework can be regarded as a promising tool for fault detection, isolation and identification, and prognosis of the complex nonlinear systems. Furthermore, the proposed framework can be extended to other complex systems in space including multi-agent formation systems and other areas where the model of the system under study is available.

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“…Khanafer et al [23] proposed automated diagnosis in troubleshooting for universal mobile telecommunications system networks using BNs. Barua and Khorasani [24], [25] proposed a systematic transparent and hierarchical fault diagnosis framework for a satellite formation flight and investigated the verification and validation of the proposed methodology. Ricks and Mengshoel [26] proposed a comprehensive fault diagnosis approach for dynamic and hybrid domains with uncertainty using BNs and arithmetic circuits and validated them using electrical power system data.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Cai

Liu

Xie

2017

IEEE Trans. Automat. Sci. Eng.

220

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Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology. Note to Practitioners-This paper is motivated by the problem of fault diagnosis of complex electronic systems in the presence of transient fault (TF) and intermittent fault (IF). Existing approaches do not involve the dynamic behavior of electronicsystems, i.e., degradation and aging. The results of fault diagnosis could be different at different times for the given identical fault symptoms because of the degradation and aging. This paper suggests a new fault diagnosis approach using dynamic Bayesian networks (DBNs). It aims at identifying the component faults and distinguishing the fault types, i.e., TF, IF, and permanent faults. We construct the DBN structure and parameter models and define two judgment rules to determine the fault diagnosis results. The future scope of work can be directed toward the development of a practice fault diagnosis system for an electronic system to validate the proposed methodology. Index Terms-Dynamic Bayesian network (DBN), fault diagnosis, intermittent fault (IF), transient fault (TF).

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Verification and Validation of Hierarchical Fault Diagnosis in Satellites Formation Flight

Cited by 10 publications

References 14 publications

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

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

Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

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