Fault detection of reaction wheels in attitude control subsystem of formation flying satellites

Mousavi, S F; Khorasani, K.

doi:10.1108/ijius-02-2013-0011

Cited by 16 publications

(6 citation statements)

References 28 publications

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“…which means this algorithm is limited by its need for component level sensor measurements. A dynamic neural network based approach is given for fault diagnosis of reaction wheels in satellites in (Mousavi & Khorasani 2014). It uses 50,000 training samples for neural network training and it is not clear if orthogonal configuration or pyramid configuration is used.…”

Section: Fault Diagnosis Of Cmgs In Satellitesmentioning

confidence: 99%

“…[PROBLEM 1] -For developing an Input-Output based data-driven model for attitude actuators in satellites, dataset with more than 20000 data points are used for training ANN (Al-zyoud & Khorasani 2006;Mousavi & Khorasani 2014). But more computational power is required for training such a huge volume of dataset onboard satellite or the training has to be done in the ground station.…”

Section: Problem Statementmentioning

confidence: 99%

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Fault Detection, Isolation and Prognosis for Control Moment Gyros Onboard Satellites

Muthusamy¹

2023

Preprint

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Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

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Section: Fault Diagnosis Of Cmgs In Satellitesmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Fault Detection, Isolation and Prognosis for Control Moment Gyros Onboard Satellites

Muthusamy¹

2023

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%

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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“…Therefore, building the white-box mathematical model is very difficult. Thus, the researchers have proposed many artificial intelligence (AI) schemes for modeling the reaction wheels [3][4][5][6][7][8]. For instance, Al-Zyoud and Khorasani [3] proposed a dynamic multilayer perceptron scheme for modeling the spacecraft reaction wheel.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, this imposes a long computation time. Later on, Mousavi and Khorasani [5] proposed a reaction wheel model that represents four spacecraft formation flight missions. Thus, reaction wheel dynamics have been introduced by using an infinite impulse response filter with dynamic hidden layer neurons.…”

Section: Introductionmentioning

confidence: 99%

A high accuracy modeling scheme for dynamic systems: spacecraft reaction wheel model

2022

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Reaction wheels are crucial actuators in spacecraft attitude control subsystem (ACS). The precise modeling of reaction wheels is of fundamental need in spacecraft ACS for design, analysis, simulation, and fault diagnosis applications. The complex nature of the reaction wheel leads to modeling difficulties utilizing the conventional modeling schemes. Additionally, the absence of reaction wheel providers’ parameters is crucial for triggering a new modeling scheme. The Radial Basis Function Neural Network (RBFNN) has an efficient architecture, alluring generalization properties, invulnerability against noise, and amazing training capabilities. This research proposes a promising modeling scheme for the spacecraft reaction wheel utilizing RBFNN and an improved variant of the Quantum Behaved Particle Swarm Optimization (QPSO). The problem of enhancing the network parameters of the RBFNN at the training phase is formed as a nonlinear constrained optimization problem. Thus, it is proposed to efficiently resolve utilizing an enhanced version of QPSO with mutation strategy (EQPSO-2M). The proposed technique is compared with the conventional QPSO algorithm and different variants of PSO algorithms. Evaluation criteria rely upon convergence speed, mean best fitness value, stability, and the number of successful runs that has been utilized to assess the proposed approach. A non-parametric test is utilized to decide the critical contrast between the results of the proposed algorithm compared with different algorithms. The simulation results demonstrated that the training of the proposed RBFNN-based reaction wheel model with enhanced parameters by EQPSO-2M algorithm furnishes a superior prediction accuracy went with effective network architecture.

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Fault detection of reaction wheels in attitude control subsystem of formation flying satellites

Cited by 16 publications

References 28 publications

Fault Detection, Isolation and Prognosis for Control Moment Gyros Onboard Satellites

Fault Detection, Isolation and Prognosis for Control Moment Gyros Onboard Satellites

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

A high accuracy modeling scheme for dynamic systems: spacecraft reaction wheel model

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