A novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites

Muthusamy, Venkatesh; Kumar, Krishna Dev

doi:10.1016/j.actaastro.2020.11.004

Cited by 30 publications

(4 citation statements)

References 15 publications

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“…When applying the data-driven methods to an actual satellite system, however, it is mandatory to choose an appropriate statistical model for representing the normal system behavior [7,15]. This is not a trivial work because a satellite system is very complex and its telemetry data include many aspects, such as the fake anomaly problem.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang

Gong

Zhang

et al. 2022

International Journal of Aerospace Engineering

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Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang

Gong

Zhang

et al. 2022

International Journal of Aerospace Engineering

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“…Therefore, the performance of these fault detection methods heavily relies on the experience of experts [ 18 ]. In recent years, data-driven fault detection methods have eliminated this heavy dependence on expert experience and become a popular research field [ 19 , 20 , 21 , 22 ]. These methods establish normal models based on satellite normal historical data, and then compare the online data with the normal models to assess whether the online data is faulty.…”

Section: Introductionmentioning

confidence: 99%

A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence

Zhang

Yang

et al. 2021

Entropy

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Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.

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“…Adopting a feed-forward wavelet-based neural network with a single hidden layer, the proposed method can be applied to nonlinear systems [13]. In [14], Chebyshev Neural Network and genetic algorithm are used to isolate CMG faults using satellite attitude rate data. However, the fault injection in that study does not accommodate in-phase faults, meaning that the faults occur out-of-phase or non-concurrently.…”

Section: Introductionmentioning

confidence: 99%

“…While several articles exist on ACS fault isolation, most focus on systems that only have one active fault [14]. The proposed models cannot handle cases with multiple inphase (concurrent) faults, while these cases are likely to occur during a real-life satellite operation.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Satellite Control Moment Gyro Assembly with Multiple In-Phase Faults

Farahani

Rahimi

2021

Electronics

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A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications.

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A novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites

Cited by 30 publications

References 15 publications

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence

Data-Driven Fault Diagnosis for Satellite Control Moment Gyro Assembly with Multiple In-Phase Faults

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