Model Based Fault Diagnosis of Low Earth Orbiting (LEO) Satellite using Spherical Unscented Kalman Filter

Nag, P.V. Sunil; Silla, Gowtham kumar; Gummadi, Venkata Harsha Vardhan; Harishankar, C B; Ray, Venkatesh Kumar; Kumar, Chejarla Santosh

doi:10.1016/j.ifacol.2016.03.127

Cited by 11 publications

(1 citation statement)

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“…Gertler [19] has surveyed model-based methods for fault detection and concludes that major quality issues for failure detection algorithms are isolability, sensitivity, and robustness. Marzat et al [20] have reviewed model-based fault diagnosis approaches for aerospace systems; these approaches include expert systems [23], neural networks [24]- [26], support vector machine (SVM) [27]- [29], principal component analysis (PCA) [30]- [33], parameter estimation [34], [35], Kalman filters (KF) [36]- [38], and unscented Kalman filters (UKF) [39]- [43] and Cubature Kalman Filter (CKF) [44]. More recently, Gao et al [21], [22] have comprehensively reviewed fault diagnosis approaches and their applications from the model and signal-based perspectives.…”

Section: Fault Detectionmentioning

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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