2021
DOI: 10.3390/electronics10192432
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A Three-Stage Data-Driven Approach for Determining Reaction Wheels’ Remaining Useful Life Using Long Short-Term Memory

Abstract: Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable system parameter of a faulty reaction wheel induced with incipient fault to estimate the remaining useful life of the reaction wheels. We achieve this goal in three stages, as none of the observable system parameters are directly re… Show more

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Cited by 14 publications
(5 citation statements)
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“…Then, the difference between estimated and actual torque values used to generate threshold and residual for FDI task. [11][12][13][14][15][16].…”
Section: Artificial Neural Network (Ann) Model-based Fdi Techniquementioning
confidence: 99%
“…Then, the difference between estimated and actual torque values used to generate threshold and residual for FDI task. [11][12][13][14][15][16].…”
Section: Artificial Neural Network (Ann) Model-based Fdi Techniquementioning
confidence: 99%
“…In Reference [7], a three-step prognosis technique was proposed for predicting the remaining useful life (RUL) of the reaction wheels (RWs) that are widely used in the attitude control of small satellites. In the study, a version of long short-term memory recurrent neural network was used to predict future system measurements, i.e., state reconstruction.…”
Section: The Present Issuementioning
confidence: 99%
“…Machine learning (ML) algorithms are very useful for predicting the mechanical properties of composite materials [20]. To make accurate predictions or respond appropriately to new and unknown inputs, machine learning algorithms must be able to recognize patterns and relationships within datasets [21,22]. One of the key components of machine learning that underpins its many applications in a variety of disciplines is its innate capacity to learn from data, generalize to new cases, and make acceptable outcomes without having explicit instructions [23][24][25].…”
Section: Introductionmentioning
confidence: 99%