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Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter./
作者:	Alidadi, Mahdi.
面頁冊數:	1 online resource (70 pages)
附註:	Source: Masters Abstracts International, Volume: 84-09.
Contained By:	Masters Abstracts International84-09.
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798374412352

Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter.
Alidadi, Mahdi.

Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter. - 1 online resource (70 pages)

Source: Masters Abstracts International, Volume: 84-09.

Thesis (M.A.Sc.)--University of Windsor (Canada), 2023.

Includes bibliographical references

Reaction wheel (RW), the most common Attitude Control Systems (ACS) in satellites, are highly prone to failure. A satellite needs to be oriented in a particular direction to maneuver and accomplish its mission goals; losing the RW can lead to a complete or partial mission failure. Therefore, estimating the remaining useful life (RUL) in long and short spans can be extremely valuable. The short-period prediction allows the satellite's operator to manage and prioritize mission tasks based on the RUL and increases the chances of a total mission failure becoming a partial one. Studies show that lack of proper bearing lubrication and uneven frictional torque distribution, which lead to variation in motor torque, are the leading causes of failure in RWs. Hence, this study aims to develop a three-step prognostic method for longterm RUL estimation of RWs based on the remaining lubricant for the bearing unit and potential fault in the supplementary lubrication system. In the first step of this method, the temperature of the lubricants is estimated as the non-measurable state of the system, using a proposed Adaptive particle filter (APF) with an-gular velocity and motor current of RW as the available measurements. In the second step, the estimated lubricant's temperature and amount of injected lubrication in the bearing alongside the lubrication degradation model are fed to a two-step Particle Filter (PF) for online model parameter estimation. In the last step, the performance of the proposed prognostics method is evaluated by predicting the RW's RUL under two fault scenarios, including excessive loss of lubrication and insufficient injection of lubrication. The results show promising performance for the proposed scheme with accuracy in estimation of degradation model's parameters around 2-3% of root mean squared percentage error (RMSPE) and prediction of RUL around 0.1- 4% percentage error.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798374412352Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

EstimationIndex Terms--Genre/Form:

554714
Electronic books.

Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter.
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502 $a Thesis (M.A.Sc.)--University of Windsor (Canada), 2023.
504 $a Includes bibliographical references
520 $a Reaction wheel (RW), the most common Attitude Control Systems (ACS) in satellites, are highly prone to failure. A satellite needs to be oriented in a particular direction to maneuver and accomplish its mission goals; losing the RW can lead to a complete or partial mission failure. Therefore, estimating the remaining useful life (RUL) in long and short spans can be extremely valuable. The short-period prediction allows the satellite's operator to manage and prioritize mission tasks based on the RUL and increases the chances of a total mission failure becoming a partial one. Studies show that lack of proper bearing lubrication and uneven frictional torque distribution, which lead to variation in motor torque, are the leading causes of failure in RWs. Hence, this study aims to develop a three-step prognostic method for longterm RUL estimation of RWs based on the remaining lubricant for the bearing unit and potential fault in the supplementary lubrication system. In the first step of this method, the temperature of the lubricants is estimated as the non-measurable state of the system, using a proposed Adaptive particle filter (APF) with an-gular velocity and motor current of RW as the available measurements. In the second step, the estimated lubricant's temperature and amount of injected lubrication in the bearing alongside the lubrication degradation model are fed to a two-step Particle Filter (PF) for online model parameter estimation. In the last step, the performance of the proposed prognostics method is evaluated by predicting the RW's RUL under two fault scenarios, including excessive loss of lubrication and insufficient injection of lubrication. The results show promising performance for the proposed scheme with accuracy in estimation of degradation model's parameters around 2-3% of root mean squared percentage error (RMSPE) and prediction of RUL around 0.1- 4% percentage error.
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650 4 $a Aerospace engineering. $3 686400
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653 $a Lubrication decay
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