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- W4320038034 abstract "Actuator defects can easily lead to the thrust loss of rotors and rise of power consumption. Thus, it is important to study performance assessment strategy to estimate the degradation level and next value of health indicator (HI) for a quadrotor. This paper introduces a method of predictive maintenance (PdM) for quadrotor propulsion system, consisting of two parts that are degradation level classification and HI forecast. The sensor signals are transformed into several time-domain features and ten representative features are selected by Spearman correlation coefficient. Next, a long short-term memory (LSTM) neural network is used to evaluate current status and health indicator of the system by modeling intricate relation between sensor signals and degradation pattern. A series of experiments is based on DJI Mavic Air quadrotor drone by progressively making blade damage to simulate the propulsion degradation. The classification and forecast result of validation data set verify the effectiveness of this data-based PdM approach." @default.
- W4320038034 created "2023-02-12" @default.
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- W4320038034 date "2023-01-01" @default.
- W4320038034 modified "2023-10-14" @default.
- W4320038034 title "A Data-Driven Predictive Maintenance Method for Quadrotor Propulsion System Based on LSTM Network" @default.
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- W4320038034 doi "https://doi.org/10.1007/978-981-19-6613-2_602" @default.
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