TY - GEN
T1 - Performance Degradation Prediction of Aircraft Auxiliary Power Unit Using the Improved SVR
AU - Liu, Xiaolei
AU - Liu, Liansheng
AU - Wang, Lulu
AU - Peng, Xiyuan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/17
Y1 - 2021/5/17
N2 - The auxiliary power unit (APU) can provide compressed air and electric power for aircraft. The accurate performance degradation prediction of APU can not only provide information for condition-based maintenance, but also ensure the safety of the aircraft to a certain degree. However, due to its complexity and the stochastic working condition, it is difficult to achieve accurate prediction results by traditional time series analysis methods. To address this issue, a probabilistic prediction method named as the improved support vector regression (SVR) is proposed. Firstly, the Gaussian process regression is utilized to capture the trend features of the performance data. Then, these features are used as the input of SVR to predict the performance degradation of the APU. The improved SVR is evaluated by the real on-wing monitoring data of the APU. Compared with other two methods, experimental results show that the improved SVR obtains better prediction results.
AB - The auxiliary power unit (APU) can provide compressed air and electric power for aircraft. The accurate performance degradation prediction of APU can not only provide information for condition-based maintenance, but also ensure the safety of the aircraft to a certain degree. However, due to its complexity and the stochastic working condition, it is difficult to achieve accurate prediction results by traditional time series analysis methods. To address this issue, a probabilistic prediction method named as the improved support vector regression (SVR) is proposed. Firstly, the Gaussian process regression is utilized to capture the trend features of the performance data. Then, these features are used as the input of SVR to predict the performance degradation of the APU. The improved SVR is evaluated by the real on-wing monitoring data of the APU. Compared with other two methods, experimental results show that the improved SVR obtains better prediction results.
KW - Auxiliary power unit
KW - condition-based maintenance
KW - improved support vector regression
KW - performance degradation prediction
UR - https://www.scopus.com/pages/publications/85113710423
U2 - 10.1109/I2MTC50364.2021.9460110
DO - 10.1109/I2MTC50364.2021.9460110
M3 - 会议稿件
AN - SCOPUS:85113710423
T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference
BT - I2MTC 2021 - IEEE International Instrumentation and Measurement Technology Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2021
Y2 - 17 May 2021 through 20 May 2021
ER -