TY - GEN
T1 - Health indicator extraction for electro-mechanical actuator with CHMM
AU - Zhang, Yujie
AU - Liu, Liansheng
AU - He, Min
AU - Lyu, Dangxia
AU - Peng, Yu
AU - Liu, Datong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Electro-Mechanical Actuator (EMA) has played an important role as there are more EMAs incorporated in the flight control actuation of More Electric Aircraft. However, the difficulty of EMA Health Indicator (HI) extraction caused by limitation of sensor installation hinders the development of EMA Prognostic and Health Management (PHM). As a result, to address this issue, a new HI extraction method based on Continuous Hidden Markov Model (CHMM) is proposed for EMA. In the CHMM-based HI extraction method, the monitoring data in health condition of EMA are utilized to train a CHMM with log-likelihood function. Based on the CHMM and the monitoring data in the degradation condition of EMA, the output of log-likelihood function for EMA degradation condition can be obtained, which implies the similarity between the degradation condition and health condition. Furthermore, the normalized similarity is used as an EMA HI. Thus, the sensors with high correlation to the extracted HI (i.e. normalized similarity) no longer need to be installed or can be removed. This study provides a new way of EMA HI extraction with the limitation of sensor installation. To validate the effectiveness of CHMM-based HI extraction method for EMA, experiments are conducted, in which the data derived from NASAs Flyable Electro-Mechanical Actuator (FLEA) test stand are utilized. Experimental results show that the CHMM-based method has a good performance in EMA HI extraction.
AB - Electro-Mechanical Actuator (EMA) has played an important role as there are more EMAs incorporated in the flight control actuation of More Electric Aircraft. However, the difficulty of EMA Health Indicator (HI) extraction caused by limitation of sensor installation hinders the development of EMA Prognostic and Health Management (PHM). As a result, to address this issue, a new HI extraction method based on Continuous Hidden Markov Model (CHMM) is proposed for EMA. In the CHMM-based HI extraction method, the monitoring data in health condition of EMA are utilized to train a CHMM with log-likelihood function. Based on the CHMM and the monitoring data in the degradation condition of EMA, the output of log-likelihood function for EMA degradation condition can be obtained, which implies the similarity between the degradation condition and health condition. Furthermore, the normalized similarity is used as an EMA HI. Thus, the sensors with high correlation to the extracted HI (i.e. normalized similarity) no longer need to be installed or can be removed. This study provides a new way of EMA HI extraction with the limitation of sensor installation. To validate the effectiveness of CHMM-based HI extraction method for EMA, experiments are conducted, in which the data derived from NASAs Flyable Electro-Mechanical Actuator (FLEA) test stand are utilized. Experimental results show that the CHMM-based method has a good performance in EMA HI extraction.
KW - Continuous hidden markov model
KW - Degradation
KW - Electro-mechanical actuator
KW - Health indicator
KW - Prognostic health management
UR - https://www.scopus.com/pages/publications/85072829368
U2 - 10.1109/I2MTC.2019.8826930
DO - 10.1109/I2MTC.2019.8826930
M3 - 会议稿件
AN - SCOPUS:85072829368
T3 - I2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
BT - I2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
T2 - 2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019
Y2 - 20 May 2019 through 23 May 2019
ER -