TY - GEN
T1 - Multi-stage Degrading Process Modeling with Change Point Detecting
AU - Sun, Xiaotong
AU - Jie, Xun
AU - Zhao, Guangyan
AU - Yuan, Yuxi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/4
Y1 - 2019/1/4
N2 - As the changes in the external environment and internal mechanisms, some components of high-reliability products may show the multi-stage failure processes under long-term operation. That is a mutation occurring in a degradation path at some time which we called change points segmenting the degradation process. It is not caused by external shocks but due to the qualitative change caused by the degradation of natural degradation to a certain extent. However, it hard to get the value and position of the change point. In this paper, we establish the model of multi-stage with change point degrading process and give its parameter estimation. Furthermore, we detect whether the change points exist in the degrading process and determine the time interval where the change point happens by using Schwarz information criterion (SIC) iteratively. We also introduce the experiment likelihood method to detect the existence of change points when the degrading process distribution is unknown. Finally, an experimental analysis is conducted by using SIC method iteratively to verify its validity and flexibility in the change point detection.
AB - As the changes in the external environment and internal mechanisms, some components of high-reliability products may show the multi-stage failure processes under long-term operation. That is a mutation occurring in a degradation path at some time which we called change points segmenting the degradation process. It is not caused by external shocks but due to the qualitative change caused by the degradation of natural degradation to a certain extent. However, it hard to get the value and position of the change point. In this paper, we establish the model of multi-stage with change point degrading process and give its parameter estimation. Furthermore, we detect whether the change points exist in the degrading process and determine the time interval where the change point happens by using Schwarz information criterion (SIC) iteratively. We also introduce the experiment likelihood method to detect the existence of change points when the degrading process distribution is unknown. Finally, an experimental analysis is conducted by using SIC method iteratively to verify its validity and flexibility in the change point detection.
KW - Change points
KW - Degrading process
KW - Experiment likelihood
KW - Schwarz Information Criterion
UR - https://www.scopus.com/pages/publications/85061767723
U2 - 10.1109/PHM-Chongqing.2018.00132
DO - 10.1109/PHM-Chongqing.2018.00132
M3 - 会议稿件
AN - SCOPUS:85061767723
T3 - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
SP - 741
EP - 745
BT - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
A2 - Ding, Ping
A2 - Li, Chuan
A2 - Yang, Shuai
A2 - Ding, Ping
A2 - Sanchez, Rene-Vinicio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Y2 - 26 October 2018 through 28 October 2018
ER -