TY - JOUR
T1 - Online Failure Prediction for Railway Transportation Systems Based on Fuzzy Rules and Data Analysis
AU - Ding, Zuohua
AU - Zhou, Yuan
AU - Pu, Geguang
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received October 23, 2016; revised April 10, 2017, August 9, 2017, October 24, 2017, and December 8, 2017; accepted January 7, 2018. Date of publication October 5, 2018; date of current version August 30, 2018. This work was supported by NSFC 61751210. Associate Editor: M. Grottke. (Corresponding author: Zuohua Ding.) Z. Ding is with the School of Information Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China (e-mail:,zouhuading@hotmail.com).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - Nowadays, software systems have been more and more complex, which causes great challenges to maintain the availability of the systems. Online failure prediction provides an effective approach to guaranteeing the validity of the systems. Most of the current technologies for online failure prediction require some prior knowledge, such as the model of the system or failure patterns. This paper proposes a new method based on fuzzy rules and time series analysis. Specifically, fuzzy rules are used to model the relationships among different variables, whereas univariate time series analysis is used to describe the evolution of each variable. Thus, for a dependent variable, we have two predicted values: one is from the time series model, and the other is computed from fuzzy rules with fuzzy inference. If the difference between the two values exceeds a threshold, then we declare that there would be a failure in some time period ahead. Different from the existing methods, the proposed method considers not only the evolutionary trend of each variable but also the relationships among different variables. Moreover, we do not need any prior knowledge such as system model or failure patterns. We use a railway transportation system as an example to illustrate our method.
AB - Nowadays, software systems have been more and more complex, which causes great challenges to maintain the availability of the systems. Online failure prediction provides an effective approach to guaranteeing the validity of the systems. Most of the current technologies for online failure prediction require some prior knowledge, such as the model of the system or failure patterns. This paper proposes a new method based on fuzzy rules and time series analysis. Specifically, fuzzy rules are used to model the relationships among different variables, whereas univariate time series analysis is used to describe the evolution of each variable. Thus, for a dependent variable, we have two predicted values: one is from the time series model, and the other is computed from fuzzy rules with fuzzy inference. If the difference between the two values exceeds a threshold, then we declare that there would be a failure in some time period ahead. Different from the existing methods, the proposed method considers not only the evolutionary trend of each variable but also the relationships among different variables. Moreover, we do not need any prior knowledge such as system model or failure patterns. We use a railway transportation system as an example to illustrate our method.
KW - Autoregressive integrated moving average (ARIMA)
KW - failure prediction
KW - fuzzy rules
KW - railway transportation system
KW - time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85046727599&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046727599&partnerID=8YFLogxK
U2 - 10.1109/TR.2018.2828113
DO - 10.1109/TR.2018.2828113
M3 - Article
AN - SCOPUS:85046727599
SN - 0018-9529
VL - 67
SP - 1143
EP - 1158
JO - IRE Transactions on Reliability and Quality Control
JF - IRE Transactions on Reliability and Quality Control
IS - 3
M1 - 8357501
ER -