Title :
Fault-tolerant mining algorithm of sampling data from dynamic system
Author :
Hu Shaolin ; Li Ye ; Zhang Dong
Author_Institution :
State Key Lab. of Astronaut., Xi´an, China
Abstract :
Time series data mining is an useful tool for us to design data-driven condition monitoring as well as fault diagnosis system. Aiming at monitoring abnormal changes of dynamic process, a series of mining algorithms are built up to mine signal form, model structure of process and statistical properties of noise in sampling data series, the architecture of information mining system of sampling time series is set up. These algorithms are very fault tolerant for patchy outliers in sampling data set of the complex system. Results given in this paper can be used not only in the safety analysis and fault diagnosis of complicated dynamic process but also in change detection as well as other related fields.
Keywords :
condition monitoring; data mining; fault diagnosis; production engineering computing; safety; sampling methods; time series; change detection; data-driven condition monitoring; dynamic system; fault diagnosis system; fault-tolerant mining algorithm; information mining system; patchy outlier; safety analysis; sampling data; statistical property; time series data mining; Data mining; Databases; Fault diagnosis; Fault tolerance; Fault tolerant systems; Noise; Time series analysis; Data Mining; Fault-tolerant Mining; Knowledge Discovery; Time Series;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561826
