TY - GEN
T1 - Anomaly detection based on data stream monitoring and prediction with improved Gaussian process regression algorithm
AU - Pang, Jingyue
AU - Liu, Datong
AU - Liao, Haitao
AU - Peng, Yu
AU - Peng, Xiyuan
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/2/9
Y1 - 2015/2/9
N2 - Condition monitoring has gradually become the necessary part of the diagnostics and prognostics for the complex systems. Especially, with the rapid development of data acquisition and communication technology, the appearing of large scale data set and data stream brings great challenges to model and process the condition monitoring data As a result, anomaly detection of the streaming monitoring data attracts more attention in the fields of prognostics and health management (PHM). Hence, in this study, Gaussian process regression (GPR) is applied for the abnormal detection in data stream; and on this basis a real-time abnormal detection method is proposed based on the improved anomaly detection and mitigation (IADAM) strategy and GPR which realizes incremental detecting for future data samples and requires no pre-classification labels of anomalies. Anomaly detection tested on an artificial data set and actual mobile traffic data set indicates the effectiveness and reasonability of IADAM-GPR model compared with naïve and Multilayer Perceptron (MLP) models.
AB - Condition monitoring has gradually become the necessary part of the diagnostics and prognostics for the complex systems. Especially, with the rapid development of data acquisition and communication technology, the appearing of large scale data set and data stream brings great challenges to model and process the condition monitoring data As a result, anomaly detection of the streaming monitoring data attracts more attention in the fields of prognostics and health management (PHM). Hence, in this study, Gaussian process regression (GPR) is applied for the abnormal detection in data stream; and on this basis a real-time abnormal detection method is proposed based on the improved anomaly detection and mitigation (IADAM) strategy and GPR which realizes incremental detecting for future data samples and requires no pre-classification labels of anomalies. Anomaly detection tested on an artificial data set and actual mobile traffic data set indicates the effectiveness and reasonability of IADAM-GPR model compared with naïve and Multilayer Perceptron (MLP) models.
KW - Anomaly detection and mitigation
KW - Anomoly deteciton
KW - Data stream
KW - Gaussian process regression
KW - Hypothesis testing
UR - http://www.scopus.com/inward/record.url?scp=84929587516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929587516&partnerID=8YFLogxK
U2 - 10.1109/ICPHM.2014.7036394
DO - 10.1109/ICPHM.2014.7036394
M3 - Conference contribution
AN - SCOPUS:84929587516
T3 - 2014 International Conference on Prognostics and Health Management, PHM 2014
BT - 2014 International Conference on Prognostics and Health Management, PHM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Prognostics and Health Management, PHM 2014
Y2 - 22 June 2014 through 25 June 2014
ER -