TY - GEN
T1 - Detecting continual anomalies in monitoring data stream based on sampling GPR algorithm
AU - Pang, Jingyue
AU - Liu, Datong
AU - Peng, Yu
AU - Peng, Xiyuan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - Prognostics and health management (PHM) can improve the system availability and efficiency to realize Condition Based Maintenance (CBM). As the primary and critical process of PHM, the anomaly detection can discover the abnormal condition and potential fault in time. Generally, monitoring data can reflect the operating condition of components, subsystems or systems. Thus, the monitoring data can be applied to detect the anomalies to improve the system readness and help the system health management. However, monitoring data arriving in the form of streaming gradually shows the growth in variability, velocity and volume. As a result, detecting the anomalies in data stream brings new challenges to traditional anomaly detection algorithm. In this case, this paper proposes a sampling Gaussian process regression (GPR) method for continual anomaly detection based on the specialty of streaming data, providing important information for estimating the system conditon. The effectiveness of this method is evaluated by the synthetic datasets and public data sets.
AB - Prognostics and health management (PHM) can improve the system availability and efficiency to realize Condition Based Maintenance (CBM). As the primary and critical process of PHM, the anomaly detection can discover the abnormal condition and potential fault in time. Generally, monitoring data can reflect the operating condition of components, subsystems or systems. Thus, the monitoring data can be applied to detect the anomalies to improve the system readness and help the system health management. However, monitoring data arriving in the form of streaming gradually shows the growth in variability, velocity and volume. As a result, detecting the anomalies in data stream brings new challenges to traditional anomaly detection algorithm. In this case, this paper proposes a sampling Gaussian process regression (GPR) method for continual anomaly detection based on the specialty of streaming data, providing important information for estimating the system conditon. The effectiveness of this method is evaluated by the synthetic datasets and public data sets.
KW - Sampling GPR
KW - anomoly deteciton
KW - continual anomalies
KW - data stream
KW - prognostics and health management
UR - https://www.scopus.com/pages/publications/84957916441
U2 - 10.1109/ICPHM.2015.7245071
DO - 10.1109/ICPHM.2015.7245071
M3 - 会议稿件
AN - SCOPUS:84957916441
T3 - 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015
BT - 2015 IEEE Conference on Prognostics and Health Management
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Prognostics and Health Management, PHM 2015
Y2 - 22 June 2015 through 25 June 2015
ER -