TY - GEN
T1 - Anomalous sensing data recovery with mutual information and Relevance Vector Machine
AU - Liansheng, Liu
AU - Datong, Liu
AU - Yu, Peng
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Sensing data are the basic input for system prognostics and health management. However, sensing data may become anomalous due to the sensor fault and failure, malfunction of connectors, etc. The sensing anomaly data could bring wrong prognostics result and unreasonable maintenance schedule. The problem becomes more challenging when the sensing data are sparse, i.e., only a few sensors are available for utilization. To deal this problem, the sensing anomaly data recovery approach is proposed in this article. The relationship among sensing data is analyzed by mutual information to select the maximal relevant training data for anomaly data recovery. One dimensional training data to recover anomaly data is achieved by Relevance Vector machine which has the feature of sparsity. The effectiveness of the proposed approach is evaluated by utilizing the Prognostics and Health Management 2008 Challenge data.
AB - Sensing data are the basic input for system prognostics and health management. However, sensing data may become anomalous due to the sensor fault and failure, malfunction of connectors, etc. The sensing anomaly data could bring wrong prognostics result and unreasonable maintenance schedule. The problem becomes more challenging when the sensing data are sparse, i.e., only a few sensors are available for utilization. To deal this problem, the sensing anomaly data recovery approach is proposed in this article. The relationship among sensing data is analyzed by mutual information to select the maximal relevant training data for anomaly data recovery. One dimensional training data to recover anomaly data is achieved by Relevance Vector machine which has the feature of sparsity. The effectiveness of the proposed approach is evaluated by utilizing the Prognostics and Health Management 2008 Challenge data.
KW - Data recovery
KW - prognostics and health management
KW - remaining useful life prediction
UR - https://www.scopus.com/pages/publications/85047120077
U2 - 10.1109/ICEMI.2017.8265779
DO - 10.1109/ICEMI.2017.8265779
M3 - 会议稿件
AN - SCOPUS:85047120077
T3 - ICEMI 2017 - Proceedings of IEEE 13th International Conference on Electronic Measurement and Instruments
SP - 247
EP - 252
BT - ICEMI 2017 - Proceedings of IEEE 13th International Conference on Electronic Measurement and Instruments
A2 - Juan, Wu
A2 - Jiali, Yin
A2 - Qi, Zhang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2017
Y2 - 20 October 2017 through 22 October 2017
ER -