TY - GEN
T1 - A data driven sensor fault tolerant scheme for nonlinear systems
AU - Yu, Han
AU - Jiang, Yuchen
AU - Yin, Shen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - In this paper, a data driven sensor fault tolerant strategy is proposed for nonlinear systems. The core of the proposed strategy is just-in-time learning based soft sensor. When sensor fault occurs, the value of soft sensor is adopted as the redundancy instead of the real faulty sensor value. Meanwhile, the influence of the sensor fault can be tolerated. Due to the complexity of mechanism model for nonlinear system, a kind of just-in-time learning method is employed for soft sensor. The indexes will be predicted by just-in-time learning method online. And only historical sample data will be used for prediction. Instead of considering system global model as normal soft-sensor approaches, just-in-time learning methods only consider the approximate system at current time. Thus JITL owns strong capacity of on-line implementation. Two nonlinear systems, a typical numerical one and a benchmark of wastewater treatment system, are employed for experiments. The experiment results verify the accuracy and implementability of the proposed scheme.
AB - In this paper, a data driven sensor fault tolerant strategy is proposed for nonlinear systems. The core of the proposed strategy is just-in-time learning based soft sensor. When sensor fault occurs, the value of soft sensor is adopted as the redundancy instead of the real faulty sensor value. Meanwhile, the influence of the sensor fault can be tolerated. Due to the complexity of mechanism model for nonlinear system, a kind of just-in-time learning method is employed for soft sensor. The indexes will be predicted by just-in-time learning method online. And only historical sample data will be used for prediction. Instead of considering system global model as normal soft-sensor approaches, just-in-time learning methods only consider the approximate system at current time. Thus JITL owns strong capacity of on-line implementation. Two nonlinear systems, a typical numerical one and a benchmark of wastewater treatment system, are employed for experiments. The experiment results verify the accuracy and implementability of the proposed scheme.
KW - Data driven
KW - fault detection
KW - fault tolerant
KW - just-in-time learning
KW - nonlinear system
UR - https://www.scopus.com/pages/publications/85046697567
U2 - 10.1109/IECON.2017.8217234
DO - 10.1109/IECON.2017.8217234
M3 - 会议稿件
AN - SCOPUS:85046697567
T3 - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
SP - 7055
EP - 7060
BT - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Y2 - 29 October 2017 through 1 November 2017
ER -