Sensor fault detection and estimation for nonlinear process with time-delayed variables
Time-delayed variables is of potentially critical impact on product quality within the processes of Metallurgical industry,Aviation industry and Chemical industry.It is very important to detect the sensor fault and estimate the values of time-delayed variables immediately as soon as faults occurred.Any fault of the time-delayed variable implies that a detrimental effect on the process will go unnoticed until the next sample become available.During this time,of course,poor quality product continues to be manufactured,a situation which is clearly unacceptable from a cost-effectiveness point of view.For nonlinear process with time-delayed variables,kernel partial least squares is introduced to monitor and estimate the fault of the Tennessee Eastman benchmark.Simulation results demonstrate the effectiveness of the proposed algorithm.
Fault detection kernel partial least square KPLS
Kechang Fu Peng Liu Ming Zhu Shiqi Jiang
Department of Control Engineering,Chengdu University of Information Technology Chengdu 610225,China
国际会议
沈阳
英文
1435-1439
2012-09-07（万方平台首次上网日期，不代表论文的发表时间）