Data-driven diagnosis of sensor precision degradation in the presence of control

This paper considers the precision degradation type of sensor faults within control loops. In a closed loop, sensor faults propagate through controller to manipulated variables and disturb the other process variables, which obscures the source of sensor faults but receives less attention in existing methods of data-driven sensor fault diagnosis. With the assumption that only closed-loop data in normal condition are available, difficulty arises due to the facts that little a priori knowledge is known about closed-loop sensor fault propagation and the open-loop process model may not be identifiable. The proposed method in this paper constructs residual that is regarded as including two parts: the first part is the current sensor faults whose fault direction is known to be the identity matrix; and for the purpose of diagnosing the first part, the second part is considered as the disturbance which is affected by noises and past sensor faults due to unknown fault propagation. The disturbance variance is minimized in residual generator design to improve fault sensitivity. And the corresponding disturbance covariance is estimated and then utilized in residual evaluation. The proposed method in this paper is motivated by a pioneer work on closed-loop sensor fault diagnosis which performs principal component analysis in the feedback-invariant subspace of the closed-loop process outputs. But it is revealed by the proposed method that the feedback-invariant signal is affected by past sensor faults, leading to performance degradation of the pioneer work. The improvement of the proposed approach is due to analysis of residual dynamics and explicit handling of the disturbance in residual evaluation, which is not considered in the pioneer work. A simulated 4 x 4 dynamic process and a simulated two-product distillation column are studied to verify the effectiveness of the proposed approach compared to the existing principal component analysis method in feedback-invariant subspace. (C) 2011 Elsevier Ltd. All rights reserved.