A New Method for Multisensor Data Fusion Based on Wavelet Transform in a Chemical Plant

Abstract This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet "db4" functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR) and minimum square error (MSE) criteria

Multisensor fusion fault-tolerant control with diagnosis via a set separation principle

In this paper, a multisensor fusion fault-tolerant control system with fault detection and isolation via set separation is presented. The fault detection and isolation unit verifies that for each sensor-estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense, recently proposed in the literature. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and good performance under the occurrence of abrupt sensor faults