An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis

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

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...

Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-numb...

A Novel Intelligent Fault Diagnosis Method and Its Application

In order to improve fault diagnosis precision and decrease misinformation diagnosis, rough set theory(RST) and RBF neural network (RBFNN) are introduced to overcome respective deficiency in order to propose a novel intelligence fault diagnosis(NCIRRFD) model and method in this paper. In this NCIRRFD method, the RST as a new mathematical tool is used to process inexact and uncertain knowledge in...

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

As an important tool of information fusion, Dempster-Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data...

Multisensor Data Fusion