Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Unsupervised multisensor data fusion approach

A new iterative approach of multisensor data fusion based on the Dempster-Shafer formalism is presented. Mass functions, formalized by a Gaussian model, are estimated at each iteration using the output fused image and the source images. The effectiveness of the method is demonstrated on synthetic images.

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...

Multisensor Image Fusion Using a Region-Based Wavelet Transform Approach

Using multiple sensors in a vision system can significantly reduce both human and machine errors in detection and recognition of objects. A particular case of interest is where images from possibly different types of sensors are to be combined. An image fusion scheme is proposed which combines aspects of feature-level fusion with the pixel-level fusion. Images are fused by combining their wavel...

Multisensor data fusion using fuzzy concepts

In this study, a fuzzy-based multisensor data fusion classifier is developed and applied to land cover classification using ERS-1/JERS-1 SAR composites. This classifier aims at the integration of multisensor and contextual information in a single and a homogeneous framework. Initial Fuzzy Membership Maps (FMM) to different thematic classes are first calculated using classes and sensors a priori...

Multisensor Data Fusion