A Hybrid Multi-Experts Approach for Mechanical Defects´ Detection and Diagnosis

Compared with parametric classifiers, several advantages set neural networks as privileged approaches to be used as discriminating classifiers in performing diagnosis tasks. In this paper, we present a hybrid multi-experts neural based architecture for mechanical defects´ detection and diagnosis. This solution is evaluated within vibratory analysis frame using a wavelet transform faults´ detection scheme.