Pattern classification by a neurofuzzy network: application to vibration monitoring

Abstract

An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fisher's Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.