A neurofuzzy network and its application to machine health monitoring

An innovative neurofuzzy network is proposed for pattern classification applications to machine health 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, online, and incremental learning algorithm. To evaluate the proposed network, the numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a U.S. Navy CH-46E helicopter test stand. The proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.