A Fault Detection and Identification System for Gearboxes using Neural Networks

This paper concentrates on a new procedure which experimentally recognizes gears and bearings faults of a typical gearbox system using a multi-layer perceptron neural network. Feature vector which is one of the most significant parameters to design an appropriate neural network was innovated by standard deviation of wavelet packet coefficients. The gear conditions were considered to be normal gearbox and slight- and medium-worn and broken-teeth gears faults and a general bearing fault which were five neurons of output layer with the aim of fault detection and identification. A downscaled 2-layer multi-layer perceptron neural-network-based system with great accuracy was designed to carry out the task. Vibration signals were recognized as the most reliable source to extract the feature vector which were by piecewise cubic Hermite interpolation synchronized and pre-processed using the standard deviation of wavelet packet coefficients in this research