USE OF GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORK FOR GEAR CONDITION DIAGNOSTICS

摘要

A procedure is presented for gear condition diagnostics using genetic algorithm (GA) and artificial neural network (ANN). The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from both original and preprocessed signals are used in an ANN based diagnostic approach. The output layer consists of two binary nodes indicating the status of the machine - normal or defective gears. The selection of input features and the number of nodes in the hidden layer are optimized using a GA based approach in combination with ANN. For each trial, the ANN is trained using back-propagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The roles of different vibration signals, obtained under both normal and light loads and at low and high sampling rates, are investigated. The results show the effectiveness of the proposed approach in diagnosis of the machine condition.