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A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two-class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.

A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs), namely, multilayer perceptron (MLP), radial basis function (RBF) network, and probabilistic neural network (PNN). The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault) recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA). For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition

In this work, energy-based features are introduced for monitoring and diagnosis of machine conditions in spite of speed and load variations. The basic feature, termed here the energy index ( EI ), is a statistical measure of relative energy levels of segments of a time domain signal over a cycle. The properties of the EI are discussed and its diVerent forms are derived. A procedure is presented for fault diagnosis of gears using the proposed features. As an illustration, time domain acoustic emission (AE ) signals of a test gearbox have been processed to extract these features and to test their relative signi cance in the diagnostic process. The proposed technique is compared with some of the existing methods using the same AE data for early fault detection. The applicability of the proposed technique is also studied using a set of vibration data of a helicopter drivetrain system gearbox. The results show the eVectiveness of the proposed features in monitoring and diagnosis of machine conditions, with the capability of early fault detection.