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In this paper one method for bearings diagnosis is proposed and evaluated. This method use signal pattern recognition from mechanical vibrations. Wavelet and Fourier transforms are used for pre-processing the signal and an Artificial Neural Network (ANN) is used as a classifier. Analysis of variance (ANOVA) is used for evaluating the ANN inputs. ANOVA is performed to compare the effect of the factors: speed, load, outer race fault and rolling element fault on each of the parameters proposed as inputs of the ANN, looking for the best parameters for classifying the faults. About 2000 ANN structures were trained in order to find the most appropriate classifier. The results show that the average of success in classifying was 88,5 % for the scaled conjugate gradient algorithm (trainscg), while the Levenberg Marquardt algorithm (trainlm) presented 91,8 %. Besides, it was possible to achieve 100 % of success in classifying in 7 cases. [ABSTRACT FROM AUTHOR]

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network. [ABSTRACT FROM AUTHOR]