Optimizing Probabilistic Neural Networks by the Use of Genetic Algorithms for Rolling Bearing Fault Diagnosis
نویسندگان
چکیده
The present work analyses the use of the Probabilistic Neural Network (PNN) as an automatic diagnosis system for detecting defects in rolling bearings using vibration signals. The influence of the metric and that of the mono and multi sigma in the PNN performance is analyzed and discussed. Genetic algorithms are used in order to optimize the sigma set that maximizes the PNN detection and classification performance. Two forms of training sets were constructed, one using the power spectral density of the signals and another using a blend of scalar parameters. Different classification complexities were employed. The results allow obtaining excellent classification rate as well as analyzing the influence of the main PNN parameters in its performance.
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