{'fraction_non_alphanumeric': 0.024213075060532687, 'fraction_numerical': 0.0, 'mean_word_length': 5.511811023622047, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Faults classification of asynchronous machine based on the probabilistic neural network (PNN)

In this paper, a probabilistic neural network (PNN) based approach for fault diagnostic in case of faults affecting asynchronous machines is proposed. Performances of the proposed probabilistic neural network are compared to those of Multi-Layer Perceptron (MLP) and Generalized Regression Neural Network (GRNN), using a cross-validation procedure to provide better generalization of neural network classifiers. To perform efficient diagnostic results the cross-validation procedure input data is partitioned into three sets: a training set, a validation set and a test set. The stator RMS values of three-phase voltages and currents are used as model inputs to identify the different types of faults and the normal operating mode.