{'fraction_non_alphanumeric': 0.025538707102952914, 'fraction_numerical': 0.0, 'mean_word_length': 4.9714285714285715, '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}}
Research on fault diagnosis of servo valve based on deep learning

Aiming at some characteristics of servo valves that the complex influence between the failure modes and the high order nonlinearity of the fault datum, this paper presents a fault diagnosis model-Deep Intelligent Generalized Regression Neural Network (DGN). The DGN is a supervised deep learning model. In order to fully learning the fault datum, this paper proposed a logistic mapping and dynamic step fruit fly algorithm (lFOA-η). Then the model introduces the improved Fruit Fly Optimization Algorithm (lFOA-η) on the basis of the Generalized Regression Neural Network (GRNN). The lFOA-η and GRNN are optimized to each other, forming the basis of training layer and deep training layer. The training layer and deep training layer are optimized and feedback information to each other. And finally the model structure is formed as input layer, training layer, deep training layer, and output layer. This structure improves the nonlinear approximation of the model and the convergence rate. The experimental results show that the accuracy and speed of this paper's model are improved with respect to the BP, SVM and deep belief network (DBN) model, and the application prospect is good.