{'fraction_non_alphanumeric': 0.012032085561497326, 'fraction_numerical': 0.0, 'mean_word_length': 5.294117647058823, '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}}
Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. To improve the diagnosis accuracy a method that combines the multi-class support vector machines (MSVMs) outputs with the degree of importance of individual MSVMs based on fuzzy integral is presented. This provides a sound basis for integrating the results from MSVMs to get more accurate classification. The experimental results with the recognition problem of a blower machine show the performance of fault diagnosis can be improved.