{'fraction_non_alphanumeric': 0.0125, 'fraction_numerical': 0.0, 'mean_word_length': 5.243119266055046, '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}}
Wavelet-based diagnostic model for rotating machinery subject to vibration monitoring

This paper proposes a new diagnosis method based on the wavelet transform with fuzzy theory in order to improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of turbo-generator sets. To increase the signal-noise-ratio, a novel method based on the statistic rule is brought forward to determine the threshold of each order of wavelet space and the decomposition level adaptively. The binary discrete wavelet transform is used to acquire effective eigenvectors. The fuzzy diagnosis equation based on correlation matrix is used to classify the fault modes. The network structure is obtained by establishing the fault diagnosis model of turbo-generator set and using the improved least squares algorithm. Also the robustness of fault diagnosis equation is discussed in this paper. The faults are input into the trained diagnosis equation by means of choosing enough samples to train the fault diagnosis equation and the information representing. The type of fault can be determined according to the output result. The experiment results show that multi-concurrent fault for stator temperature fluctuation and rotor vibration can be diagnosed effectively by this new method and the diagnosis result is correct.