{'fraction_non_alphanumeric': 0.017112299465240642, 'fraction_numerical': 0.0, 'mean_word_length': 5.455172413793103, '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}}
Rolling Bearing Composite Fault Diagnosis Method Based On Harmonic Vector Analysis

For the problem that traditional methods are difficult to identify bearing composite faults, a determined convolution blind source separation method based on harmonic vector analysis (HVA) was studied. First, HVA uses the sparsity of cepstral coefficients to capture the co-occurrence of harmonic components. Then a Wiener-like masking is constructed to make the signals separated at each iteration more independent of each other. Finally, the back projection technique is used to align the frequency scales of separated signals. The experimental data of bearing faults verily the feasibility of this method. The simulation experiments show that, this method is better than the classical method in separating the composite fault signals of rolling bearings. Therefore, the proposed method can realize the composite fault diagnosis of rolling bearings.