{'fraction_non_alphanumeric': 0.016129032258064516, 'fraction_numerical': 0.0007012622720897616, 'mean_word_length': 6.1708542713567835, '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}}
A new strategy to eliminate interference of varying operating conditions during multivariate signal processing-based fault diagnosis approach

Multivariate signal acquisition and processing of faulty mechanical components extract more fault characteristic information, contributing to comprehensive and accurate fault diagnosis. Multivariate empirical mode decomposition (MEMD) is an effective approach in the field of multivariate signal processing. While nuisance attribute projection (NAP) can be used to filter out the components of nuisance attributes caused by varying operating condition. This paper proposes a new strategy for eliminating interference during multivariate signal processing- based fault diagnosis approach. MEMD is used to decompose multivariate signal of faulty rolling bearing into multivariate IMFs depicting dynamical properties, with extracting quantitative features of multivariate IMFs. Then, NAP is used to eliminate nuisance attributes of varying operating conditions in the constructed feature matrices. To illustrate the effectiveness and superiority of the proposed new strategy, the extracted features are presented by t-distribution stochastic neighbour embedding (t-SNE). The dimensionalities of feature vectors are reduced to 3 on account of the visualization simplicity. The comparison analysis during application researches can validate the superiority of the proposed novel strategy.