Abstract: In view of the defect that the vibration signal collected by a single sensor is difficult to accurately describe the dynamic characteristics of rotating machinery, which lead to the inability to accurately identify the fault characteristics of rotating machinery. A fault diagnosis method for rotating machinery based on composite multivariate multiscale fluctuation dispersion entropy (CMMFDE), multi-sensor information fusion and Harris hawk algorithm optimized extreme learning machine (HHO-ELM) was proposed. Firstly, the composite multivariate coarse-grained processing was introduced and the CMMFDE method was proposed, the defect of traditional univariate analysis methods was avoided, it could only handle single channel vibration signals, resulting in insufficient feature characterization performance, and the representation performance of fault features was enhanced. Then, sensors arranged in different parts of the rotating machinery were used to collect various types of signals to compose mixed multi-channel signals, and CMMFDE analysis was carried out to construct fault characteristics. Finally, HHO was used to optimize the parameters of the extreme learning machine, and the feature samples were trained and tested to complete the fault identification of the rotating machine. Two typical rotating machinery data sets of gear box and centrifugal pump were used for experimental analysis. The research results show that the accuracy obtained when analyzing signals from multiple channels reaches 100% and 98%, which is superior to the accuracy obtained when analyzing single channel signals. At the same time, the recognition accuracy and feature extraction time of the CMMFDE method are superior to the refined composite multivariate multiscale sample entropy (RCMMSE), refined composite multivariate multiscale fuzzy entropy (RCMMFE), refined composite multivariate multiscale permutation entropy (RCMMPE), and multivariate multiscale fluctuation dispersion entropy (MMFDE).
Key words:  rotating machinery; fault diagnosis; gear box; centrifugal pump; composite multivariate multiscale fluctuation dispersion entropy (CMMFDE); Harris hawk algorithm optimization extreme learning machine (HHOELM)