For bearing fault diagnosis at time-varying speed with tachometer-free and non-resampling, the crucial process is to obtain a high-resolution time-frequency representation and extract fault features. However, current multi-component non-stationary signal feature extraction methods based on time-frequency transform suffer from fixed parameter settings and insufficient resolution for low signal-to-noise ratio signals. To address these issues, a novel adaptive clustered fractional Gabor transform is proposed and applied to extract bearing fault features at time-varying speed. Firstly, the Grey Wolf optimization is utilized to adaptively search for the optimal fractional order and Gauss window length based on the maximum spectral kurtosis and the generalized time-bandwidth product to achieve the most adequate fractional Gabor spectrum. Then, the Clustering by Fast Search and Find of Density Peaks algorithm reconstructs the sparse representation of the fractional Gabor spectrum, remapping multi-component signals into single-component clusters. Bearing fault diagnosis is achieved by matching the relative order of each cluster with the bearing fault characteristic coefficients. Simulation signals validate the superiority of the feature extraction method, and experimental signals validate the feasibility of the bearing fault diagnosis method.
Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of selfrectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.
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