Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.     

Double Gaussian potential stochastic resonance method and its application in centrifugal fan blade crack detection

Stochastic resonance (SR) has been extensively utilized in the field of weak fault signal detection for its characteristic of enhancing weak signals by transferring the noise energy. Aiming at solving the output saturation problem of the classical bistable stochastic resonance (CBSR) system, a double Gaussian potential stochastic resonance (DGSR) system is proposed. Moreover, the output signal-to-noise ratio (SNR) of the DGSR method is derived based on the adiabatic approximation theory to analyze the effect of system parameters on the DGSR method. At the same time, for the purpose of overcoming the drawback that the traditional SNR index needs to know the fault characteristic frequency (FCF), the weighted local signal-to-noise ratio (WLSNR) index is constructed. The DGSR with WLSNR can obtain optimal parameters adaptively, thereby establishing the DGSR system. Ultimately, a DGSR method is proposed and applied in centrifugal fan blade crack detection. Through simulations and experiments, the effectiveness and superiority of the DGSR method are verified.     

Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms.     

Application of ultrasound-assisted physical mixing treatment improves in vitro protein digestibility of rapeseed napin

The in vitro protein digestibility (IVPD) of napin was studied using different pretreatment methods, including ultrasound, mixing napin with lactalbumin, and ultrasound-assisted protein mixing. The relationships between IVPD, molecular structure, and disulfide bonds were explored, showing that the IVPD of napin was the highest compared with the control when treated with 40% ultrasound power. When the proportion of napin to lactalbumin was 5:5, a synergistic influence between the two proteins was observed. Further investigation showed that the IVPD of napin was clearly improved by treatment with ultrasound-assisted protein mixing. Compared with the single protein in the control, the β-sheet content in the secondary structure of the mixed protein after sonication was reduced from 45.02% to 37.16%. The ordered protein structure was also disrupted by ultrasound, as supported by fluorescence intensity and surface hydrophobicity analyses. The decreased number of disulfide bonds and conformational changes indicated that the IVPD of rapeseed napin was closely related to the disulfide bond content. This study provides a theoretical basis for improving protein digestibility by combining ultrasound with physical mixing.     

A survey on HHL algorithm: From theory to application in quantum machine learning

The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.     

Research on detection method of multi-frequency weak signal based on stochastic resonance and chaos characteristics of Duffing system

Weak signal detection has been widely used in many fields such as military and national economy. Aiming at the problem that the traditional stochastic resonance (SR) method can’t obtain the signal amplitude when detecting weak signals, the frequency and amplitude of the weak signal are obtained by combining the SR and chaos characteristics of the two-dimensional Duffing system. Firstly, the effects of two-dimensional Duffing system parameters a, b, k, noise intensity D on the Kramers rate and signal-to-noise ratio (SNR) are analyzed under the Gaussian white noise environment. The results show that the damping ratio K can hinder the SR effect of the system to some extent. Secondly, to solve the misjudgment of the state method of the weak signal amplitude in the detection, the Lyapunov exponent is used to assure the threshold's range, and the threshold of the chaotic critical state is found. Finally, the paper gives the processes of frequency and amplitude detection of multiple high-frequency signals, which realizes the effective detection of the frequency and amplitude of multiple high-frequency signals in a Gaussian white noise environment, and successfully applies the method to the accurate detection of boundary voltage amplitude in electrical impedance tomography.