A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM.

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Refined composite moving average fluctuation dispersion entropy and its application on rolling bearing fault diagnosis.

Vibration signal analysis based on multiscale entropy is one of the important means to realize rotating machinery fault diagnosis. However, the length of the time series will be shortened during the coarse-graining process with the increase of the scale factor, which makes the calculated entropy values unstable. This inherent drawback of the coarse-graining method limits its application in fault feature extraction. This paper presents a novel feature extraction method for vibration signals called refined composite moving average fluctuation dispersion entropy (RCMAFDE). It is verified by simulation experiments that RCMAFDE has high stability of entropy values under different time series lengths as well as different disturbances. The RCMAFDE was applied to the fault diagnosis of rolling bearings, and a new fault diagnosis method of rolling bearings was proposed by combining RCMAFDE and kernel extreme learning machine (KELM) optimized by the chaos sparrow search optimization algorithm (CSSOA). First, the vibration signal is preprocessed to form a sample set, and then, the fault feature vector is calculated by RCMAFDE. Finally, the feature vector set is input into the CSSOA-KELM model for training and testing, and the fault diagnosis result is output. To demonstrate the effectiveness and feasibility of the fault diagnosis method, two publicly available datasets and a self-collected dataset are used for experimental validation. The experimental results show that the proposed fault diagnosis method can extract the nonlinear dynamic complexity information of vibration signals more effectively compared with the comparison methods and obtain the highest fault identification accuracy under different datasets.

Development of novel fermented stinky sea bass and analysis of its taste active compounds, flavor compounds, and quality.

This paper developed novel fermented stinky sea bass (FSSB) products and reports the first analysis of its taste active compounds, flavor compounds, and quality. The FSSB with Xian Hen stinky tofu (F-XH) had the best sensory quality. After fermentation, the texture of FSSB improved, and the umami amino and sweet amino acid contents significantly increased, whereas that of the bitter amino acids decreased. Moreover, the IMP content and EUC in FSSB increased significantly. Of the six key volatile flavor compounds distinguished, the key volatile flavor compounds of F-XH are Ethyl Acetate, Propan-2-ol, alpha-pinene, 2-methylbutanal, acetol, 4-Methylpentan-2-one. Ethyl Acetate and 2-propanol were thought to give F-XH its unique wine flavor after cooking. The quality evaluation results demonstrated that the six FSSB complied with the Chinese Standard (GB10136-2015) (2015) animal aquatic products. Six types of FSSB products with unique flavors were developed, and a reference was provided for their industrial application.

Nontargeted metabolomics reveals differences in the metabolite profiling among methicillin-resistant and methicillin-susceptible Staphylococcus aureus in response to antibiotics.

Staphylococcus aureus (S. aureus) causes infections and can be fatal. In the long-term struggle against antibiotics, S. aureus has acquired resistance toward antibiotics and become more difficult to kill. Metabolomics could directly reflect the responses of S. aureus toward antibiotics, which is effective for studying the resistance mechanism of S. aureus. In this study, based on a nontargeted metabolic figure printing technique, the metabolome of a pair of isogenic methicillin-susceptible and resistant S. aureus strains ATCC25923 (MSSA) and ATCC43300 (MRSA) treated with or without oxacillin was characterized. 7 and 29 significantly changed metabolites in MRSA and MSSA were identified by combined accurate mass and mass fragmentation analysis. Pathway enrichment analysis suggested that DNA repair and flavin biosynthesis are the universal pathways of both MSSA and MRSA under antibiotic stress. MRSA systematically and effectively fights against oxacillin through precise control of energy production, PBP2a substrate biosynthesis and antioxidant function. In contrast, MSSA lacks effective defense pathways against oxacillin. The different metabolome responses of MSSA and MRSA toward antibiotics provide us with new insights into how S. aureus develops antibiotic resistance.

Cu-based metal-organic framework HKUST-1 as effective catalyst for highly sensitive determination of ascorbic acid.

In this work, a Cu-based nanosheet metal-organic framework (MOF), HKUST-1, was synthesised using a solvent method at room temperature. Its morphology, structure and composition were characterised by scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), powder X-ray diffraction (XRD), Fourier transform infrared (FTIR), Raman spectroscopy, nitrogen adsorption and desorption isotherms, energy dispersive X-ray spectroscopy (EDS) and elemental analysis (EA). This material was then loaded onto the surface of an indium tin oxide (ITO) electrode to catalyse the electrochemical oxidation of ascorbic acid (AA). An equal-electron-equal-proton reaction was deduced from the pH investigation, and a diffusion-controlled process was reinforced by the dynamics study. Under optimal conditions, the oxidation peak current at +0.02 V displayed a linear relationship with the concentration of AA within the ranges of 0.01-25 and 25-265 mM, respectively. The limit of detection (LOD) was 3 µM at S/N of 3. The superb response could be ascribed to the porous nanosheet structure of HKUST-1, which enhanced both the effective surface area and the electron transfer ability significantly. Moreover, the novel AA sensor demonstrated good reproducibility, favourable stability and high sensitivity towards glucose, uric acid (UA), dopamine (DA) and several amino acids. It was also successfully applied to the real sample testing of various AA-containing tablets.