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The effect of nanosized bamboo dietary fiber (NBDF) on whey syneresis in yogurt during shelf storage was studied. The milk wettability of micro- and nano-modified NBDF with different particle sizes was analyzed. The inhibitory mechanism of the modified NBDF exhibiting the best milk wettability on whey syneresis during the shelf storage of yogurt was investigated. The results showed that the morphology of NBDF changed from blocky to filamentous after sequential ultrasound, pressurized heating and enzymatic hydrolysis. The microfibrils were fractured after high-energy mechanical ball milling, forming nanoclusters. The milk wettability of modified NBDF initially increased and then decreased with the decrease in particle size. The microfibrils with a particle size of 10–30 μm had the strongest milk wettability. Addition of the microfibrils at a mass concentration of 9 g/L improved the oscillation stability of set yogurt. After 28 days of shelf storage, the whey separation rate was 5.84%, which was only 57.87% of that of the control group. This may be related to the fact that the microfibrils effectively increased the proportion of bound water in yogurt (by 41.4%), improved the casein network structure, and increased the coatability, contributing to the formation of tiny and uniform whey pore channels in yogurt.

This work aims to study the regulatory mechanism of calcium fortification on the texture of kelp pickle during the shelf-life period in order to provide theoretical support for improving its texture quality using exogenous calcium. The effects of fermentation acidity and sodium/calcium ratio on the texture of kelp pickle during its shelf life at 4 ℃ were studied, and the evolution of water distribution and the structure of calcium ion bridge was analyzed. The results showed the hardness of kelp pickle decreased with increasing fermentation acidity, but its changes were effectively slowed down by exogenous calcium lactate. The rate of hardness preservation of kelp pickle with a sodium/calcium of 1.5:1 increased by 92.8% compared with the control group. The results of scanning electron microscopy (SEM) and low-frequency nuclear magnetic resonance (NMR) spectroscopy showed calcium lactate alleviated the morphological damage of kelp tissues caused by acid produced during fermentation, and inhibited the transition of bound water to free water during the shelf-life period. The proportion of free water in kelp pickle with a sodium/calcium ratio of 2:1 increased by only 10.29 percentage points after six months of storage. The results of X-ray photoelectron spectroscopy (XPS) showed that the mechanism by which calcium lactate enhanced the texture of kelp pickle during its shelf-life might be related to the formation of a stable calcium ion bridge at a sodium/calcium ratio of 2:1 or 1.5:1.

Blind deconvolution (BD) has proven to be an effective approach to detecting repetitive transients caused by bearing faults. However, BD suffers from instability issues including excessive sensitivity of kurtosis-guided BD methods to the single impulse and high computational time cost of the eigenvector algorithm-aided BD methods. To address these critical issues, this paper proposed a novel BD method maximizing negative entropy (NE), shortened as maximum negative entropy deconvolution (MNED). MNED utilizes NE instead of kurtosis as the optimization metric and optimizes the filter coefficients through the objective function method. The effectiveness of MNED in enhancing repetitive transients is illustrated through a simulation case and two experimental cases. A quantitative comparison with three existing BD methods demonstrates the advantages of MNED in fault detection and computational efficiency. In addition, the performance of the four methods under different filter lengths and external shocks is compared. MNED exhibits lower sensitivity to random impulse noise than the kurtosis-guided BD methods and higher computational efficiency than the BD methods based on the eigenvalue algorithm. The simulation and experimental results demonstrate that MNED is a robust and cost-effective method for bearing fault diagnosis and condition monitoring. [ABSTRACT FROM AUTHOR]

Considering the shortcomings of the maximum correlated kurtosis deconvolution（MCKD） method that cannot automatically identify the period of bearing fault impulses,exists the resampling process and the multiple input parameters,an adaptive maximum correlated kurtosis deconvolution（AMCKD） method is proposed.The periodic modulation intensity（PMI） of envelope signal is used to identify the period of bearing fault impulses adaptively.Moreover,the period is constantly updated during searching for the optimal deconvolution filter iteratively,so that the real fault period is gradually approximated.Finally,the filtered signal with the largest correlated kurtosis is selected as the optimal deconvolution signal.Compared with MCKD method,AMCKD method can identify fault impulse period adaptively,avoid signal resampling process,and reduce the input parameters of the algorithm.Simulated and experimental results verify the effectiveness of this method in early fault feature extraction of rolling bearings,and the comparison with fast kurtogram method shows the superiority of AMCKD method in enhancing periodic impulse characteristics.

A sparse squared envelope is crucial for efficient and accurate diagnosis of bearing faults. Blind deconvolution (BD) is a well-established sparse feature enhancement method for the diagnostics of rolling bearings. Traditional BD methods, such as minimum entropy deconvolution (MED), are susceptible to random transients, making it difficult to enhance fault features of rolling bearings subject to strong random shocks. Deconvolution methods that take the fault characteristic frequency (or fault impulse period) of interest as an algorithm input parameter, such as maximum second-order cyclostationarity blind deconvolution (CYCBD), can alleviate this deficiency. However, bearing fault features are difficult to be enhanced by these methods when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index (MSI) of the squared envelope is proposed as the objective function of the deconvolution method, and a new BD method is developed to achieve a sparse squared envelope for fault diagnosis of rolling bearings. Furthermore, the methodology is extended to the frequency domain, and another new BD method that utilizes the MSI of the squared envelope spectrum as the objective function is established to achieve a sparse squared envelope spectrum for bearing diagnostics. These two proposed BD methods are robust to random transients and do not require characteristic frequency or impulse period as an input parameter for feature enhancement. The performance of the two proposed BD methods is verified on experimental datasets from two different railway axle bearing test rigs and compared with the state-of-the-art deconvolution methods. The results show that the two proposed methods can effectively enhance repetitive transient features in noisy vibration signals and accurately diagnose different faults of railway axle bearings. [ABSTRACT FROM AUTHOR]

The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields of machine condition monitoring and fault diagnosis, and they can be reformulated as the ratio of different quasi-arithmetic means (RQAM). Under this framework, generalized Gini indices (GGIs) have been developed for sparse quantification by applying nonlinear weights to GI, and another generalized form of GI, referred to here as power function-based Gini indices I (PFGI1s), has been introduced by using power function as the generator of quasi-arithmetic means. The GGIs with different weight parameters exhibit reliable sparse quantization capability for repetitive transient features, while their repetitive transient discriminability is lower than kurtosis and negentropy under noise contamination. PFGI1 achieves enhanced repetitive transient discriminability with increasing power exponent, showing the advantage of the generalization approach. In this paper, based on RQAM, a single-parameter generalization method for generating PFGI1s is introduced into GI II and GI III from the perspective of the quasi-arithmetic mean generator, which leads to the power function-based Gini indices II and III (PFGI2s and PFGI3s) constructed from GI II and GI III, respectively. Mathematical derivation proves that PFGI2s and PFGI3s satisfy at least five of six typical attributes of sparsity measures and are two new families of sparsity measures. Simulation analysis shows that, similar to PFGI1s, PFGI2s and PFGI3s can monotonically estimate the sparsity of the data sequence and can simultaneously achieve strong random transient resistibility and high repetitive transient discriminability compared with traditional sparsity measures. The experimental results of bearing run-to-failure demonstrate that PFGI1s, PFGI2s, and PFGI3s with appropriate power exponents can effectively quantify the repetitive transient features caused by bearing faults and can accurately characterize the bearing degradation status compared with the state-of-the-art sparsity measures. [ABSTRACT FROM AUTHOR]