Simulation and Experimental Investigation on the AE Tomography to Improve AE Source Location in the Concrete Structure

Acoustic emission (AE) tomography, which is based on the time-travel tomography with AE events as its signal sources, is a new visualization tool for inspecting and locating the internal damages in the structures. In this paper, AE tomography is applied to examine a man-made damage in a typical heterogeneous concrete structure to validate its effectiveness. Firstly, the finite element (ABAQUS/Explicit) simulation model of the concrete structure with one damaged circle in its center is built, and the simulated AE signals are obtained to establish the AE tomography. The results show that the damaged circle in the created model can be visualized clearly with the AE tomography in its original location. Secondly, the concrete specimen based on the FE model is fabricated, and the pencil lead break (PLB) signal is taken as the exciting source for AE tomography. It is shown that the experimental results have good consistency with the FE simulation results, which also verifies the feasibility of the finite element model for AE tomography. Finally, the damage source location based on AE tomography is compared with the traditional time of arrival (TOA) location method, and the better location accuracy is obtained with the AE tomography. The research results indicate that AE tomography has great potential in the application of structure damage detection.Foundation of China (no. 51175079, no. 51305176) as well as the Fundamental Research Funds for the Central Universities (CXLX12 0079)

Research of Feature Extraction Method Based on Sparse Reconstruction and Multiscale Dispersion Entropy

As one of the most important components in rotating machinery, it’s necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. Therefore, in this paper a new feature extraction method combining sparse reconstruction and Multiscale Dispersion Entropy (MDErms) is proposed. Firstly, the Sliding Matrix Sequences (SMS) truncation and sparse reconstruction by Hankel-matrix are applied to the vibration signal. Then MDErms is utilized as a characteristic index of vibration signal, which is suitable for a short time series. Additionally, the MDErms is employed in the sparse reconstructed matrix sequences to achieve the Multiscale Fusion Entropy Value Sequence (MFEVS). The MFEVS keeps the fault potential feature information in different scales and is superior in distinguishing fault periodic impulses from heavy background noise. Finally, the designed FIR bandpass filter based on the MFEVS, shows prominent features in denoising and detecting weak bearing faults, which is separately verified by simulation studies and artificial fault experiments in different cases. By comparison with traditional methods like EEMD, Wavelet Packet (WP), and fast kurtogram, it can be concluded that the proposed method has a remarkable ability in removing noise and detecting rolling bearing faint fault

Mitogenome of a cryptic species within <i>Uropsilus</i> and divergence time estimation

<p><i>Uropsilus</i> sp. 4 is a new cryptic species, collected in Changyang county, Hubei province, China. In this study, the whole mitochondrial genome of <i>Uropsilus</i> sp. 4 was first determined and characterized. The genome is 16,542 bp in length, containing 13 protein coding genes, 22 transfer RNA genes, two ribosomal RNA genes, and a putative control region. Base on NJ, ML, and BI methods, we obtained the same topologies. <i>U.</i> sp. 4 clustered with <i>U. gracilis</i> and the divergence time was 1.78 Ma (95% CI 1.24–2.32 Ma), in concordance with the third period of last orogenic push of the Qinghai-Tibetan Plateau, might contribute to the speciation of <i>U</i>. sp. 4.</p

A Novel Approach to Develop Lager Yeast with Higher NADH Availability to Improve the Flavor Stability of Industrial Beer

Flavor stability is important for beer quality and extensive efforts have been undertaken to improve this. In our previous work, we proved a concept whereby metabolic engineering lager yeast with increased cellular nicotinamide adenine dinucleotide hydride (NADH) availability could enhance the flavor stability of beer. However, the method for breeding non-genetically modified strains with higher NADH levels remains unsolved. In the current study, we reported a novel approach to develop such strains based on atmospheric and room temperature plasma (ARTP) mutagenesis coupled with 2,4-dinitrophenol (DNP) selection. As a result, we obtained a serial of strains with higher NADH levels as well as improved flavor stability. For screening an optimal strain with industrial application potential, we examined the other fermentation characteristics of the mutants and ultimately obtained the optimal strain, YDR-63. The overall fermentation performance of the strain YDR-63 in pilot-scale fermentation was similar to that of the parental strain YJ-002, but the acetaldehyde production was decreased by 53.7% and the resistance staling value of beer was improved by 99.8%. The forced beer aging assay further demonstrated that the favor stability was indeed improved as the contents of 5-hydroxymethylfurfural in YDR-63 was less than that in YJ-002 and the sensory notes of staling was weaker in YDR-63. We also employed this novel approach to another industrial strain, M14, and succeeded in improving its flavor stability. All the findings demonstrated the efficiency and versatility of this new approach in developing strains with improved flavor stability for the beer industry

Total process of fault diagnosis for wind turbine gearbox, from the perspective of combination with feature extraction and machine learning: A review

With the increasing of the installed capacity of wind power, the condition monitoring and maintains technique is becoming more important. Wind Turbines (WT) gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation. Towards this, a number of scholars have pay attention to the fault diagnosis of WT gearbox. The efficiency of Machine Learning (ML) algorithms is highly correlated with signal type, data quality, and extracted features employed. The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms. More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms, while providing limited insights into the features of the acquired data. Therefore, it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms, and provide a detailed direction for future WT gearbox fault diagnosis technology research. In this paper, data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed. For the using of ML method in WT gearbox fault diagnosis, the data prepared for training is very important. The paper firstly reviewed the data analysing method which will support the ML method. The data analysing methods include data acquisition, data preprocessing and feature extraction method. Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis, as it serves as the essence of effective detection. This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis. Then typical ML method for WT gearbox fault diagnosis are carefully reviewed. Moreover, some prospects for future research directions are discussed in the end

Fed-Batch Fermentation of Saccharomyces pastorianus with High Ribonucleic Acid Yield

(1) Background: The degradation products of ribonucleic acid (RNA)are widely used in the food and pharmaceutical industry for their flavoring and nutritional enhancement functions. Yeast is the main source for commercial RNA production, and an efficient strain is the key to reducing production costs; (2) Methods: A mutant Saccharomyces pastorianus G03H8 with a high RNA yield was developed via ARTP mutagenesis and fed-batch fermentation was applied to optimize production capacity. Genome sequencing analysis was used to reveal the underlying mechanism of higher RNA production genetic differences in the preferred mutant; (3) Results: Compared with the highest RNA content of the mutant strain, G03H8 increased by 40% compared with the parental strain G03 after response surface model optimization. Meanwhile, in fed-batch fermentation, G03H8&prime;s dry cell weight (DCW) reached 60.58 g/L in 5 L fermenter by molasses flowing and RNA production reached up to 3.58 g/L. Genome sequencing showed that the ribosome biogenesis, yeast meiosis, RNA transport, and longevity regulating pathway were closely related to the metabolism of high RNA production; (4) Conclusion: S. pastorianus G03H8 was developed for RNA production and had the potential to greatly reduce the cost of RNA production and shorten the fermentation cycle. This work lays the foundation for efficient RNA content using S. pastorianus