The tremendous acoustic impedance difference between the piezoelectric composite and air prevents the ultrasonic transition, resulting in low amplitude for the received signal for the composite defect detection using an air-coupled transducer. The matching system, which includes the matching layers and bonding layers attached to the piezoelectric composite, can reduce the acoustic impedance difference and benefit the acoustic transition. In this paper, the fabrication method and modeling for the matching layers are proposed to optimize the transducer performance. The effects of bonding layer material on the transducer performance are also discussed. Experiments were conducted for modeling validation. The proposed model can predict the matching layer acoustic properties with an error of less than 11%. The bonding layer using the same material as the first matching layer can help to increase the sensitivity by about 33% compared to the traditional epoxy bonding. The optimized air-coupled ultrasonic transducer, based on the results of this study, has a 1283 mV amplitude in the air, which is 56% higher than commercially available transducers, and can identify the defects in two typical non-metallic composite materials easily.

The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.

In high-temperature environments, the signal-to-noise ratio (SNR) of the signal measured by electromagnetic acoustic transducers (EMAT) is low, and the signal characteristics are difficult to extract, which greatly affects their application in practical industry. Aiming at this problem, this paper proposes the least mean square adaptive filtering interpolation denoising method based on variational modal decomposition (AFIV). Firstly, the high-temperature EMAT signal was decomposed by variational modal decomposition (VMD). Then the high-frequency and low-frequency noises in the signal were filtered according to the excitation center frequency. Following the wavelet threshold denoising (WTD) for the noise component after VMD decomposition was carried out. Afterward, the noise component and signal component were connected by an adaptive filtering process to achieve further noise reduction. Finally, cubic spline interpolation was used to smooth the noise reduction curve and obtain the time information. To verify the effectiveness of the proposed method, it was applied to two kinds of ultrasonic signals from 25 to 700 °C. Compared with VMD, WTD, and empirical mode decomposition denoising, the SNR was increased by 2 times. The results show that this method can better extract the effective information of echo signals and realize the online thickness measurement at high temperature.

Background: Oscillation is a special cell behavior in microorganisms during continuous fermentation, which poses threats to the output stability for industrial productions of biofuels and biochemicals. In previous study, a spontaneous oscillatory behavior was observed inClostridium butyricum-intensive microbial consortium in continuous fermentation for 1,3-propanediol (1,3-PDO) production from glycerol, which led to the discovery of oscillation in speciesC. butyricum.Results: Spontaneous oscillations byC. butyricumtended to occur under glycerol-limited conditions at low dilution rates. At a glycerol feed concentration of 88 g/L and a dilution rate of 0.048 h⁻¹, the oscillatory behavior ofC. butyricumwas observed after continuous operation for 146 h and was sustained for over 450 h with an average oscillation period of 51 h. During oscillations, microbial glycerol metabolism exhibited dramatic periodic changes, in which productions of lactate, formate and hydrogen significantly lagged behind that of other products including biomass, 1,3-PDO and butyrate. Analysis of extracellular oxidation–reduction potential and intracellular ratio of NAD⁺/NADH indicated that microbial cells experienced distinct redox changes during oscillations, from oxidized to reduced state with decreasing of growth rate. Meanwhile,C. butyricumS3 exhibited periodic morphological changes during oscillations, with aggregates, elongated shape, spores or cell debris at the trough of biomass production. Transcriptome analysis indicated that expression levels of multiple genes were up-regulated when microbial cells were undergoing stress, including that for pyruvate metabolism, conversion of acetyl-CoA to acetaldehyde as well as stress response.Conclusion: This study for the first time systematically investigated the oscillatory behavior ofC. butyricumin aspect of occurrence condition, metabolism, morphology and transcriptome. Based on the experimental results, two hypotheses were put forward to explain the oscillatory behavior: disorder of pyruvate metabolism, and excessive accumulation of acetaldehyde.

Low excitation voltage (LEV) electromagnetic ultrasonic detection is needed for the safety of high-temperature boilers, reactors, pipelines, and other equipment. However, the low signal intensity and high noise due to the LEV and the high-temperature environment result in a low signal-to-noise ratio (SNR) in the defect detection process. This study proposes a novel noise reduction method, called VMD-SVD-CEEMD (VSCE), which utilizes variational mode decomposition (VMD), singular value decomposition (SVD), and complementary ensemble empirical mode decomposition (CEEMD) to extract defect information and suppress pulsating noise, instrument noise, and high-temperature structural noise. The shear wave generated by an electromagnetic acoustic transducer was used to detect two different materials at 25V LEV, with a temperature range of 25°C to 700°C. The VSCE method was found to improve the SNR of received signals by 2-3 times compared to conventional denoising methods such as wavelet threshold denoising (WTD), VMD, and empirical mode decomposition (EMD). The positioning error of the defect location is less than 2.06%, which has proven the great application prospects and value of the VSCE methods in the LEV detection of high-temperature equipment.