Bearings, a crucial element in rotating machinery, are most susceptible to failure during operation, making up over half of all malfunctions. Detecting bearing faults in a timely manner can be quite challenging due to the frequent influence of strong noise on the fault features. To address the issue, an adaptive parameter optimization based on Minimum Entropy Deconvolution (MED) method in conjunction with Infogram is proposed. First, the Infogram is created through spectral negentropy, followed by the construction of a filter that utilizes the center frequency and the frequency band exhibiting the highest spectral negentropy. This filter is then applied to the original vibration signal in order to isolate the frequency band that contains the most significant bearing fault information. Then, based on MED method, the autocorrelation function and L1 norm are introduced to evaluate the filtered signal to realize the adaptive search of deconvolution filter length. Finally, the characteristic frequency components are extracted by envelope spectrum analysis. The validation of the effectiveness of the proposed method is conducted on a public dataset across various bearing operating conditions. The verification results show that this method can accurately detect the conditions of bearings, and extract more distinctive characteristic frequency components compared with some relevant techniques.
Most DNA methylation occurs in the cytosine of nucleosides. In this study, the absorption coefficients of cytosine and 5-methylcytosine were measured at 0.1-2.0 THz through terahertz time domain spectroscopy (THz-TDS) without being labeled. Our results showed that these important biomolecules had distinct spectral characteristics within this terahertz band. Moreover, structural optimization and lattice dynamics calculations were performed to determine the lattice vibrational motions of both cytosine crystals and 5-methylcytosine crystals using the density functional theory (DFT) and the properly modeled intermolecular potentials. Typically, the lattice vibrational motion simulated the absorption spectrum which indicated that the terahertz technique was able to detect the absorption spectral features of cytosine and 5-methylcytosine. Hopefully, this study provided the molecular background to detect DNA methylation and lay certain foundation for subsequent cancer detection.
The terahertz time-domain spectra of sildenafil, metformin hydrochloride and phenolphthalein samples were obtained by terahertz time-domain spectroscopy. The refractive index spectra and absorption spectra of these additives were obtained according to the formula. The experimental results show that these three health supplements have obvious characteristic absorption peaks in the terahertz band. The illegal additives were classified based on support vector machine (SVM). The experimental results show that the established SVM model can accurately identify the check set and identify the additive within a certain range. The feasibility of terahertz time-domain spectroscopy for food ingredient detection was verified, which provided a new experimental method for the detection of health product additives.
Terahertz time-domain spectroscopy (THz-TDS) has been applied to medical applications as THz waves are sensitive to biomolecules. In this paper, terahertz time-domain attenuated total reflection spectroscopy (THz-ATR) was used to identify human colon cancer cell line. The refractive indices of cancerous and normal cell lines with six different concentrations were analyzed in the frequency domain from 0.1-1.2THz. t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Components Analysis (PCA) methods were adopted to extract the key features from the THz spectrum. Experimental results showed that the features extracted by t-SNE had better recognition accuracy owing to enhanced clustering efficacy. This work may be helpful for detecting human colon cancer cells by using terahertz techniques.
Recovering component spectra from terahertz measurements of unknown mixtures has been studied in this paper using nonnegative matrix factorization (NMF). NMF mathematically decomposes the spectra data into two nonnegative matrixes which describe the component spectra and the corresponding fractional abundance. Two basic algorithms in the class of this method, NMF and NMF with smoothness constraint (cNMF), were adopted to resolve the terahertz absorption spectra matrix obtained from a ternary mixture with varying compositions of Nitrofurantoin, L-Leucine and D-Tyrosine. The quality of the decomposition results was evaluated. The performance of the two algorithms on extracting component terahertz spectra was compared. The optimal result reached by cNMF in this study implies the capability of the NMF method for blind terahertz spectral unmixing. The attempt made in our work helps to further investigate unknown mixtures by terahertz spectroscopy.
Terahertz (THz) spectroscopy has fingerprint features for many bio-molecules with frequency between infrared and microwave covering the vibrational models of a great number of materials. In this study, THz-TDS was used to detect the preserved and bad meat. And the absorption coefficient indices of bad meat and preserved meat were measured in the range of 0.2–1.0 THz. The result shows that there are differences of pork tissue in both time domain and absorption coefficient in the process of deterioration. Then differences between preserved and bad meat were also presented. In order to investigate the relationship between the terahertz characteristics and meat quality, the changes of water content and material in the samples were also discussed. This work supplies reference for the application of THz technology in meat quality detection.
Amino acids are important nutrient substances for life, and many of them have several isomerides, while only L-type amino acids can be absorbed by body as nutrients. So it is certain worth to accurately classify and identify amino acids. In this paper, terahertz time-domain spectroscopy (THz-TDS) was used to detect isomers of various amino acids to obtain their absorption spectra, and their spectral characteristics were analyzed and compared. Results show that not all isomerides of amino acids have unique spectral characteristics, causing the difficulty of classification and identification. To solve this problem, partial least squares discriminant analysis (PLS-DA), firstly, was performed on extracting principal component of THz spectroscopy and classifying amino acids. Moreover, variable selection (VS) was employed to optimize spectral interval of feature extraction to improve analysis effect. As a result, the optimal classification model was determined and most samples can be accurately classified. Secondly, for each class of amino acids, PLS-DA combined with VS was also applied to identify isomerides. This work provides a suggestion for material classification and identification with THz spectroscopy.
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