Modal Parameters Identification Method Based on Symplectic Geometry Model Decomposition

This paper proposes a novel method of structural system modal identification, where the iterative method is introduced in symplectic geometric model decomposition (SGMD). The proposed method can decompose the measured response into finite symplectic geometric components and identify the modal parameters of time-invariant structures and the instantaneous frequency of time-variant system through each symplectic geometric component. To obtain the shape information of the structural model, the SGCs of the same frequency at different measuring points are subjected to singular value decomposition (SVD). Both simulated data verification and measured data verification were used to verify if the method proposed in this article is effective for time-invariant system and time-variant system identification. For the simulated data, we study on a structural system which is set up with time-variant stiffness and time-invariant system. The measured vibration data of beam structure and time-variant wheel-rail coupling system were also tested and varied. Compared with the results of empirical model decomposition, the proposed method is capable of identifying instantaneous frequencies with better accuracy

Acoustics Source Identification of Diesel Engines Based on Variational Mode Decomposition, Fast Independent Component Analysis, and Hilbert Transformation

Diesel engines are widely used in railway systems, particularly in freight trains. Despite their high efficiency in energy conversion, they usually generate high levels of acoustics pollution during operation. In order to mitigate this problem, a series of active/passive acoustics control methods are used to reduce noise. Most of these methods are only effective if the prior knowledge of sources is given. In other words, it is essential to recognize the acoustics source. Variational mode decomposition (VMD) is a signal processing method that enhances the signal corrupted by background noise. However, the decomposed results of VMD depend on their mode parameter and penalty parameter. Therefore, an evaluation method based on system modal parameters (natural frequency and damping ratio) is proposed to select the mode parameter, and the penalty parameter can be selected from the power spectra of signals. In order to increase the accuracy of decomposition for diesel engines and find out the sources of acoustics, a method combining VMD, fast independent component analysis, and Hilbert transformation (VMD-FastICA-HT) is proposed for the separation and identification of different sources for diesel engines. The optimization results indicate that when the penalty parameter value is 1.5 to 16 times the maximum signal amplitude, better decomposition results can be achieved. Therefore, the separated independent acoustics are more accurate in source identification. Furthermore, both simulation data and in situ operational data of diesel engines for vehicles are used to validate the effectiveness of the proposed method

The Rail Surface Defects Recognition via Operating Service Rail Vehicle Vibrations

Rail surface defects will not only bring wheel rail noise during train operation, but also cause corresponding accidents. Most of the existing detection methods are manual detection, which is time-consuming, laborious, inefficient, and subjective. With the development of technology, automatic detection replaces manual detection, which reduces manual labor, improves efficiency, and objectively evaluates the surface state of rails, which is in line with the purpose of modern intelligent production. The automatic detection of a single sensor is usually not enough to complete the recognition, but multiple sensors need to be additionally installed and refitted on the service vehicle, which creates difficulty for on-site test conditions. Therefore, in order to overcome these shortages and to adapt to the actual vibration characteristics of service vehicles, a rail surface defect recognition method based on optimized VMD gray image coding and DCNN is proposed in this paper. Firstly, the optimization method of VMD mode number based on the maximum envelope kurtosis is proposed. The VMD after parameter optimization is used to decompose the four-channel axle box vibration signal, and the component with the largest correlation coefficient between each order eigenmode component and the original signal is extracted. Secondly, the filtered IMF components are arranged in sequence and encoded into grayscale images. Finally, the DCNN structure is designed, and the training set is input into the network for training, and the test set verifies the effectiveness of the network and realizes the recognition of rail surface defects. The test accuracy of railway data set measured on the serviced vehicle is 99.75%, and the results show that this method can accurately identify the category of rail surface defects. After adding Gaussian noise to the original signal, the test accuracy reaches 99.20%, which proves that the method has good generalization ability and anti-noise performance. Additionally, this method can ensure the safe operation of vehicles without adding new equipment, which reduces operation costs and improves the intelligent operation and maintenance of rails

Characteristics, Cryoprotection Evaluation and In Vitro Release of BSA-Loaded Chitosan Nanoparticles

Chitosan nanoparticles (CS-NPs) are under increasing investigation for the delivery of therapeutic proteins, such as vaccines, interferons, and biologics. A large number of studies have been taken on the characteristics of CS-NPs, and very few of these studies have focused on the microstructure of protein-loaded NPs. In this study, we prepared the CS-NPs by an ionic gelation method, and bovine serum albumin (BSA) was used as a model protein. Dynamic high pressure microfluidization (DHPM) was utilized to post-treat the nanoparticles so as to improve the uniformity, repeatability and controllability. The BSA-loaded NPs were then characterized for particle size, Zeta potential, morphology, encapsulation efficiency (EE), loading capacity (LC), and subsequent release kinetics. To improve the long-term stability of NPs, trehalose, glucose, sucrose, and mannitol were selected respectively to investigate the performance as a cryoprotectant. Furthermore, trehalose was used to obtain re-dispersible lyophilized NPs that can significantly reduce the dosage of cryoprotectants. Multiple spectroscopic techniques were used to characterize BSA-loaded NPs, in order to explain the release process of the NPs in vitro. The experimental results indicated that CS and Tripolyphosphate pentasodium (TPP) spontaneously formed the basic skeleton of the NPs through electrostatic interactions. BSA was incorporated in the basic skeleton, adsorbed on the surface of the NPs (some of which were inlaid on the NPs), without any change in structure and function. The release profiles of the NPs showed high consistency with the multispectral results