No abstract

In addition to providing basic protective functions, modern military uniforms are also being designed to provide special functions, such as infrared shielding. In this study, a nanoscale copper film was deposited on Kevlar para-1414 aramid fabric by magnetron sputtering technology to significantly enhance infrared shielding. The deposition of a uniform nano-copper film on the surface of the aramid fabric enhanced infrared shielding, tensile strain, and conductivity, which is of guiding significance for the development of infrared shielding garments.

Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments. extract the characteristic signal. Cyclostationary signal analysis [21,22] relies on the cyclostationary characteristics of the bearing fault impulse signal to design a filter to eliminate random noise. A Wiener filter [23,24] is used to eliminate the stationary random noise in the fault impulse signal. A wavelet transform [25,26] first decomposes the vibration signal into different frequency bands, then defines the threshold to eliminate the noise components and reconstructs the characteristic signal. A Kalman filter [27,28] first establishes the vibration state model and signal observation model of rolling bearing, then iteratively eliminates the noise in the signal. Stochastic resonance [29,30] uses noise to enhance the fault characteristic component of the bearing vibration signal. All of the above mentioned methods are universal, can effectively eliminate background noise and interference components, and extract fault signals in specific environment, but they are not suitable for complex interference situations, and especially when the interference components in vibration signals are similar to fault signals, it is difficult to distinguish them by the above method, let alone eliminating interference components and extract fault signals. Blind source separation (BSS) technology can realize the separation of multiple aliased signals [1,31]. Meanwhile, blind source separation is not affected by the time and frequency overlap of source signals, and the separated output signal will not lose the weak feature information in the source signal.So far, many effective and distinctive blind source separation algorithms have been constructed. Typical algorithms include fast fixed-point [32] algorithms, natural gradient [28] algorithms, second-order blind identification (SOBI) [33] algorithms, equivalation adaptive separation via independence (EASI) [34] algorithms, and joint approximate diagonalization of eigenmatrices (JADE) [35] algorithms. When separating the noiseless mixed signals, these ...