Improved variational mode decomposition method for vibration signal processing of flood discharge structure

It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring.

Operation feature extraction of flood discharge structure based on improved variational mode decomposition and variance dedication rate

Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.

Development of a microchip based on LIGA technology for bacterial inactivation

We designed and fabricated a new micro-needle-column discharge chip by Lithographie, Galvanoformung and Abformung technology to meet sterilization requirements. The dimensions of the chip are 11.6 mm × 11.7 mm × 3 mm. The micro-discharge structure is composed of a needle electrode and two cylinder electrodes. We employed a needle with a tip diameter of 20 [Formula: see text]m and a thickness of 20 [Formula: see text]m as the discharge cathode. We used a cylinder with a thickness of 1 mm and diameter of 0.4 mm as the anode. We carried out the gas discharge experiments in ambient air and at room temperature, without external air flow. In the experiments, a corona and glow discharge with applied voltages of −2.1 kV and −3.8 kV were realized. We carried out the sterilization experiments for six different discharge times (15, 30, 60, 90, 120 and 180 s) with an applied voltage of −3.8 kV. The inactivation efficiency of Escherichia coli and Staphylococcus aureus reached 100% for sterilization times of 120 s and 180 s. This indicates that the micro-needle-column discharge chip may be suitable as a portable sterilizer.

Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm

Extraction of the vibration characteristics of a flood discharge structure under the influence of intensive background noise is one of the main challenges in vibration-based damage identification. A novel algorithm called normalized central frequency difference spectrum is proposed to improve the variational mode decomposition algorithm for high-frequency noise filtering. To eliminate the errors caused by end effect, the waveform matching extension algorithm is used to further improve the variational mode decomposition. However, the vibration signal is still coupled in low-frequency noise. Thereupon, the singular spectrum analysis algorithm is applied to filter the low-frequency noise. In this article, a simulated signal and the measured signals from a dam model are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high denoising precision. In addition, this algorithm can offer clues for damage identification and localization of a flood discharge structure.

Pulse Current of Multi-Needle Negative Corona Discharge and Its Electromagnetic Radiation Characteristics

Negative corona discharge occurs widely in high voltage transmission lines and other “high voltage” uses, which can cause strong electromagnetic interference (EMI). In this research, the pulse current of multi-needle negative corona discharge and its electromagnetic (EM) radiation characteristics were studied and compared with that of single-needle negative corona discharge. A dipole radiation model was established to analyze the EM radiation characteristics of the negative corona discharge. The results show that the Trichel pulse discharge process of one discharge needle in multi-needle discharge structure will inhibit the discharge of the other discharge needles. It is only when the voltage reaches a certain threshold will the current and EM radiation fields of multi-needle discharge structure with a significant increasing of amolitude. The frequency of EM radiation of negative corona discharge is not affected by the number of needles, but is only related to ambient air pressure. This research provides a basis for detecting corona discharge sources in different conditions.