Study on dry friction vibration model based on energy method

Purpose This study aims to establish the friction vibration model. Design/methodology/approach The friction vibration experiment was carried out on a pin disk friction tester. The causes of friction vibration are discussed, and the friction vibration model is established based on the energy method. Findings The experimental and simulation results show that the main cause of friction vibration is the nonlinear change of friction coefficient; degree of the friction vibration has a positive relationship with the friction relative velocity and normal contact positive pressure; the proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration. Originality/value The proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration.

Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.

Specific Emitter Identification Based on Multi-Domain Feature Fusion and Integrated Learning

Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources according to the varying individual characteristics of the emitter hardware within the transmitted signals. In response to the lack of scarcity among labeled samples in specific emitter identification, this paper proposes a method combining multi-domain feature fusion and integrated learning (MDFFIL). First, the received signal is preprocessed to obtain segmented time domain signal samples. Then, the signal is converted to time–frequency distribution using wavelet transform. Afterwards, an integrated learning two-stage recognition classification method is designed to extract data features of 1D time domain signals and 2D time–frequency distribution signals using the symmetry network structures of CVResNet and ResNet. Finally, fused features are fed into the complex-valued residual network classifier to obtain the final classification results. We demonstrate through the analysis results of the measured data that the proposed method has a higher accuracy as compared with the classical feature extraction method, and that this can improve the identification of communication radiation sources with fewer labeled samples.

A System-of-Systems Perspective on Frequency Estimation: Time-Frequency Distribution of Multiple LFM Signals

This chapter provides a System-of-Systems (SoS) perspective on a study of frequency estimation of signals with a focus on Linear Frequency Modulation (LFM) signals. This chapter describes an SoS approach for frequency estimation using Chirplet Transform (CT), Hough Transform (HT), and the Short Time Fourier Transform (STFT) with filtering viewpoint. The filtering viewpoint employs the filter impulse response length to obtain the best time-frequency concentration for accurate estimation of a signal frequency. The optimum impulse response length can be found by varying the length of the filter impulse response and observe the changing in the time-frequency distribution (TFD). The chapter shows that when the length of the impulse response becomes longer, the time-frequency concentration in TFD increases first and then decreases.