For a stochastic resonance system, the characteristics of the nonlinear model have an important influence on the output. To further improve the enhanced detection of stochastic resonance, a novel potential well stochastic resonance model is constructed to simultaneously solve the problem of the output saturation and high barrier in the classical bistable model. Analytical expressions of the Kramers rate and output signalto-noise ratio are presented, and weak signal detection is theoretically analyzed. The performance of the system based on the novel potential well model is simulated and analyzed. Finally, the proposed model is used for the detection of multiple high-frequency weak signals in an α-stable noise environment and a practical bearing fault signal. The simulation and experimental results demonstrate that the output of the stochastic resonance system proposed in this paper exhibits a large output signal-to-noise ratio and a high spectral peak at the characteristic frequency.

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In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy–Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected.

In this work, a circular polarized (CP) maintaining metasurface is proposed, which can realized ultra-wideband CP-maintaining reflection and make its co-polarization reflection coefficient under CP incidence close to 1.0 in the frequency range from 6.2 to 26.4 GHz. Because Pancharatnam-Berry (PB) phase can be generated in the co-polarized reflection coefficient under CP incidence by rotating its unit cell structure, a 2-bit PB coding metasurface for Radar Cross Section (RCS) reduction is further proposed based on the CP-maintaining metasurface. The simulated results show that the proposed PB coding metasurface has excellent performance in RCS reduction, compared with a pure metal plate with the same size, its RCS can be reduced more than10dB under arbitrary polarization normal incidences in the ultra-wide frequency band 6.2-26.5GHz with relative band of 124.1%. Finally, an effective experimental validation is carried out.

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