基于驰振的压电能量采集器建模与实验研究

针对低信噪比环境下核电站松动件的检测，以降低误报率、漏报率为目标，提出了一种基于盲解卷积算法的松动部件冲击响应提取方法，并进一步结合支持向量机分类辨识算法，给出了一种低信噪比环境下核电站松动部件检测方法。利用叠加实堆背景噪声的平板钢球跌落实验数据开展了报警研究，并对盲解卷积算法进行了参数优化设计。结果表明：优化后的盲解卷积算法能够很好地恢复出信噪比低至-20dB的冲击响应信号，并使噪声能量降低了75%，有效抑制了噪声；给出的松动部件检测方法在信噪比低至-14dB时，仍具有极低的漏报率，并且噪声误报率和脉冲干扰误报率为零，因而具有良好的抗误报、抗漏报能力。

Modeling and Experimental Study of Galloping-Based Piezoelectric Energy Harvester

Aiming at the detection of loose parts in nuclear power plant under low signal-to-noise ratio (SNR) environment, an impulse response extraction method for loose parts based on blind deconvolution algorithm is proposed to reduce the false alarm rate and the false negative rate. Combined with Support Vector Machine classification identification algorithm, a detection method for loose parts of nuclear power plant is developed as well in low SNR environment. The simulatedstudies are carried out based on the test data from steel ball-flat plate drop experiment superimposed with real background noise, and the parameters involved in the blind deconvolution algorithm are optimized. The results show that the optimized blind deconvolution algorithm can effectively suppress the noise. Consequently, the impulse response signal with SNR as low as -20dB can be recovered well and the noise energy is reduced by 75%. The proposed detection method for loose parts has a very low false negative rate and a zero false alarm rate for noise and pulse interference even when the signal to noise ratio is as low as -14 dB. These results indicate the presented loose parts detection method has a good ability of anti-false positive and false negative.