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An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information, each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized. 相似文献
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一种用于滚动轴承故障诊断的方法 总被引:1,自引:0,他引:1
采用振动分析法来进行滚动轴承元件的故障诊断.通过带通滤波、包络谱分析和小波包分析提取了反映滚动轴承故障的5个频域特征参数,同时还提取了对轴承早期冲击故障较敏感的5个时域指标.基于上述10个故障特征值,采用BP神经网络、基于遗传算法的RBF神经网络进行故障分类训练.试验结果表明上述10个特征值对不同的滚动轴承故障非常敏感;BP网络和基于遗传算法的RBF网络都能有效地分类不同故障;基于遗传算法的RBF网络在训练时间、训练误差以及识别精度上优于BP网络.试验证明了上述方法在滚动轴承故障诊断中的有效性. 相似文献
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介绍了模拟神经网络VLSI脉冲流技术实现神经网络模式识别硬件电路的方法,并且直接将故障分类。提出利用包含有故障信息的原始模拟噪声信号,经过前置信号处理和神经网络运算,得出VLSI电路输出端电容的电压值-代表待识别信号与模板故障信号的“欧氏距离”,以实现噪声故障信号的实时硬件在线识别。 相似文献
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