Intelligent Modeling of Abnormal Vibration for Large-Complex Machine Based on Chaos and Wavelet Neural Networks

Zhonghui Luo
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Abstract

This paper analyses the chaotic characteristics of a large temper rolling millpsilas abnormal vibration signals, and studies phase space reconstruction techniques of the signals. Then, combining the theory of chaotic dynamics and wavelet neural networks, a new vibration model is set up, through inversion method. The property of the model is tested and compared with the model of backpropagation(BP) neural networks, respectively. The result shows that the wavelet neural networks have an advantage over the backpropagation neural networks in rapid convergence and high accuracy.
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基于混沌和小波神经网络的大型复杂机械异常振动智能建模
分析了某大型回火轧机异常振动信号的混沌特性,研究了异常振动信号的相空间重构技术。然后,结合混沌动力学理论和小波神经网络,通过反演方法建立了新的振动模型。对该模型的性能进行了测试,并与BP神经网络模型进行了比较。结果表明,与反向传播神经网络相比,小波神经网络具有收敛速度快、精度高等优点。
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