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首页> 外文会议>Natural Computation (ICNC), 2012 Eighth International Conference on >Adaptive cancellation of background machine noise based on combination of ICA-R and RBFNN
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Adaptive cancellation of background machine noise based on combination of ICA-R and RBFNN

机译：基于ICA-R和RBFNN的自适应背景机器噪声消除

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摘要

Extraction of machine fault signals from background machine noises is of great help in improving the accuracy of machine fault diagnosis. In this paper, a prediction model of time series based on RBF neural network (RBFNN) is proposed to learn the priori knowledge of background machine noise that obscure in a template noise which is tailored from the historical samples of background machine noises. By defining the mean square error of prediction to candidate independent component with the proposed RBFNN model as the contrast function, a new ICA-R algorithm is proposed to extract the ‘pure’ background machine noise which is then taken as reference input of a Volterra Adaptive Noise Cancellation (VANC) system. The simulation shows that the combination of ICA-R and VANC system prevails over a standard VANC system. The new VANC system is easier to be implemented in engineering applications due to its sensor-position independent characteristics.
机译：从背景机器噪声中提取机器故障信号对提高机器故障诊断的准确性很有帮助。本文提出了一种基于RBF神经网络（RBFNN）的时间序列预测模型，以学习根据背景机器噪声的历史样本量身定制的模板噪声中掩盖的背景机器噪声的先验知识。通过将提出的RBFNN模型作为对比函数，将预测的均方误差定义为候选独立分量，提出了一种新的ICA-R算法来提取“纯”背景机器噪声，然后将其作为Volterra自适应算法的参考输入降噪（VANC）系统。仿真表明，ICA-R和VANC系统的组合优于标准VANC系统。由于其与传感器位置无关的特性，新的VANC系统更易于在工程应用中实施。

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