Fault feature extraction based on optimal energy using lifting wavelet packet

Xiaoli Xu, Tao Chen, Shao-hong Wang
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Abstract

Fault prediction is the key technology to guarantee the safe operation of large mechanical equipment,and fault feature extraction is a key issue in fault prediction. To extract fault feature from the non-stationary fault signals, this paper proposed a fault feature extraction method using lifting wavelet packet, and constructed the fault feature vector of optimal energy. The fault feature extraction analysis shows that the proposed method can highlight the energy change within the optimal decomposition frequency band, and effectively reflect the fault status.
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基于最优能量的提升小波包故障特征提取
故障预测是保证大型机械设备安全运行的关键技术,故障特征提取是故障预测中的关键问题。为了从非平稳故障信号中提取故障特征,提出了一种提升小波包的故障特征提取方法,并构造了能量最优的故障特征向量。故障特征提取分析表明,该方法能突出最优分解频带内的能量变化,有效反映故障状态。
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