基于奇异值分解和未消差提升方案包的初始特征提取

Li-xiang Duan, Nan Liu, Yu Tang, Yafeng Liu, Qinchun Zhang
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引用次数: 1

摘要

从机械设备上测得的振动信号常常受到各种噪声的严重干扰。本文提出了一种联合降噪方法,从分解的子带中获取增强信号,提取早期故障特征。首先,采用奇异值分解(SVD)方法对信号进行去噪。然后,采用非消差提升方案包(ULSP)将去噪后的信号分解为四层。最后,绘制第四层全部16个子带,利用断层信息丰富的子带提取早期特征。仿真数据验证了该方法的有效性。在工程信号处理中,对往复式压缩机阀门故障引起的微弱特征进行膨胀,检测弹簧的早期故障。
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Incipient Feature extraction based on singular value decomposition and undecimated lifting scheme packet
Vibration signal measured from machinery is often heavily interfered with by various noises. This paper puts forward a joint method to reduce noises, acquire the enhanced signals from the decomposed subbands and extract the incipient fault features. First, the signals are denoised by the method of singular value decomposition (SVD). Then, the denoised signal is decomposed into four layers by undecimated lifting scheme packet (ULSP). Finally, all 16 subbands of the fourth layer are plotted and the rich-fault-information subbands are used to extract incipient features. The effectiveness of the proposed method is validated with simulated data. Furthermore, in the processing of engineering signal, the weak feature caused by the fault of a valve in reciprocating compressor is bulged and the early failure of spring is detected.
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