基于EEMD和自相关函数特性的自适应降噪方法
在机械设备早期故障中，反映故障特征的冲击信号非常微弱，容易被噪声淹没，使得故障诊断有一定难度。集合经验模式分解方法将含噪信号分解为多个固有模式分量，其中包括噪声分量和有用信号分量。根据两者自相关函数特性的不同，提出了利用能量集中比找到噪声分量分界点的自适应降噪方法，并利用改进的软阈值方法拾取噪声分量中的高频有用信号。对不同频率的合噪信号进行降噪处理，结果表明，该方法对中低频信号的降噪具有很好的效果。故障轴承振动信号的降噪效果表明该方法的实用性。...
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| Published in | 计算机应用研究 Vol. 32; no. 1; pp. 206 - 209 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
武汉科技大学冶金自动化与检测技术教育部工程研究中心,武汉430081
2015
中原工学院信息商务学院信息工程系,郑州451191%武汉科技大学冶金自动化与检测技术教育部工程研究中心,武汉,430081 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2015.01.047 |
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| Summary: | 在机械设备早期故障中，反映故障特征的冲击信号非常微弱，容易被噪声淹没，使得故障诊断有一定难度。集合经验模式分解方法将含噪信号分解为多个固有模式分量，其中包括噪声分量和有用信号分量。根据两者自相关函数特性的不同，提出了利用能量集中比找到噪声分量分界点的自适应降噪方法，并利用改进的软阈值方法拾取噪声分量中的高频有用信号。对不同频率的合噪信号进行降噪处理，结果表明，该方法对中低频信号的降噪具有很好的效果。故障轴承振动信号的降噪效果表明该方法的实用性。 |
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| Bibliography: | 51-1196/TP In the incipient failure of mechanical equipment, the impulse reflecting the fault feature is too weak to extract from strong noise, which increases the difficulty of fault diagnosis. The ensemble empirical mode decomposition method decomposes a noisy signal into several intrinsic mode functions, including noise and useful signal components. There are difference in auto- correlation function properties between noise and useful signal components. According to this, it presented an adaptive de-noi- sing method,which used the energy concentration ratio to find noise component cut-off point and applied an improved soft threshold method to pickup useful high-frequency signal from noise component. The de-noisy results of simulation signals with different frequency show its efficiency for the intermediate and low frequency signal de-noisy. The noise reduction effect of fault bearing vibration signal proves its feasibility. YU Fa-jun ZHOU Feng-xing （ 1. Metallurgical Automation ＆ Detection Technology ERC of |
| ISSN: | 1001-3695 |
| DOI: | 10.3969/j.issn.1001-3695.2015.01.047 |