| 基于STFT和SVD的滚动轴承异常声识别 |
| |
| 引用本文： | 孙晖,朱善安. 基于STFT和SVD的滚动轴承异常声识别[J]. 轴承, 2003, 0(11): 24-27 |
| |
| 作者姓名： | 孙晖 朱善安 |
| |
| 作者单位： | 浙江大学,电气工程学院,浙江,杭州,310027 |
| |
| 摘 要： | 本文提出轴承异常声在振动曲线上可以被描述为一种瞬态脉冲，可以用时频谱图加以分析，通过奇异值分解有效地提取特征矢量，并将特征矢量送入神经网络分类器进行模式识别。仿真实验表明，这是一种稳定、有效的方法。
|
| 关 键 词： | 异常声 傅里叶变换 奇异值分解 神经网络 |
| 文章编号： | 1000-3762(2003)11-0024-04 |
| 修稿时间： | 2003-03-29 |
Rolling Bearing Abnormal Noise Identification Based on STFT and SVD |
| |
| SUN Hui,ZHU Shan-an. Rolling Bearing Abnormal Noise Identification Based on STFT and SVD[J]. Bearing, 2003, 0(11): 24-27 |
| |
| Authors: | SUN Hui ZHU Shan-an |
| |
| Abstract: | The abnormal sound of rolling bearing can be described a kind of transient pulses and can be analyzed by the time-frequency spectrogram. The characteristic vectors can be extracted effectively from singular value decomposition, and the pattern can be identified by sending it to the segregator of neural network. The simulation results show that it is a stable and efficient method. |
| |
| Keywords: | abnormal sound short-time Fourier transform (STFT) singular value decomposition neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
