国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理
关键词搜索：
| 标 题： |
A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture
联合稀疏小波系数提取与自适应降噪方法在多分量信号混合中恢复弱轴承故障特征 |
| 作 者： |
Dong Wang
Wei Guo
Xiaojuan Wang |
| 关键词： |
Cyclic spike detection method [循环尖峰检测方法]
Simulated annealing [ 模拟退火]
Wavelet transform [ 小波变换]
Fault diagnosis [ 故障诊断]
Rolling element bearing [ 滚动轴承] |
| 出版年： |
2013 |
| 期刊名： |
Applied Soft Computing
应用软计算 |
| 年卷期： |
2013, vol.13, no.10 |
| 摘 要： |
Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration signal caused by bearing localized defects is easily overwhelmed by other strong vibration signals. Extracting the bearing fault signal from a multi-component signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The filtered signal is then obtained by applying the optimal wavelet filter to the multi-component signal mixture. After that, a new adaptive local maximum selection method is proposed to make the filtered signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component signal mixtures, including a simulated signal and a real vibration signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration signals and noise.
滚动轴承广泛用于支撑机器的旋转部件。由于机器内部部件空间紧密，由轴承局部缺陷引起的振动信号容易被其他强振动信号所掩盖。因此，从多成分信号混合中提取轴承故障信号对于早期检测轴承故障特征、防止机器损坏具有重要意义。本文提出一种名为循环尖峰检测法的轴承故障诊断方法，用于从多成分信号混合中提取微弱的轴承故障特征。首先，通过单纯模拟退火算法结合最大稀疏性目标函数，确定复数莫雷特小波滤波器的最佳中心频率和带宽。然后，将最优小波滤波器应用于多成分信号混合，得到滤波后的信号。接着，提出一种新的自适应局部最大值选择方法，使滤波后的信号更加简洁，仅保留少数尖峰，以揭示由轴承局部缺陷引起的潜在循环间隔。采用两种多组分信号混合信号，包括模拟信号和从工业机械采集的实际振动信号，以验证所提出循环尖峰检测方法的有效性。结果表明，该方法能够从其他强干扰振动信号和噪声中提取出轴承微弱的故障特征。 |
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