为了解决滚动轴承故障检测中出现的振动信号非线性问题,课题团队提出了一种基于小波包-核偏最小二乘(wavelet packet and kernel partial least squares method, WP-KPLS)的故障检测方法。首先对采集到的信号进行小波包分解,将振动信号...为了解决滚动轴承故障检测中出现的振动信号非线性问题,课题团队提出了一种基于小波包-核偏最小二乘(wavelet packet and kernel partial least squares method, WP-KPLS)的故障检测方法。首先对采集到的信号进行小波包分解,将振动信号分解到独立的频段,提取不同频段的能量谱,并构建反映频谱状态改变的能量谱特征向量;再对得到的能量谱特征向量进行核偏最小二乘分析,建立故障检测模型,利用T^(2)及SPE统计量来检测故障是否发生。实验结果表明:该方法能够较为准确地检测到轴承的内外圈故障,证明该模型是有效的。该方法综合了小波包对信号的分析优势和核偏最小二乘法在非线性情况下的数据处理优点,为解决故障检测中的非线性数据处理问题提供了一种新方法。展开更多
The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- ...The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.展开更多
文摘为了解决滚动轴承故障检测中出现的振动信号非线性问题,课题团队提出了一种基于小波包-核偏最小二乘(wavelet packet and kernel partial least squares method, WP-KPLS)的故障检测方法。首先对采集到的信号进行小波包分解,将振动信号分解到独立的频段,提取不同频段的能量谱,并构建反映频谱状态改变的能量谱特征向量;再对得到的能量谱特征向量进行核偏最小二乘分析,建立故障检测模型,利用T^(2)及SPE统计量来检测故障是否发生。实验结果表明:该方法能够较为准确地检测到轴承的内外圈故障,证明该模型是有效的。该方法综合了小波包对信号的分析优势和核偏最小二乘法在非线性情况下的数据处理优点,为解决故障检测中的非线性数据处理问题提供了一种新方法。
基金Supported by National Natural Science Foundation of China(51705431,51375078)Natural Sciences and Engineering Research Council of Canada(RGPIN-2015-04897)
文摘The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.
