一种基于压缩感知的轴承故障检测方法
首发时间:2013-07-02
摘要:在频域对轴承进行故障检测时,传统的方法需要收集所关心频段的所有数据点然后分析判断轴承状态,当感兴趣的频段很宽时将增大数据存储压力。针对此问题,本文提出一种基于压缩感知的轴承故障检测方法,可以在实现故障检测的同时减轻数据存储压力。该方法首先对所关心频段的数据进行压缩采样,得到相对来说很少的数据点,然后利用匹配追踪算法找到所关心频段内最大的幅值位置,从而判断轴承的状态。该方法不需要完整地恢复出原信号,因此不要求原信号是稀疏的,从而扩大了该方法的适用范围。实验结果验证了所提出方法的有效性。
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A Bearing Fault Detection Method base on Compressed Sensing
Abstract:For bearing fault detection in frequency domain, traditional methods estimate bearing fault condition based on the whole data points of interesting frequency range, which will burden the storage when interested frequency range is extremely wide. A new bearing fault detection method based on compressed sensing will be proposed in this paper in allusion to the problem mentioned above, which can achieve fault detection and also alleviate the storage burden of mass data. The method presented here carried out compressive sampling first to interested range in frequency domain and few data points relatively will be acquired. Then the position corresponding to the maximum amplitude will be located using the few data points based on matching pursuit and the bearing condition will be estimated finally. Sparsity of original signal is not demanded since the signal does not need to be recovered completely, which will also helped to expanded the method to other signals with similar characteristics in frequency domain. Related test will be achieved to verify the effectiveness of the method proposed in this paper.
Keywords: Mechatronic Engineering Bearing Fault Detection Compressed Sensing Matching Pursuit
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