Fault detection of roller-bearings using signal processing and optimization algorithms.
Fault detection of roller-bearings using signal processing and optimization algorithms.
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DOI：
10.3390/s140100283
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发表时间：
2013-12-24
期刊：
影响因子：
--
通讯作者：
Koh BH
中科院分区：
文献类型：
--
作者：
Kwak DH;Lee DH;Ahn JH;Koh BH
本研究提出了通过信号处理和优化技术对滚子轴承进行故障检测。轴承内圈出现划痕型缺陷后，采用两种不同的数据处理技术研究峰度值的变化：最小熵反卷积（MED）和Teager-Kaiser能量算子（TKEO）。 MED 和 TKEO 用于定性增强轴承振动数据上缺陷引起的重复峰值与测量噪声的辨别能力。从 MED 和 TKEO 的执行顺序来看，研究发现对轴承缺陷的峰度敏感性可以大大提高。此外，来自健康轴承和损坏轴承的振动信号通过经验模态分解 (EMD) 被分解为多个固有模态函数 (IMF)。 IMF 的权重向量成为遗传算法 (GA) 的设计变量。每个IMF的权重可以通过遗传算法进行优化，以增强峰度对受损轴承信号的敏感性。实验结果表明，EMD-GA 方法成功提高了有缺陷的滚子轴承与完整系统之间的检测分辨率。
This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.
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