Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.
Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.
复制标题
基于SVM、小波提升和RBR的智能变速箱诊断方法
DOI：
10.3390/s100504602
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Chen P
中科院分区：
文献类型：
--
作者：
Gao L;Ren Z;Tang W;Wang H;Chen P
针对智能齿轮箱故障诊断方法难以获得所需信息和样本容量大等问题，提出了小波提升、支持向量机和基于规则推理等多种方法在齿轮箱故障诊断中的应用。在复杂的现场环境中，机器发生相同故障的可能性较小；此外，故障特征也可能不同。因此，支持向量机可以用于初步诊断。首先，对齿轮箱振动信号进行小波包分解，提取各频段的信号能量系数作为支持向量机的输入特征向量，用于正常和故障模式识别。其次，利用小波提升进行精度分析，可以在保持故障脉冲特征的同时，成功滤除噪声信号，从而有效地提取出机器的故障频率。最后，根据专家总结的领域规则建立知识库，识别详细的故障类型。结果表明，在小样本情况下，支持向量机是实现齿轮箱故障模式识别的有力工具，而小波提升方法能有效地提取故障特征，并利用基于规则的推理来识别详细的故障类型。因此，采用支持向量机、小波提升和基于规则推理相结合的方法，保证了变速箱故障的有效诊断。
Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.
影响因子：
8.4
作者：
Jack, LB;Nandi, AK
通讯作者：
Nandi, AK
DOI：
10.1243/0954406011524423
发表时间：
2001-01-01
影响因子：
2
作者：
Jack, LB;Nandi, AK
通讯作者：
Nandi, AK
影响因子：
2.5
作者：
Sweldens, W
通讯作者：
Sweldens, W