Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District, Beijing, 100124, China.
Sensors (Basel). 2010;10(5):4602-21. doi: 10.3390/s100504602. Epub 2010 May 4.
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.
鉴于智能变速箱诊断方法存在的问题，很难获得所需的信息和足够大的样本量进行研究；因此，我们提出将各种方法应用于变速箱故障诊断，包括小波提升、支持向量机（SVM）和基于规则的推理（RBR）。在复杂的现场环境中，机器不太可能出现相同的故障；此外，故障特征也可能有所不同。因此，可以使用 SVM 进行初步诊断。首先，使用小波包分解处理变速箱振动信号，并提取每个频带的信号能量系数作为 SVM 的输入特征向量，用于正常和故障模式识别。其次，小波提升的精密分析可以成功地滤除噪声信号，同时保持故障的脉冲特性；从而有效地提取机器的故障频率。最后，根据专家总结的现场规则构建知识库，以识别详细的故障类型。结果表明，当样本量较小时，SVM 是完成变速箱故障模式识别的有力工具，而小波提升方案可以有效地提取故障特征，并且基于规则的推理可以用于识别详细的故障类型。因此，结合 SVM、小波提升和基于规则的推理的方法可以确保有效的变速箱故障诊断。
