Zhang Nannan, Wu Lifeng, Yang Jing, Guan Yong
College of Information Engineering, Capital Normal University, Beijing 100048, China.
Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China.
Sensors (Basel). 2018 Feb 5;18(2):463. doi: 10.3390/s18020463.
The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.
轴承是旋转机械的关键部件，其性能直接决定系统的可靠性和安全性。基于数据的轴承故障诊断已成为研究热点。基于独立假设的朴素贝叶斯（NB）在故障诊断中被广泛应用。然而，轴承数据并非完全独立，这降低了NB算法的性能。为解决此问题，我们提出一种基于增强数据独立性的NB轴承故障诊断方法。该方法从属性特征和样本维度两个方面处理数据向量。经过处理后，通过独立性假设降低了NB的分类局限性。首先，我们有效地提取轴承原始信号的统计特征。然后，使用决策树算法选择时域信号的重要特征，并选择低相关性特征。接下来，使用选择性支持向量机（SSVM）对维度数据进行修剪并去除冗余向量。最后，我们使用NB对低相关性数据进行故障诊断。实验结果表明，数据的独立性增强对轴承故障诊断是有效的。
