Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing
Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing
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DOI：
10.1016/j.ymssp.2006.09.007
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发表时间：
2007-07-01
影响因子：
8.4
通讯作者：
Ramachandran, K. I.
中科院分区：
文献类型：
--
作者：
Sugumaran, V.;Ramachandran, K. I.
滚子轴承是旋转机械中使用最广泛的元件之一。这些元件的状态监测被视为模式识别问题。模式识别有两个主要阶段：特征提取和特征分类。最小值、标准误差和峰度等统计特征被广泛用作故障诊断中的特征。这些特征是从振动信号中提取的。规则集由提取的特征形成并输入到模糊分类器。构建模糊分类器所需的规则集主要是通过直觉和领域知识获得的。本文提出使用决策树从特征集中自动生成规则。来自压电传感器的振动信号在以下情况下被捕获：良好的轴承、有内圈故障的轴承、有外圈故障的轴承以及内外圈故障。提取统计特征，并使用决策树选择区分轴承不同故障情况的良好特征。利用决策树再次获得模糊分类器的规则集。建立模糊分类器并用代表性数据进行测试。结果令人鼓舞。 (c) 2006 Elsevier Ltd. 保留所有权利。
Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has two main phases: feature extraction and feature classification. Statistical features like minimum value, standard error and kurtosis, etc. are widely used as features in fault diagnostics. These features are extracted from vibration signals. A rule set is formed from the extracted features and input to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper presents the use of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions-good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The statistical features are extracted and good features that discriminate the different fault conditions of the bearing are selected using decision tree. The rule set for fuzzy classifier is obtained once again by using the decision tree. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging. (c) 2006 Elsevier Ltd. All rights reserved.