| 综合模式分量能量及时频域特征的柴油机故障诊断 |
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| 引用本文： | 李敏通,杨 青,宋 蒙,杨福增.综合模式分量能量及时频域特征的柴油机故障诊断[J].农业工程学报,2012,28(21):37-43. |
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| 作者姓名： | 李敏通 杨 青 宋 蒙 杨福增 |
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| 作者单位： | 西北农林科技大学机械与电子工程学院,杨凌 712100 |
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| 基金项目： | 国家"十二五"科技支撑计划项目（2011BAD20B10-3） |
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| 摘 要： | 柴油机缸盖振动信号中包含着丰富的柴油机工作状态信息,利用缸盖振动信号诊断柴油机工作状态是一种有效方法。鉴于缸盖振动信号非平稳性的特点,该文提出用经验模式分解方法对获取的信号进行分解,选取前三阶的模式分量近似代替原信号,用模式分量的能量百分比、重心频率和重心幅值、偏度、峭度、方差等构成柴油机工作状态特征向量,基于支持向量机对实测的柴油机故障进行诊断分类,诊断的正确率达到92%以上,验证了方法的可行性。该研究也可为其他机械设备的故障诊断提供参考。
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| 关 键 词： | 柴油机 故障诊断 特征提取 支持向量机 EMD |
| 收稿时间： | 6/3/2012 12:00:00 AM |
| 修稿时间： | 2012/10/10 0:00:00 |
Fault diagnosis of diesel engine based on energy and time frequency domain characteristics of vibration signals IMF |
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| Li Mintong,Yang Qing,Song Meng and Yang Fuzeng.Fault diagnosis of diesel engine based on energy and time frequency domain characteristics of vibration signals IMF[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(21):37-43. |
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| Authors: | Li Mintong Yang Qing Song Meng and Yang Fuzeng |
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| Institution: | (College of Mechanical and Electronic Engineering, Northwest A & F University, Yang ling 712100, China) |
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| Abstract: | It is a more convenient way to use vibration signals for the fault diagnosis of diesel engine since such signals contain a lot of useful information which can reflect the status of the diesel engine. In view of the non-stationary characteristics of the vibration signals, the empirical mode decomposition was used to decompose the signals obtained, the three main IMFs of signals were selected approximately to replace the original signals, and their energy percentage, gravity frequency, center of gravity amplitude, skewness and kurtosis were used as the feature vector of the status of the diesel engine. Based on support vector machines, the diesel engine fault diagnosis was conducted applying vectors obtained in the method presented in this paper. Diagnostic accuracy rate reached above 92%, which verified the feasibility of the method. The research can provide a reference for other mechanical equipment fault diagnosis. |
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| Keywords: | diesel engine failure analysis feature extraction support vector machine empirical mod decomposition |
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