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| 大型复杂装备智能化故障诊断方法 | |
| 引用本文： | 杨丽金,杨业,田静宜. 大型复杂装备智能化故障诊断方法[J]. 煤矿机械, 2012, 33(1): 267-269 |
| 作者姓名： | 杨丽金 杨业 田静宜 |
| 作者单位： | 1. 山西平遥减速器厂,山西平遥,031100 2. 唐山轨道客车有限责任公司,河北唐山,063035 3. 河北联合大学轻工学院,河北唐山,063000 |
| 摘 要： | 为提高大型复杂装备故障诊断的准确率和效率,提出了一种智能化故障诊断方法。该方法以粗糙集理论为基础,实现信号采集位置优化和特征值约简,应用信息融合技术将振动和噪声信号融合诊断,通过人工神经网络进行故障模式分类,以柴油机为例,验证了方法的正确性和有效性。 |
| 关 键 词： | 粗糙集理论 信息融合 人工神经网络 故障诊断 |
Intelligent Fault Diagnosis of Large and Complex Equipment |
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| YANG Li-jin , YANG Ye , TIAN Jing-yi. Intelligent Fault Diagnosis of Large and Complex Equipment[J]. Coal Mine Machinery, 2012, 33(1): 267-269 | |
| Authors: | YANG Li-jin YANG Ye TIAN Jing-yi |
| Affiliation: | 1.Shanxi Pingyao Reducer Factory,Pingyao 031100,China;2.Tangshan Railway Vehicle Co.,Ltd.,Tangshan 063035,China;3.College of Light Industry,Hebei United University,Tangshan 063000,China) |
| Abstract: | In order to improve fault diagnosis accuracy and efficiency of large and complex equipment,proposed an intelligent fault diagnosis method.The method is based on rough set theory,can optimize monitoring location and domain values.The application of information fusion technology will diagnose vibration and noise signal integration.Fault diagnosis can be achieved by artificial neural network.Take diesel engines for example,the process verified correctness and effectiveness of method. |
| Keywords: | rough set information fusion artificial neural networks fault diagnosis |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |