共查询到19条相似文献,搜索用时 125 毫秒
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针对活塞式氢气压缩机各级排气温度超限的故障难以用传统故障的二态性准确描述,以及目前未见能对该故障实施自诊断的故障诊断系统等问题,以DW-(5.6-13)/(0.2-0.5)-20型无油润滑变频活塞式氢气压缩机为研究对象,对相关零部件进行了重要度分析,并设计了故障自诊断系统。首先,对该故障搭建了模糊高木-关野(T-S)故障树模型,并利用基于贝叶斯网络(BN)的T-S故障树分析法对底事件进行了重要度分析,以确定薄弱环节和故障零部件诊断顺序;然后,利用模糊数表征了零部件的故障状态,对排气温度超限故障发生概率进行了预测;最后,设计了基于规则的故障诊断专家系统,并对实例进行了故障原因的推理排查。研究结果表明:基于规则的故障诊断专家系统在对现有故障数据进行分析后,其诊断结果与事实相符,说明该系统能够准确地诊断出故障原因,并进行自诊断;同时,基于BN的T-S故障树分析法,使该系统可对顶事件故障发生概率进行预测,为活塞式氢气压缩机故障诊断提供了一个可行方案。 相似文献
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连续小波变换在往复泵泵阀故障识别中的应用 总被引:10,自引:1,他引:9
探讨了小波变换在往复泵泵阀故障识别的应用。分析研究表明,通过对测取的往复泵阀箱上振动加速度信号的小波变换,可有效提取泵阀失效的故障信息,结合泵阀关闭位置信号的相位分析,可较为准确地判别三缸泵任一泵阀的故障,该方法用到的测量数据少,具有较为理想的诊断树现场往复泵泵阀状态监测与故障诊断具有重要意义。 相似文献
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文章根据数控机床故障诊断的特点,分析了数控机床故障诊断系统的故障原因和故障源,然后引入一种带联想记忆功能的神经网络模型,并介绍了这种模型的原理和算法;最后利用所得的神经网络模型,对该故障系统的样本进行学习并记忆,再就检测或用户输入得来的故障原因进行联想回忆,得到诊断结果。实验结果表明,这种基于神经网络联想记忆的数控机床故障诊断系统简单,且能满足数控系统的故障诊断要求。 相似文献
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介绍了往复泵泵阀故障诊断系统的原理与结构,阐述了系统并对之进行分析、诊断的基本方法与过程,通过可靠的振动信号分析结果,证明了该系统的有效性。 相似文献
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针对飞行载体中余度敏感器结构的故障诊断,讨论了基于神经网络的直接比较测量值的诊断方法。应用自适应线性元件能实现线性,感知器具有分类的特点,设计出一种神经网络,可用于余度敏感器结构的故障诊断。仿真结果表明:这种方法不仅可以检测出故障、故障的类型和发生的时间,而且灵敏度高,为余度敏感器结构的故障诊断提出了一种有实用价值的方法。 相似文献
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C.L. Huang T.S. Li T.K. Peng 《The International Journal of Advanced Manufacturing Technology》2005,27(1-2):119-127
This paper proposes an integrated intelligent system that builds a fault diagnosis inference model based on the advantage
of rough set theory and genetic algorithms (GAs). Rough set theory is a novel data mining approach that deals with vagueness
and can be used to find hidden patterns in data sets. Based on this approach, minimal condition variable subsets and induction
rules are established and illustrated using an application for motherboard electromagnetic interference (EMI) test fault diagnosis.
This integrated system successfully integrated the rough set theory for handling uncertainty with a robust search engine,
GA. The result shows that the proposed method can reduce the number of conditional attributes used in motherboard EMI fault
diagnosis and maintain acceptable classification accuracy. The average diagnostic accuracy of 80% shows that this hybrid model
is a promising approach to EMI diagnostic support systems . 相似文献
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基于免疫机理的往复压缩机气阀故障检测方法 总被引:9,自引:0,他引:9
气阀故障是往复压缩机最常见的故障类型之一,由于往复压缩机的工作机理复杂,故障样本缺乏,难以应用常规方法对其进行有效的故障检测。为了能够准确检测气阀的各种故障,基于生物免疫系统的反面选择机理及反面选择算法,首先对设备故障检测问题进行了描述,引进了设备状态空间、自己—非己空间及模糊空间的概念,继而提出了适于往复压缩机气阀故障检测的新方法。通过对气阀常见故障的检测结果表明,所提出的方法能够以异常度曲线的形式较好地反映出气阀的各种故障,表明了该方法的有效性。基于免疫机理的设备故障检测方法,是在对设备正常运行数据学习的基础上,实现对设备的故障检测,不需要设备运行的故障数据,它适合对故障数据缺乏的设备进行有效的故障检测。 相似文献
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采用粗糙集理论来进行装载机故障知识的获取,给出了装载机诊断推理的策略和流程。该方法提高了诊断系统的容错性,为在不完备信息下的装载机故障诊断提供了一种新的思路。实例诊断结果证明了该方法的有效性。 相似文献
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Yang Ping 《Frontiers of Mechanical Engineering in China》2006,1(2):162-167
Large amounts of data in the SCADA systems’ databases of thermal power plants have been used for monitoring, control and over-limit
alarm, but not for fault diagnosis. Additional tests are often required from the technology support center of manufacturing
companies to diagnose faults for large-scale equipment, although these tests are often expensive and involve some risks to
equipment. Aimed at difficulties in fault diagnosis for boilers in thermal power plants, a hybrid-intelligence data-mining
system based only on acquired data in SCADA systems is structured to extract hidden diagnosis information directly from the
SCADA systems’ databases in thermal power plants. This makes it possible to eliminate additional tests for fault diagnosis.
In the system, a focusing quantization algorithm is proposed to discretize all variables in the preparation set to improve
resolution near the change between normal value to abnormal value. A reduction algorithm based on rough set theory is designed
to find minimum reducts from all discrete variables in the preparation set to represent diagnosis rules succinctly. The diagnosis
rules mining from SCADA systems’ database are expressed directly by variables in the database, making it easy for engineers
to understand and use in industry applications. A boiler fault diagnosis system is designed and realized by the proposed approach,
its running results in a thermal power plant of Guangdong Province show that the system can satisfy fault diagnosis requirement
of large-scale boilers and its accuracy rangers from 91% to 98% in different months. 相似文献
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基于矢谱和粗糙集理论的旋转机械故障诊断 总被引:1,自引:0,他引:1
矢谱融合了转子同源双通道的信息,能准确反映转子运动状态.粗糙集理论是一种对决策表进行简化,去除冗余属性的数据分析和处理方法.提出了基于矢谱和粗糙集理论的旋转机械故障诊断方法.计算了旋转机械振动4种典型故障的矢谱征兆,使用粗糙集理论对其进行约简,根据约简的结果生成矢谱诊断规则,并利用得到的规则对故障测试样本进行了诊断.结果表明:相对于单通道数据,基于矢谱和粗糙集理论的故障诊断不仅简化了诊断规则,而且明显提高了故障诊断的准确率. 相似文献