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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
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Nan Xie Lin Chen Aiping Li 《The International Journal of Advanced Manufacturing Technology》2010,48(9-12):1239-1247
Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. 相似文献
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戴红伟 《机械设计与制造工程》2014,(5):79-82
以机舱监控系统研发为背景,给出了船舶故障诊断系统的总体框架,详细阐述了船舶故障的分类表示。提出了一种基于粗糙集的数据预处理算法,实现了采集实时数据的约简,得到最小的故障诊断数据的约简集,提高了故障诊断的准确性及效率。 相似文献
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基于粗糙集的逻辑故障树方法及其应用 总被引:5,自引:0,他引:5
故障树是一种分析复杂系统可靠性、诊断系统故障的有效方法。在传统的故障树方法中 ,故障树的生成通常靠人工完成。故障知识的获取以及故障树结构的确定一直是有待解决的瓶颈问题。这里结合粗糙集、专家系统及人工智能等理论 ,提出了构造逻辑故障树进行故障诊断的方法并给出了相应的故障树评价标准。这种方法利用粗糙集对知识系统的知识发现和知识提取能力 ,从系统运行状态样本中建立基于知识的故障树模型。通过实例讨论了如何运用该方法对工业监控过程进行故障建模 ,检测系统运行过程中所发生的故障。 相似文献
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针对目前垃圾破碎机故障诊断效率低的问题,设计了一种基于粗糙集理论与BP神经网络的故障诊断系统。结合粗糙集理论和BP神经网络的优点,首先利用粗糙集对原始故障诊断样本进行处理,然后对条件属性进行约简,删除冗余的信息,减少神经网络输入端的数据,从而简化神经网络的结构。并将基于粗糙集-BP神经网络的故障诊断系统对垃圾破碎机进行故障诊断。利用粗糙集对故障知识进行约简,简化BP神经网络结构,提高故障诊断的速度及准确度。将此方法应用于某型号垃圾破碎机的故障诊断中,诊断结果表明所提诊断方法可简化神经网络结构,提高诊断效率。 相似文献
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A Rough Set Approach to the Ordering of Basic Events in a Fault Tree for Fault Diagnosis 总被引:10,自引:0,他引:10
L.P. Khoo S.B. Tor J.R. Li 《The International Journal of Advanced Manufacturing Technology》2001,17(10):769-774
The performance of manufacturing systems or equipment is, to a great extent, dependent upon the condition of their components.
Closely monitoring the condition of the critical components and carrying out timely system diagnosis whenever a fault symptom
is detected would help to reduce system downtime and improve overall productivity. Fault tree analysis (FTA) is a powerful
tool for reliability studies and risk assessment. However, most research on FTA focuses on the generation of minimum cut sets
and how to calculate the probability of main events. As a result, the issue concerning the ordering of basic events in a fault
tree has been largely neglected. In this paper, a novel approach based on rough set theory and a pairwise comparison table
for fault diagnosis is proposed. The approach attempts to learn from the pattern of decision-making by domain experts from
past experience and uses the knowledge acquired, which is in the form of a minimum decision rule set, to determine the ordering
of basic events in a fault tree. The details of the approach, together with the basic concepts of rough set theory, are presented.
A case study is used to illustrate the application of the proposed approach. Results show that a reasonable ordering of basic
events in a fault tree can be generated easily. With the ordering of basic events determined, a maintenance engineer in a
manufacturing plant can then carry out fault diagnosis in an efficient and orderly manner. 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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Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump 总被引:2,自引:0,他引:2
The fault diagnosis problem is conceived as a classification problem. In the present study, vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis. Rough set theory is applied to generate the rules from the vibration signals. Based on the strength of the rules the faults are identified. The different faults considered for this study are: pump at good condition, cavitation, pump with faulty impeller, pump with faulty bearing and pump with both faulty bearing and impeller. However, the classification accuracy is based on the strength and number of rules generated using rough set theory. Wavelet features are computed using Discrete Wavelet Transform (DWT) from the vibration signals and rules are generated using rough sets and classified using fuzzy logic. The results are presented in the form of confusion matrix which shows the classification capability of wavelet features with rough set and fuzzy logic for fault diagnosis of monoblock centrifugal pump. 相似文献
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设备故障智能诊断方法的研究 总被引:8,自引:0,他引:8
模糊聚类、粗糙集理论、灰色系统理论等相关技术曾被广泛应用于设备故障诊断中,但是模糊聚类只能对已知样本做出决策,不具有柔性,不能通过已知信息和聚类结果对问题所涉及领域内的新样本的类别做出决策;粗糙集理论不能处理连续变量;而灰色系统理论无法去除故障诊断中冗余的特征参数,不能区分各特征参数的重要性,因而制约了它们在故障诊断中的应用.在本文中,这几种理论被有机地结合起来,应用于设备故障诊断中.在故障诊断过程中,首先利用模糊c均值聚类对样本的参数进行离散化处理,求得各类别的聚类中心,接着基于粗糙集原理对设备特征参数进行约简,去除冗余参数,定量确定各特征参数的重要程度,然后根据约简的特征参数和各参数的重要程度,利用灰色关联分析的方法确定各种标准故障状态与目前设备状态的关联度,从而找到设备的故障所在之处.在本文最后部分通过实例证明,将模糊c均值聚类、粗糙集理论和灰色系统理论结合起来,应用于设备的故障诊断中是一种行之有效的方法,为智能故障诊断提供了理论基础. 相似文献
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提出了一种基于粗糙集与支持向量机的电动机转子断条故障诊断方法。首先将电动机在不同故障状态下的振动信号离散化,再应用粗糙集软件rosetta对数据进行进一步的约简,得到约简后的数据应用于支持向量机的训练从而得到基于支持向量机的多分类器。实验证明:该方法检测电动机的转子断条故障是可行的。 相似文献
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基于粗糙集理论的变压器故障诊断专家系统研究 总被引:4,自引:2,他引:4
在传统的变压器故障诊断专家系统的基础上 ,引入粗糙集理论以解决专家系统较难获取完备知识的瓶颈问题。该系统从历史故障数据所形成的决策表出发 ,运用粗糙集理论进行约简 ,构建专家系统知识库模型。通过计算规则隶属粗糙度 ,来表示诊断规则的置信程度。利用推理机和故障事例库 ,实现对知识库的动态维护。 相似文献
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旋转机械在实际工程应用中常处于正常状态,因而呈现故障样本稀少甚至部分缺失等非理想数据情况。针对直接采用非理想数据建立深度学习诊断模型时的低准确率问题,提出基于有限元仿真数据辅助迁移学习的故障诊断方法。首先,通过数值仿真计算不同运行工况和故障类型的轴承信号;进而,利用大量低成本高保真的仿真样本对模型预训练,利用真实小样本或者仿真样本增补后的混合样本进行模型微调,以完成高准确率故障诊断,并降低迁移学习对故障轴承实测数据的依赖;最后,利用两个轴承实验台数据进行验证。结果表明在单类故障样本数为1时,采用所提方法建立的模型准确率超过95%;在故障样本稀少且多类缺失时,准确率比仿真数据直接增补方式提升超10%。 相似文献