共查询到20条相似文献,搜索用时 125 毫秒
1.
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
2.
3.
由于柴油发动机的结构复杂给故障诊断的研究带来了很多不便,本文基于粗糙集理论对柴油机故障诊断的决策进行属性约简,然后使用支持向量机对故障属性进行分类,从而使柴油发动机的故障诊断更加切实可靠。因此本文对粗糙集和支持向量机相整合的柴油机故障诊断算法进行浅析。 相似文献
4.
5.
6.
应用变精度粗糙集获取柴油机故障有效监测点 总被引:1,自引:0,他引:1
刘军 《振动、测试与诊断》2009,29(1):27-30
提出基于变精度粗糙集(Variable Precision Rough Set,简称VPRS)理论获取柴油机故障有效监测点的方法.通过变精度粗糙集在不同精度时分类近似质量的计算,得到柴油机在不同采集点的分类近似质量,进一步计算出各采集点条件属性与决策属性的近似依赖值,得出在故障监测中最敏感的状态信息监测点.通过变精度粗糙集理论对采集点数据的处理,找出故障诊断系统中最有效工作状态监测点.有效信息的采集,可以最大限度地减少噪声对故障诊断的影响,提高故障诊断系统的工作效率和准确率. 相似文献
7.
8.
对滚动轴承几种常见点蚀故障的振动信号特征值进行分析,使用粗糙集基于熵的离散化算法进行属性离散化,并采用基于属性重要度的启发式约简算法进行属性约简,然后选用径向基核函数的支持向量机将经过属性约简的特征向量输入支持向量机训练,建立支持向量机模型并进行故障识别与诊断。实验分析结果表明,应用粗糙集和支持向量机相结合的混合智能诊断方法,将粗糙集作为支持向量机的前置系统对数据进行预处理,利用粗糙集可以减少信息表达的属性数量和故障诊断决策系统的规则数,使支持向量机输入端数据量大大减少,提高系统的处理速度,对于滚动轴承振动信号的故障模式识别可以获得良好的效果。从而验证了粗糙集理论对滚动轴承故障诊断的有效性以及其应用价值。 相似文献
9.
10.
11.
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
12.
13.
利用粗集的约简功能消除压缩机故障样本数据中冗余信息,将粗集与神经网络相结合,构建了一个基于粗集一神经网络的智能混合压缩机故障诊断系统,实现了粗集对神经网络在压缩机故障诊断中的优化。 相似文献
14.
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
15.
基于粗糙集-神经网络系统的轴承故障诊断 总被引:1,自引:0,他引:1
针对神经网络在故障诊断中存在着输入属性维数多和数据量庞大的缺点,利用粗糙集理论对原始数据进行约简,并剔除其中不必要的属性,构建了优化的粗糙集-神经网络系统.实例分析表明,使用该系统能够减少故障诊断的时间. 相似文献
16.
MMAS与粗糙集在轴承复合故障诊断中的应用 总被引:4,自引:0,他引:4
在分析振动加速度信号的基础上,提出了新的粗糙集属性约简算法,并应用于轴承复合故障诊断.将最大一最小蚂蚁系统(max-min ant system,简称MMAS)引入条件属性约简中,以最坏Fisher准则函数作为启发式信息以提高搜索效率,综合考虑分类正确率和条件属性个数两方面因素,利用粗糙集理论约简故障诊断决策表,有效地提高了轴承故障诊断的效率. 相似文献
17.
18.
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
19.
20.
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