共查询到19条相似文献,搜索用时 875 毫秒
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利用模糊推理算法,提出了故障代码建立的方法,运用ART1神经网络进行故障诊断,并应用到发动机数据故障诊断中。该算法引入了放宽尺度原则和状态保持优化算法,增加了阈值设定的灵活性,降低算法的实现难度,提高了故障代码的准确性;并且可以在不影响先前诊断的基础上,进行故障诊断。实验结果证明,该方法精确度高,噪声抑制力强.而且诊断准确,大大降低了虚警率。 相似文献
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为精确诊断飞机液压系统故障,提出了一种基于小波包特征熵的神经网络故障诊断新方法。对采集到的飞机液压系统压力信号进行小波包分解,提取小波包特征熵,然后构造信号的小波包特征熵向量,并以此向量作为故障样本,利用ART1神经网络进行训练,实现智能化故障诊断。试验结果表明,训练成功的ART1网络能够很好地诊断出飞机液压系统是否发生故障,为飞机液压系统故障诊断开辟了新的途径。 相似文献
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在分析电气负载管理中心故障特点的基础上,利用神经网络权值和阈值能够随实际的排故结果不断更新及正向推理速度较快的特性,提出了基于BP神经网络的负载管理中心故障诊断方案,并确立了故障诊断BP网络模型.借助于MATLAB的神经网络工具箱,采用两种改进的训练算法对网络进行训练,得到了用于诊断的BP神经网络模型,为检验该模型故障诊断的准确性,采用大量的数据样本进行了仿真.结果表明:基于神经网络的诊断方法故障识别率高、快速有效,具有良好的实用价值. 相似文献
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针对目前使用神经网络诊断故障时出现的输入向量选择困难、网络结构复杂、对并发故障诊断效果不好等问题,提出了基于邻域粗糙集和并行神经网络的故障诊断方法;先利用邻域粗糙集对初始征兆进行约简,留下有价值的征兆作为神经网络的输入向量,然后针对每种故障类型设计一个神经网络;用多个训练好的神经网络来并行诊断故障,综合每个神经网络的结果给出最终的诊断结论;用转子实验台的实验数据对这种故障诊断方法进行验证,结果显示该方法能优化神经网络结构,且神经网络具有训练速度快、诊断正确率高的特点。 相似文献
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神经网络和模糊系统在故障诊断中的应用 总被引:5,自引:0,他引:5
本文提出了一种神经网络和模糊系统相结合的分级式故障诊断方法。神经网络通过对部分测量数据的处理,实现系统的回路级故障诊断,输出各回路故障出现的可信度。模糊系统通过对神经网络得到的初步诊断结果和其他测量值的处理,实现系统的元件级故障诊断,并对最终诊断结果作出解释。该方法融合了神经网络自适应学习能力强和模糊系统知识表达明确的优点,简化了神经网络学习数据获取及模糊推理规则建立的过程。通过对热硝酸冷却系统故障诊断的仿真,证明了该故障诊断方法的有效性。 相似文献
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研究故障诊断问题;针对传统Petri网难以精确地描述故障现象和故障原因之间的复杂关系,基于模糊逻辑BP神经网络和传统Petri网模型结合,提出了一种新的自适应的加权模糊神经网络Petri网模型故障检测方法;该方法首先采用改进的BP神经网络算法对模型的权值进行训练,然后采用构造的自适应模糊Petri网模型对故障进行诊断;在柔性制造系统实例中进行了故障诊断,实验结果表明,该方法具有很强的故障推理能力以及自适应能力,能有效地对故障进行诊断,具有一定的实际应用价值。 相似文献
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This article presents a model‐based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, the estimated parameters are transferred to the fault classifier by the adaptive resonance theory 2 neural network (ART2 NN) with uneven vigilance parameters for fault isolation. The simulation results show the effectiveness of the proposed ART2 NN–based fault diagnosis method. © 2003 Wiley Periodicals, Inc. 相似文献
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《Applied Soft Computing》2008,8(1):740-748
The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. An early detection of faults may help to avoid product deterioration, performance degradation, major damage to the machinery itself and damage to human health or even loss of lives. The centrifugal pumping rotary system is considered for this research. This paper presents the development of artificial neural network-based model for the fault detection of centrifugal pumping system. The fault detection model is developed by using two different artificial neural network approaches, namely feed forward network with back propagation algorithm and binary adaptive resonance network (ART1). The training and testing data required are developed for the neural network model that were generated at different operating conditions, including fault condition of the system by real-time simulation through experimental model. The performance of the developed back propagation and ART1 model were tested for a total of seven categories of faults in the centrifugal pumping system. The results are compared and the conclusions are presented. 相似文献
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基于ART2网络聚类分析的数据融合算法研究 总被引:1,自引:1,他引:0
人工神经网络为数据融合提供了新的理论方法和技术手段,在数据融合的各个方面具有广泛的应用前景。自适应共振理论(ART)是一种无监督神经网络,能够实现对输入的任何模拟信号的自动识别和分类。据此提出了一种以ART2网络聚类分析为核心的数据融合算法,探讨了ART2网络用于特征层数据融合实现模式识别/分类的机理,最后给出该算法在一例模式识别/分类中的应用-实现对工业控制系统中设备运行状态的实时监测和故障诊断,验证了该算法的有效性和可行性。 相似文献
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在对汽车的故障诊断过程中,基于SAE J1939协议的CAN通信的ECU提供的发动机性能检测参数和整车网络通信数据,实现整车网络中多个ECU数据的共享;J1939协议同时也支持故障的诊断,通过数据转换模块将接收的数据转换成串行数据(包含CAN的ID地址),诊断工具(手持终端)可以读取当前故障码DM1或清除当前故障码DM11.本文提出了一种车辆故障诊断的研究策略,同时提出了一种基于JAVA语言的报文的解析方法,能够有效实时地实现对汽车发动机的故障检测. 相似文献
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Anna Wang Wenjing Yuan Junfang Liu Zhiguo Yu Hua Li 《Computers & Mathematics with Applications》2009,57(11-12):1908
Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method’s efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with simulations and the best method is validated with experimental data. 相似文献
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基于小波一神经网络技术的电机故障模式识别与诊断 总被引:5,自引:0,他引:5
针对电机震动信号的频谱特点,提出基于小波神经网络技术的电机故障模式识别与诊断的新方法。利用小波包可进行多维多分辨率的特性,对电机振动信号进行分解与重构,获得震动信号的突变信息,实现电机状态的特征提取。对提取出的特征,用ART2神经网络进行状态分类,进而诊断故障类型,并利用这种方法进行仿真试验,通过对仿真结果的分析证实这种诊断的可行性。 相似文献
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Artificial neural networks (ANNs) are suitable for fault detection and identification (FDI) applications because of their pattern recognition abilities. In this study, an unsupervised ANN based on Adaptive Resonance Theory (ART) is tested for FDI on an automated O-ring assembly machine testbed, and its performance and practicality are compared to a conventional rule-based method. Three greyscale sensors and two redundant limit switches are used as cost-effective sensors to monitor the machine’s assembly process. Sensor data are collected while the machine is operated under normal condition, as well as 10 fault conditions. Features are selected from the raw sensor data, and data sets are created for training and testing the ANN. The performance of the ANN for detecting and identifying known, unknown and multiple faults is evaluated; the performance is compared to a conventional rule-based method using the same data sets. Results show that the ART ANN is able to achieve excellent fault detection performance with minimal modeling requirements; however, the performance depends on careful tuning of its vigilance parameter. Although the rule-based system requires more effort to set up, it is judged to be more useful when unknown or multiple faults are present. The ART network creates new outputs for unknown and multiple fault conditions, but it does not give any more information as to what the new fault is. By contrast, the rule-based method is able to generate symptoms that clearly identify the unknown and multiple fault conditions. Thus, the rule-based method is judged to be the most feasible method for FDI applications. 相似文献
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轴承是机械设备主要零部件之一,也是机械设备主要故障零部件之一。轴承故障问题为机械设备的重点,机械设备的使用受到故障轴承的直接影响。针对传统的卷积神经网络算法轴承故障诊断效率低下问题,本文提出了一种基于信号特征提取和卷积神经网络的优化方法。首先对原始数据信号进行时域和频域的信号特征提取,获得有效的故障特征值。之后,使用卷积神经网络对提取的特征值进行故障诊断,完成故障分类。本文使用美国凯斯西储大学的滚动轴承振动加速度信号作为数据集,对提出的方法进行验证,得到的故障诊断平均准确率为74.37%,准确率的方差为0.0001;传统的卷积神经网络算法故障诊断平均准确率为65.6%;准确率的方差为0.0019。实验结果表明,相比传统的卷积神经网络,提出的方法对轴承故障诊断的准确率有显著的提高,并且该方法的稳定性更佳,计算时间更少,综合性能更佳。 相似文献