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蚁群优化BP神经网络的电机故障诊断设计与实现 总被引:2,自引:0,他引:2
针对传统的故障诊断方法采用专家知识推理方法在获取知识方面的困难,提出了一种采用蚁群优化算法和BP神经网络的自适应电机故障诊断系统。使用BP神经网络对样本数据进行训练,可以建立故障征兆到故障之间的对应关系,从而可以克服专家系统的不足,同时,由于传统的BP算法采用梯度下降算法,具有收敛速度慢和容易陷入局部最优解的问题,且BP神经网络的网络结构和初始参数在确定时往往依靠经验,从而限制了其在故障诊断领域的进一步发展。蚁群算法是一种启发式的模拟进化优化算法,具有正反馈及其分布式计算等特点,因此,将蚁群算法应用于BP神经网络的结构和参数进行优化,然后采用优化后的BP神经网络进行故障诊断,电机诊断实例证明文中方法较BP神经网络和遗传算法优化的BP神经网络具有更高的诊断精度和训练效率。 相似文献
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为了提高塔式起重机的故障检测效率,将BP神经网络故障诊断应用于起重机电气设备的故障诊断中。该系统一方面可根据新的故障样本自动学习、训练和更新故障知识,形成新的故障诊断规则并添加到专家系统知识库中。另一方面,直接调用神经网络诊断模块实现故障诊断,根据故障现象诊断故障原因,为维修提供指导。 相似文献
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提出了一种可以减小网络规模的故障表示方法,并将Alopex算法引入神经网络模型的训练过程,将人工神经网络与规则推理相结合,建立了旋转机械故障诊断的神经网络专家系统。该系统充分利用了神经网络与规则推理的优点,采用正反向混合推理方式调用知识库中的各种知识进行诊断。采用二进制数码表示机械的各种故障,基于Alopex算法训练神经网络。建立的专家系统克服了基于规则专家系统的自学习困难问题和基于神经网络诊断系统的系统控制能力弱的缺点,具有较强的诊断能力。 相似文献
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概率神经网络(Probabilistic Neural Network)的结构简单、训练简洁,具有非常强大的非线性能力,具有较强的容错能力,充分利用故障先验知识,将诊断错误带来的损失降到最小。根据PNN理论,在MATLAB中建立一个简化的变压器的故障诊断系统,根据收集到的变压器故障实例数据进行了分析与仿真,仿真结果表明,概率神经网络在变压器故障诊断的问题中是可行的,应用前景非常广泛。 相似文献
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在ACO算法原理及框架的基础之上,将蚁群优化算法引入到神经网络的训练中来,提出了ACO训练神经网络的基本原理和方法步骤,并应用于发动机齿轮箱故障的故障诊断。本文采取经典的“频域”分析方法对齿轮箱进行故障诊断,并建立了基于蚁群神经网络的齿轮箱故障诊断模型。结果表明,用ACO算法训练的神经网络具有较高的故障诊断精度,可以有效地诊断齿轮箱中的故障,提高了诊断的效率和质量。 相似文献
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基于粒子群优化径向基神经网络在模拟电路故障诊断中的应用 总被引:1,自引:0,他引:1
为检测和诊断模拟电路中的故障,提出粒子群算法优化RBF神经网络的故障诊断方法,即把通过特征提取获得的模拟电路故障特征量作为神经网络的输入,然后利用训练好的粒子群优化后的RBF神经网络进行故障诊断. 结果表明,该方法具有良好的分类效果,能够提高诊断精确度,对于模拟电路的故障是一种有效的诊断方法. 相似文献
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基于SOM神经网络的柴油机故障诊断 总被引:2,自引:0,他引:2
利用神经网络的非线性映射及其高度的自组织和自学习能力,将SOM网络应用于柴油机的故障诊断.利用夹持式传感器获得柴油机喷射系统的燃油压力波形,对波形进行时域分析和特征提取.根据所取得故障信息及其对应的故障类型来构造网络结构,用单一故障样本对网络进行训练,根据输出神经元在输出层的位置对故障进行判断.通过仿真实验验证SOM神经网络在柴油机故障诊断的正确性.经实例分析证明,该方法可对故障进行有效诊断. 相似文献
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数控(CNC)系统故障是影响CNC机床加工精度与效率的重要因素之一,因此,怎样提高故障诊断效率一直是该领域的重点研究内容。本文介绍了反向传播(BP)神经网络在CNC系统故障诊断中的应用,给出了诊断算例。结果表明,BP网络在数控系统故障诊断方面有着重要的应用价值,为数控机床故障诊断开辟了新途径 相似文献
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BP神经网络在滚动轴承早期故障诊断中的应用 总被引:4,自引:1,他引:4
滚动轴承是旋转机械中应用普扁而又易损的元件之一,其故障在机械故障中占有很大的比例.因此,轴承故障诊断、特别是早期诊断很受重视.本文将神经网络应用于轴承早期故障诊断,简要说明了BP神经网络的基本原理、算法及特点,介绍了实验数据的分析过程和参数选择原则.实验结果表明,选择适当的网络结构进行训练、学习和检验,可以把良好轴承、内环缺陷轴承、外可缺陷轴承、滚子缺陷轴承及具有三种综合缺陷的轴承区分开来,并能初步估计出缺陷的大小. 相似文献
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提出将人工智能技术应用于数控系统的策略和模型.构造出数控机床故障诊断专家系统模型,实现数控机床故障诊断智能化;提出利用知识库中的管理子系统管理各子知识库;采用正反推理混合方式以及单元推理、行为推理和故障树推理的方式,提高了故障诊断效率. 相似文献
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《Measurement》2016
This paper proposes a fault diagnosis method for star-connected auto-transformer based 24-pulse rectifier unit (ATRU) by integrating artificial neural networks (ANN) with wavelet packet decomposition (WPD) and principal component analysis (PCA). The WPD is employed to extract the features of different fault waveforms of the output voltage of the rectifier. PCA is adopted to reduce the dimensionality of the extracted feature vectors, which leads to fast computation of the algorithm. Back Propagation (BP) neural network is adopted to classify the fault types and determine the fault location according to the extracted features. These faults are simulated in real-time simulation platform and the obtained data are then analyzed with MATLAB toolbox, and finally verified with digital signal processor. Compared with other diagnosis methods, the proposed method shows better performance and faster computing speed. 相似文献
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针对军用航空发动机的状态监测与故障诊断问题,研究了航空发动机的诊断知识动态获取模型及柔性诊断技术。建立了可扩展诊断样本库,实现样本库中故障征兆和故障模式的动态增减,以增加系统的柔性和可扩展性;运用粗糙集理论对样本集进行处理,实现冗余属性的约简、冗余样本的去除及样本冲突的消除;用神经网络通过对处理后的样本集进行学习以动态获取知识,将实际诊断样本输入到训练好的神经网络模型即可得到诊断结果。整个诊断过程具有充分的可扩展性和柔性,当有新样本加入时,按上述步骤进行处理即可实现诊断知识的动态获取和诊断。算例表明了方法的正确性和有效性。 相似文献
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《Measurement》2016
In power transformer fault diagnosis, dissolved gas analysis (DGA) has been widely used to identify the type of the fault. The common methods of DGA are IEC 60599 method, Doenenberg’s ratio method and Roger’s ratio method. The accuracy of the DGA diagnosis will determine the cost, duration and workload of the maintenance since it can influence the error in the maintenance. Although DGA methods have been used widely, sometimes they still yield incorrect diagnosis results. Thus, many works on transformer fault diagnosis have been proposed previously, which include artificial intelligence methods, to improve the accuracy of transformer fault diagnosis. However, the accuracy of the previously reported works is believed to have rooms for improvement. Therefore, in this work, hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC)-artificial neural network (ANN) was proposed for transformer fault diagnosis based on dissolved gas data. This is due to these two methods have never been proposed for transformer fault diagnosis in the past. The performance of the ANN was optimised through the proposed MEPSO-TVAC. The superiority of the proposed method was demonstrated through comparison with the existing DGA methods, unoptimised ANN and previously reported methods in literatures. The comparison shows that the proposed hybrid MEPSO-TVAC-ANN obtained the highest accuracy among all methods, which can then be used for power transformer fault diagnosis. 相似文献
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Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network 总被引:1,自引:0,他引:1
Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics
and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time
interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum
and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method
combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox
is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals
are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting.
In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental
data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with
the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and
diagnosis of the gear condition. 相似文献
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This paper mainly presents a distributed monitoring and diagnosis system for the hydraulic system of construction machinery based on the controller area net (CAN) field bus. The hardware of the distributed condition monitoring and fault diagnosis system is designed. Its structure including the sensors, distributed data acquisition units, central signal processing unit, and CAN field bus is introduced. The software is also programmed. The general software design and its realization are studied in detail. The experiments and applications indicate that the distributed condition monitoring and fault diagnosis system can effectively realize its function of real-time online condition monitoring and fault diagnosis for the hydraulic system of construction machinery. 相似文献