共查询到19条相似文献,搜索用时 343 毫秒
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基于粗糙集-BP神经网络的机车滚动轴承故障诊断 总被引:1,自引:0,他引:1
论文提出了一种基于粗糙集理论与BP神经网络相结合的机车滚动轴承故障诊断方法.首先对原始故障诊断样本的连续属性进行离散化处理,然后利用粗糙集理论,对条件属性进行约简,删除冗余信息,最后将约简的最小属性集作为BP神经网络的输入,并设计BP神经网络对滚动轴承进行诊断.仿真结果表明粗糙集-BP模型不仅简化神经网络结构,而且提高了收敛速度和故障诊断正确率. 相似文献
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在汽车防抱死制动系统(ABS)中,压力调节器和轮速传感器起着非常重要的作用,为了进一步完善汽车防抱死制动系统的制动性能,文中提出一种基于概率神经网络(PNN)的压力调节器和轮速传感器的故障诊断方法。基于高附着均一路面,起车时制动及单一的压力调节器或者轮速传感器故障的试验数据,分别建立了基于概率神经网络的压力调节器故障诊断模型和轮速传感器故障诊断模型,并与BP神经网络进行了比较。仿真结果表明,利用相同的训练样本集对概率神经网络和BP神经网络进行训练时,基于概率神经网络的压力调节器故障诊断模型和轮速传感器故障诊断模型在训练时间和诊断精度上明显优于BP神经网络,并且利用测试样本对建好的压力调节器故障模型和轮速传感器故障模型进行检测时,无论测试样本的顺序发生什么变化,基于概率神经网络的故障模型都能够准确的进行故障识别。 相似文献
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针对概率神经网络(PNN)模型强大的非线性分类能力,PNN能够很好地对变压器故障进行分类;文章通过对PNN神经网络的结构和原理的分析,应用PNN概率神经网络方法对变压器故障进行诊断;通过实例仿真表明,PNN网络的训练时间比BP网络少,比之预测准确度也要高,而且还具有高度的泛化能力,这使得PNN网络可以有效地运用到变压器故障诊断中,具有一定的可操作性。 相似文献
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容差模拟电路故障的多样性使得神经网络训练样本数量增加,BP网络结构趋于复杂,训练速度降低.针对反向传播神经网络(BPNN)学习收敛速度慢、易陷入局部极小值等问题,提出了基于概率神经网络(PNN)的容差模拟电路故障诊断方法,与传统的BP网络模型相比,该方法具有训练时间短且不易收敛到局部最小的优点.仿真实验表明:诊断过程快速,结果准确而且对软故障也有较高的识别能力. 相似文献
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主成分分析法与概率神经网络在模拟电路故障诊断中的应用 总被引:6,自引:2,他引:4
模拟电路故障的多样性使得神经网络训练样本数量增加,BP网络结构趋于复杂,训练速度降低;针对反向传播神经网络(BPNN)学习收敛速度慢、易陷入局部极小值等问题,提出了基于主成分分析(PCA)与概率神经网络(PNN)相结合的模拟电路故障诊断方法;通过主成分分析法(Principal Component Analysis)提取特征数据进行降维处理,再结合概率神经网络(Probabilistic Neural Networks)对电路故障进行分类;实例说明采用PCA和PNN结合对故障数据处理,可以大大的提高故障诊断分类的准确性。 相似文献
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针对煤炭输送机减速器出现的故障,提出在多信息融合模型的特征层使用概率神经网络(PNN)对其进行故障诊断的研究。使用PNN、BP对减速器齿轮故障进行仿真实验并比较,结果表明PNN在时间、准确度方面优于BP网络。 相似文献
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制粉系统是火电厂的主要设备,其安全稳定运行对发电企业的经济生产具有十分重要的意义;针对制粉系统的运行特性和故障分析,提出了基于极化因子神经网络的火电厂制粉系统故障诊断方法,该方法将故障征兆相应的过程变量作为输入,将制粉系统故障类型作为输出,通过训练神经网络建立其系统故障诊断模型,其中训练过程中采用极化因子来自动调整神经网络的收敛速度,从而在满足误差目标的前提下,防止其陷入局部极小;选取实际火电厂制粉系统3个典型故障及其相对应的9个故障征兆参数进行了实验;结果表明,该方法具有良好的收敛性,完全可以满足火电厂制粉系统现场故障诊断的要求. 相似文献
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当前道岔故障诊断系统大多采用BP神经网络,但由于BP神经网络结构特点,在训练样本大且诊断系统精度要求比较高时,网络常常会呈现出以下不足:不收敛且容易陷入局部最优、常用的数据挖掘方法如小波分析等对数据的利用度不高、从时域或频域角度分析时不够全面和采用数据降维使用的LLE方法会丢失部分有用数据等.采用GMM聚类方法对兰州车... 相似文献
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This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis. 相似文献
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该文在应用BP网络诊断电厂制粉系统故障的研究中,采用一种改进的学习算法,即对BP网络权值的优化方法上进行了改进,并经制粉系统故障诊断的仿真试验证明,这种改进的BP算法能有效地解决网络学习时易陷入局部极小值的问题,提高网络的学习速度和诊断精度。 相似文献
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Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data. 相似文献
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This paper presents a fault diagnosis system for an automotive air-conditioner blower based on a noise emission signal using a self-adaptive data analysis technique. The proposed diagnosis system consists of feature extraction using the empirical mode decomposition (EMD) method and fault classification using the artificial neural network technique. The EMD method has been developed quite recently to adaptively decompose the non-stationary and non-linear signals. It sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance classification performance. These energy features of various fault conditions are used as inputs to train the artificial neural network. In the fault classification, the probabilistic neural network (PNN) is used to verify the performance of the proposed system and compare with the traditional technique, back-propagation neural network (BPNN). The experimental results indicated the proposed technique performed well for quickly and accurately estimating fault conditions. 相似文献
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齿轮振动信号具有非平稳性和非线性的特点。为了准确提取其故障特征并进行故障诊断,提出一种基于双树复小波变换(DTCWT)-最大熵谱估计(MESE)和惯性权重线性递减粒子群优化(LDWPSO)算法-参数优化概率神经网络(PNN)的齿轮故障诊断方法。首先,利用DTCWT把状态已知的齿轮振动信号分解为不同频带的模态分量。其次,采用MESE得到每个分量的最小偏差频谱估计,计算出不同频段的能量熵作为故障特征矩阵。然后利用LDWPSO算法寻找出最优神经网络参数——平滑因子。最后,将故障特征矩阵输入优化后的PNN模型,建立起故障特征和齿轮运行状况之间的数值化映射关系,进而完成齿轮故障诊断模型。经试验数据分析表明,采用提出的DTCWT处理齿轮的振动信号,并引入MESE处理关键分量,可以提取稳定的信号特征并降低噪声干扰。另外,相比于传统的PNN,基于改进的PNN的齿轮故障状态的数值化判别具有更高的诊断精度和稳定性。 相似文献
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Intelligent diagnosis method for a centrifugal pump using features of vibration signals 总被引:1,自引:0,他引:1
In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because
the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because
definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis
method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived
using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used
to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency
regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics
of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using
the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition
diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically.
The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical
examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method. 相似文献
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An expert system for fault diagnosis in internal combustion engines using probability neural network
Jian-Da Wu Peng-Hsin Chiang Yo-Wei Chang Yao-jung Shiao 《Expert systems with applications》2008,34(4):2704-2713
An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system. 相似文献