最优隐层BP神经网络的滚动轴承故障诊断

针对滚动轴承的故障诊断问题,设计了一种最优隐层BP神经网络,借助经验公式确定隐层单元数的取值范围,进行计算平均迭代次数和均方误差来寻找最优隐层单元数。通过MATLAB仿真,结果表明该BP神经网络具有较高的诊断效率和准确度。     

提高动态流量软测量实时性的RBF中心优化算法

针对液压伺服系统动态流量软测量模型中神经网络训练精度和训练速度难以同时提升的问题,引入减聚类(SCM)算法将原训练样本集映射成初始径向基函数(RBF)中心集,并确定基函数宽度;利用敏感性分析算法(SenV)对基函数的中心进行优化,从而减少神经网络隐层节点数目;在根本上为同时提升神经网络训练精度和训练速度提供保障.实验表明,神经网络的隐层节点数可降低至少30%.     

基于BP神经网络的钢丝绳损伤定量识别

利用BP神经网络对钢丝绳信号的特征量和断丝数进行训练,选择标准的3层网络将实验数据进行归一化后变换隐层节点数反复多次训练,确定隐层节点数,将效果比较好的网络作为实际断丝检测时调用的网络,通过VC++语言编程,实现钢丝绳检测的智能化。     

基于代数算法神经网络在机器人逆运动学中的应用

采用多层前向INI神经网络建立机器人逆运动学模型.提出了一种神经网络代数算法来学习网络待求权和自由权,该算法选择很广一类的隐层神经元函数训练网络,将复杂的非线性优化问题转化为简单的代数方程组求解问题,求解速度快;在网络训练之前就可以根据给定的问题确定隐层神经元的个数,可以方便地求得全局最优点,实现样本空间的精确映射,不存在局部极小、收敛速度慢等问题.提出的求解机器人逆运动学的新算法可以得到高精度的解,有仿真结果为证.     

改进代数算法神经网络在机器人逆运动学中的应用

用多层前向INI神经网络建立机器人逆运动学模型.采用一种改进代数算法来学习神经网络待求权和自由权,该算法选.择很广一类的隐层神经元函数训练网络,将复杂的非线性优化问题转化为简单的代数方程组求解问题,求解速度快;在网络训练之前就可以根据给定的问题确定隐层神经元个数,可以方便地求得全局最优点,实现样本空间的精确映射,不存在局部极小、收敛速度慢等问题.提出的求解机器人逆运动学新算法可以得到高精度的解,有仿真结果为证.     

基于径向基神经网络的直线超声电机位置控制

针对直线超声电机的精密位置控制,提出了一种基于径向基神经网络的自适应控制机制。鉴于直线超声电机工作原理,其运行状态必然受到摩擦、强非线性和时变等不确定性因素的干扰,为了对这些不确定性因素进行有效的逼近,采用了径向基神经网络。为了提高控制机制的自适应能力,首先利用来自试验数据的训练样本按正交最小二乘算法确定径向基神经网络的隐层单元的个数和相关参数,再按递推最小二乘法在线调整隐层与输出层之间的权重。试验结果表明,基于径向基神经网络的自适应控制器的性能不仅优于传统的PID控制和误差反向传播神经网络控制,而且具有很好的抗干扰能力。     

基于小波神经网络的模拟电路故障诊断

本文对模拟电路提出了一种基于小波神经网络的故障诊断方法。该法利用小波空间中函数的多分辨率分解思想，构造了一种激励函数为具有紧支撑集的尺度函数和小波函数的小波神经网络。这种小波神经网络隐层节点数的选取有理论根据，解决了传统神经网络隐层节点数难以确定的问题。分别用本文提出的小波神经网络和传统BP网络对实例电路进行故障诊断，结果发现，小波网络比传统BP网络方法不仅学习收敛速度快，而且有效地避免了局部最小值问题。     

基于并行结构的Gabor小波神经网络算法及应用

给出了一种基于并行结构的Gabor小波神经网络算法。根据多CPU系统的并行结构和神经网络本身并行性的特点,设计了用于图象目标识别的Gabor小波神经网络算法,算法的输入层包括Gabor小波尺度、平移和频率调制参数的运算;隐层是在并行CPU中实现神经网络算法及优化;输出层是Gabor小波神经网络的分类结果。对4类飞机图像目标进行了仿真实验,识别率达到98％以上,识别时间为40 ms。     

基于MATLAB的BP神经网络在旋流器模拟设计中的应用

利用MATLAB工具箱中的BP神经网络模型建立了旋流器的三层神经网络,提出了旋流器隐层单元数的确定方法,并通过所选择的函数及所构建的网络结构,对网络学习训练得到了14-20-2的预测性能模型,利用所收集的60组样本数据进行训练的结果表明所采用函数和网络结构具有较高精度,由训练得到的权值和阈值对旋流器性能进行预测的结果显示它能满足工程需要.     

基于改进的RBF神经网络的滚动轴承故障诊断

该文阐述了径向基函数(radial basis function,RBF)神经网络的基本原理和算法,并针对RBF神经网络存在的隐含层的隐层单元数目及中心向量、扩展参数难以确定的问题,利用减聚类算法进行RBF网络的改进,建立应用于滚动轴承故障诊断与识别的RBF神经网络智能识别模型,并通过实验与BP(back propagation)神经网络进行比较分析研究。结果表明,减聚类算法能够有效地确定网络参数,改进的RBF神经网络对预设滚动轴承故障能够准确诊断,并且具有训练速度快的特点。     

基于广义粗糙集与神经网络集成的旋转机械故障诊断研究

故障诊断规则中判断条件的冗余、不完全和不确定性不利于实际应用。采用广义粗糙集理论对旋转机械振动故障诊断的非完备决策系统进行了约简 ,得到了更为简明的最优诊断规则 ;根据约简结果 ,建立了基于神经网络的故障诊断系统 ;网络的训练对比结果表明 ,基于粗糙集理论的约简处理简化了神经网络结构 ,提高了网络的训练效率 ;以诊断实例验证了广义粗糙集理论与神经网络集成进行故障诊断的可行性     

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.     

Deep residual learning-based fault diagnosis method for rotating machinery

Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.     

一维深度子领域适配的不同转速下旋转机械 复合故障诊断

旋转机械复合故障与单一故障样本间相关性高易造成错分类,且旋转机械转速往往不同,进一步加剧了旋转机械复合故障诊断的难度。针对上述问题,提出一维深度子领域适配的不同转速下旋转机械复合故障诊断方法。首先,以旋转机械复合故障的频域信号作为网络的输入,最大程度保留信号特征;其次,搭建领域共享的一维卷积神经网络,对不同转速下旋转机械复合故障的频域信号特征进行学习;然后,添加局部最大均值差异形成子领域适配层,对齐每对子领域分布以避免单一故障和复合故障的特征混合,并通过最小化局部最大均值差异值缩小两域子领域特征分布差异,以减少不同转速所带来的干扰;最后,在子领域适配层后添加softmax分类层,实现对目标数据的故障状态识别。通过不同转速旋转机械复合故障诊断实验证明了所提方法的有效性。     

神经网络结构与算法对变压器故障诊断的影响

BP神经网络算法本质上是基于梯度下降的一种迭代学习算法,存在学习收敛速度慢、收敛精度低、易陷入局部极小、学习率难以选取、隐层数及隐层神经元个数难以确定等缺陷。为了选择出更适宜变压器DGA故障诊断的神经网络结构及算法。本文采用了常用的几种智能算法对变压器故障样本进行了诊断性能对比实验。结果得出Levenberg-Marquardt神经网络算法是收敛速度较快的算法,有动量和自适应的梯度下降法是收敛稳定性较佳的算法;网络最优结构设计过程。为用于变压器DGA故障诊断的神经网络的结构和算法提供了系统化的试验方法。     

Rotating machinery diagnosis using wavelet packets-fractal technology and neural networks

This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The main purpose is to investigate different fault conditions for rotating machinery, such as imbalance, misalignment, base looseness and combination of imbalance and misalignment. In this study, we measured the non-stationary vibration signals induced by these fault conditions. Applying wavelet packets transform to these signals, the fractal dimension of each frequency channel was extracted and the box counting dimension was used to depict the failure characteristics of the fault conditions. The failure modes were then identified by a radial basis function neural network. An experiment was conducted and the results showed that the proposed method can detect and recognize different kinds of fault conditions. Therefore, it is concluded that the combination of wavelet packets-fractal technology and neural networks can provide an effective method to diagnose fault conditions of rotating machinery.     

独立分量分析基网络应用于旋转机械故障特征抽取与分类

提出了一种新颖的、基于独立分量分析(ICA)的多层神经网络,用于旋转机械不同模式(如正常及轴承故障等)的特征抽取,随后利用多层感知器(MLP)实施最终的模式分类。借助独立分量分析方法,隐藏于多通道振动观测中的不变特征得到有效提取,从而建立起稳定的MLP分类器。试验所获得的成功分类结果表明,所建议的新的旋转机械健康状况监测方法具有较大的应用潜力。     

可视化旋转机械故障智能诊断系统设计

介绍了可视化旋转机械故障智能诊断系统设计基本思想、方法和关键技术。讨论了旋转机械故障智能诊断专家知识的获取、可视化专家知识库的建立,及由专家规则、模糊逻辑、神经网络于一体的组合智能推理机。实现了信号采集与分析、特征参数的自动识别与提取、可视化专家知识库和组合智能推理机的集成。编制了整套系统软件,开发了一套故障智能诊断装置,对旋转机械典型故障进行智能诊断,诊断出机组运行中可能存在的故障种类、故障严重程度,故障发生时间及故障治理的专家建议,即可实现故障的检测、故障分离、故障辩识、故障对策等,并具有网络化功能。     

EMD-AR和GRNN算法下的航空液压泵多模态故障诊断分析

针对新一代飞机高综合化、高复杂度和高耦合性导致的传统推理故障诊断策略难以满足现代维修保障需求的问题,开展基于广义回归神经网络(Generalized Regression Neural Network,GRNN)的飞机液压泵智能化故障诊断研究。构建经验模态分解(Empirical Mode Decomposition,EMD)与自回归(Autoregressive,AR)相融合的深度特征提取方法,提升原始信号的隐层故障特征筛选能力;再将增强后的隐层特征与GRNN相结合进行神经网络训练,提升智能诊断模型的识别精度。实验结果表明,EMD-AR-GRNN智能诊断模型能快速、准确地诊断出液压泵各故障模态,对保障设备的安全运行,提升系统可靠性具有重要的意义。     

SEQUENTIAL DIAGNOSIS FOR A CENTRIFUGAL PUMP BASED ON FUZZY NEURAL NETWORK

A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.