压电柔性梁裂缝损伤识别实验

针对压电柔性悬臂梁裂缝损伤检测与损伤程度识别问题,采用小波包分析和小波神经网络相结合的方法进行裂缝深度识别实验研究.利用小波包频带能量谱构造柔性悬臂梁裂缝损伤指标,即能量比相对变化量的H2范数,并建立压电柔性梁裂缝损伤实验装置.激励柔性梁的振动,记录两路压电传感器采集的振动信号,进行小波包分解并计算损伤指标.将这些损伤指标进行组合,作为小波神经网络的输入特征参数,进行裂缝深度即损伤程度的识别.实验结果表明:能量比相对变化量的H2范数对柔性梁的裂缝损伤敏感,对测试噪声不敏感;采用的小波神经网络可以精确识别柔性梁的裂缝深度.     

基于模态应变能比与神经网络的复合材料结构损伤辨识

从结构动力学特性入手，以模态应变能比作为表征结构损伤的标识量，对含损伤的复合材料机翼结构进行损伤辨识仿真，通过神经网络建立起损伤标识量和损伤状态之间的映射模型。仿真结果表明，模态应变能比对结构损伤位置和损伤程度都比较敏感，是一种有效的损伤标识量。神经网络可准确地识别出结构的损伤位置和损伤程度，应用于损伤识别是有效的。     

基于模态应变能比与神经网络的复合材料结构损伤辩识

从结构动力学特性入手,以模态应变能比作为表征结构损伤的标识量,对含损伤的复合材料机翼结构进行损伤辨识仿真,通过神经网络建立起损伤标识量和损伤状态之间的映射模型.仿真结果表明,模态应变能比对结构损伤位置和损伤程度都比较敏感,是一种有效的损伤标识量.神经网络可准确地识别出结构的损伤位置和损伤程度,应用于损伤识别是有效的.     

基于光纤光栅传感器的复合材料损伤识别系统

利用光纤布拉格光栅(FBG)构建了传感器网络;结合小波分解与重构算法、频谱分析和支持向量多分类机算法研究了碳纤维复合材料板损伤的模式识别算法。首先,对带有不同损伤模式的复合材料结构进行冲击试验,探索损伤模式与信号特征之间的关系。然后,对信号进行小波分解与重构去除基线干扰;采用傅里叶变换频谱分析提取信号幅频特性,构建了复合材料结构损伤模式识别方法。最后,将提取的信号幅频特性作输入,复合材料结构损伤模式作输出,利用支持向量多分类机,实现了复合材料结构损伤模式识别。在500mm×500mm×2mm的碳纤维复合材料板中心,选定200mm×200mm的实验区域,对30组测试样本进行了损伤模式识别。实验结果表明:29组损伤模式得到了准确识别,正确率为96.7%。研究结果为碳纤维复合材料板的损伤模式识别提供了一种可靠的方法。     

基于向量自回归模型的损伤识别方法

基于向量自回归(vector auto-regression,简称VAR)模型,提出了一种能同时进行损伤定位和程度识别的时间序列方法。首先,利用测试的加速度响应时程信号建立VAR模型,提取模型系数的对角线元素作为损伤敏感向量,并采用该向量的马氏距离作为损伤特征值;然后,应用统计模式识别手段,通过受试者工作特征曲线下的面积指标来判别损伤是否出现及其部位,并通过Bhattacharyya距离来度量损伤程度。数值模拟和实验室框架模型实验表明,该算法能成功识别损伤部位和损伤程度的相对大小,且具有较好的抗噪性能,为结构长期在线损伤识别提供了一种有效手段。     

一种基于模型修改的结构多点损伤识别方法

可用于结构损伤识别的方法很多。一般来讲正向方法直接利用结构模态参数的变化，逆向方法则利用模态参数变化反演结构物理参数变化，还有些方法利用了神经网络和模式识别技术。文中利用模型修改的思想，通过逆向方法计算结构单元刚度变化系数来对结构的多点损伤进行识别。以一个七自由度弹簧阻尼质量系统作为研究对象，用数值模拟方法及特征系统实现算法计算系统的模态参数，并用这些模态参数验证所提出方法的可行性，结果表明该方法对多点损伤的识别是简单而可行的。     

基于LMD多尺度熵和概率神经网络的滚动轴承故障诊断方法

研究了一种基于LMD多尺度熵和概率神经网络的滚动轴承故障诊断方法。该方法将故障信号自适应地分解为若干乘积函数分量,然后将各分量的多尺度熵作为故障特征向量输入概率神经网络进行模式识别,实现了对损伤位置和损伤程度的诊断。将该方法与基于LMD时域统计量和神经网络的滚动轴承故障诊断方法进行了对比。实验结果表明,基于LMD多尺度熵和概率神经网络的方法能对滚动轴承故障进行有效的识别与诊断。     

模糊神经网络法及其在缺陷模式识别中的应用

针对冷轧带钢表面缺陷图像模式识别中出现的问题,引入模糊模式识别和反向传播神经网络识别方法.在研究比较两种识别方法的基础上,利用模糊模式识别在剔除噪音数据和反向传播神经网络在模型拟合和非线性识别上的优势,提出一种新的模糊神经网络方法,并详细讨论了算法的结构特点及其实现方法.对五种出现频率较高的典型缺陷图像进行计算机实验研究,结果表明,该方法能对缺陷图像进行有效的识别,具有良好的性能.     

框架结构非线性损伤的主成分分析识别方法研究

针对框架结构非线性损伤识别问题,提出一种基于主成分分析的损伤识别方法。利用主成分分析数据压缩和特征提取的特性,首先对结构基准工况响应信号进行处理,提取特征成分,得到主成分模型,然后将结构未知工况响应数据向主成分模型投影,通过构造损伤指标实现对结构非线性损伤的识别。以四层钢框架结构碰撞模型为实验对象,通过螺栓和钢柱构造碰撞非线性损伤源,实验结果表明该方法可有效识别结构的非线性损伤。     

基于柔度矩阵和神经网络的结构损伤识别法

提出一种分步识别结构损伤的方法。首先利用测量模态参数建立结构柔度矩阵来确定结构损伤的大体位置，然后应用神经网络技术和结构的加速度响应对确定的损伤范围进行参数识别，根据识别的刚度值判别结构的损伤程度。通过一个8自由度结构的仿真计算表明，该方法稳定性好，计算精度高，对噪声具有很高的鲁棒性，在10％噪声情况下，应用神经网络技术能较精确地得到结构的损伤程度，显示了该方法对大型复杂结构进行损伤诊断的潜力。     

Application of a flexible structure artificial neural network on a servo-hydraulic rotary actuator

In this article the results of the application of a flexible structure artificial neural network for controlling the angular velocity of a servo-hydraulic rotary actuator are discussed. A mathematical model for the system is derived, and a flexible artificial neural network (ANN)-based controller with the feedback error learning method as a learning algorithm is applied to the system. The neural network-based controller has a feed-forward structure and three layers. The flexible bipolar sigmoid function was used as the activation function of the network. The simulation and experimental results show good performance of the developed method in learning the inverse dynamic of the system and controlling the angular velocity of the rotary hydro motor. The advantages of the developed method for servo-hydraulic actuators over other traditional approaches are discussed.     

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors.     

航空发动机诊断知识的动态获取与柔性诊断技术

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。     

Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector

Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%.     

Design and optimization of machining fixture layout using ANN and DOE

In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout.     

A flexible artificial neural network–fuzzy simulation algorithm for scheduling a flow shop with multiple processors

One of the most popular approaches for scheduling manufacturing systems is dispatching rules. Different types of dispatching rules exist, but none of them is known to be globally the best. A flexible artificial neural network–fuzzy simulation (FANN–FS) algorithm is presented in this study for solving the multiattribute combinatorial dispatching (MACD) decision problem. Artificial neural networks (ANNs) are one of the commonly used metaheuristics and are a proven tool for solving complex optimization problems. Hence, multilayered neural network metamodels and a fuzzy simulation using the α-cuts method were trained to provide a complex MACD problem. Fuzzy simulation is used to solve complex optimization problems to deal with imprecision and uncertainty. The proposed flexible algorithm is capable of modeling nonlinear, stochastic, and uncertain problems. It uses ANN simulation for crisp input data and fuzzy simulation for imprecise and uncertain input data. The solution quality is illustrated by two case studies from a multilayer ceramic capacitor manufacturing plant. The manufacturing lead times produced by the FANN–FS model turned out to be superior to conventional simulation models. This is the first study that introduces an intelligent and flexible approach for handling imprecision and nonlinearity of scheduling problems in flow shops with multiple processors.     

Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model

In the present trend of technological development, micro-machining is gaining popularity in the miniaturization of industrial products. In this work, a hybrid process of micro-wire electrical discharge grinding and micro-electrical discharge machining (EDM) is used in order to minimize inaccuracies due to clamping and damage during transfer of electrodes. The adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP)-based artificial neural network (ANN) models have been developed for the prediction of multiple quality responses in micro-EDM operations. Feed rate, capacitance, gap voltage, and threshold values were taken as the input parameters and metal removal rate, surface roughness and tool wear ratio as the output parameters. The results obtained from the ANFIS and the BP-based ANN models were compared with observed values. It is found that the predicted values of the responses are in good agreement with the experimental values and it is also observed that the ANFIS model outperforms BP-based ANN.     

气固两相流中基于神经网络的固相质量流量检测方法的研究

用神经网络的自学习算法对采样数据进行辨识得出固相质量流量的黑箱模型，实现固相质量流量的在线测量。实验结果，模型最大测量误差为１０％     

小波分析和神经网络在超声波检测中的应用

小波分析和人工神经网络的引入,让检测得到了从定性到定量的提高。运用小波分析从超声波检测装置提取的信号,得到的特征值可作为神经网络的输人参数,用于训练和识别,实践表明,通过这种智能诊断系统可以得到令人满意的定量诊断结果。