基于小波分析与气缸压力的气门故障诊断

将小波分析引入气缸压力的分析与研究,对其进行二进小波分解,计算分解后尺度1上信号各频带的能量百分比,从中获得诊断特征量,从而对气门间隙进行故障诊断。结果表明用小波分析处理气缸压力信号较FFT更有效,作者采用的诊断方法与特征量是有效和可行的。     

柴油机机体结构振动信号特征参数计算分析

通过试验获得不同运行工况下不同测点的柴油机振动信号,采用时域特征参数计算分析柴油机工况变化对柴油机各测点振动信号的影响规律,并采用小波包技术计算柴油机不同测点振动信号各频带的能量及占总能量的百分比。研究结果表明:柴油机各测点的时域特征与频率特征参数能够体现出其不同的振动特性,很好地表征了与柴油机运转工况变化密切相关的振动状况变化规律及主要频率段的能量分布。     

小波包能量谱在发动机故障检测中的应用

小波包是继小波分析之后的又一种新型多尺度分析方法,它具有对非平稳信号进行局部化分析的功能,是在多分辨率基础上构成的一种更精细的正交分解方法,可以解决小波分析在高频部分分辨率差的缺点.本文以小波包分析为基础,对发动机振动信号进行实例分析,通过对采样信号的分解和重构,取其特征向量作为能量谱,比较正常信号和故障信号的能量谱和功率谱,能够判断出发动机的故障状态,验证了小波包能量谱对发动机故障检测的可行性.     

基于小波技术的柴油机振动信号特征参数的研究

在1110柴油机上模拟了气门漏气、气门间隙异常、供油时刻异常及喷油压力异常4种常见故障,并测得了几种故障下缸盖振动信号和缸内压力信号.对振动信号常用的几种分析方法进行对比研究,并选定小波分析法对振动信号进行时频分析,提取振动信号的特征参数.试验发现:气门漏气时整个缸盖振动信号高频带能量增加、低频带能量降低;气门间隙增大时,高频振动响应信号能量增强;供油提前角增大时,缸内燃烧始点提前,缸盖振动信号低频带信号能量增加;喷油压力增大时,缸盖振动信号中低频带信号所占能量增加.     

基于连续小波变换的内燃机瞬态工况噪声信号时频特性研究

以某四缸汽油机为研究对象,发动机加速过程中,对前端噪声信号进行测试,并对采集的噪声信号进行等长度分段预处理。采用连续小波变换方法分别对各数据段的噪声信号进行时频分析处理,分析噪声信号能量在时频域内的分布规律,以及其主要频率成分随转速或时间变化的特性。结果表明,发动机加速过程中,噪声信号能量主要集中在2阶主谐次和转动基频构成的线性调频带附近,而且随着转速的升高,调频带附近的信号幅值和能量也随之增大。在调频带上方也分布着一些频率成分,但是其幅值和能量相对较小,而且随着转速的升高,其频率成分越来越丰富,能量分布也越来越广泛。     

基于神经网络信息融合的发动机失火故障诊断

对发动机气缸失火故障进行实车模拟试验,测量了发动机的机体振动信号及瞬时转速信号,并对其进行了时、频域分析.通过小波分析方法提取了振动信号能量特征,通过复杂度分析方法提取了转速信号的复杂度特征用于故障诊断.根据多传感器信息融合理论,建立了集成神经网络信息融合模型对气缸失火故障进行了诊断.结果表明,发动机机体振动能量特征和转速复杂度特征能够反映气缸失火现象,基于发动机振动和转速信息融合进行气缸失火故障诊断,诊断可靠性较高.     

基于改进小波包的风电机组齿轮箱复合故障特征提取研究

针对小波包分解振动信号时会产生频谱混叠从而导致齿轮箱复合故障特征能量谱提取困难的问题,提出基于旁路滤波改进小波包的方法对双馈风电机组齿轮箱复合故障振动信号进行研究,并以风电场的大量齿轮箱振动信号为基础,运用传统小波包及旁路滤波改进小波包分别对齿轮箱振动信号提取特征能量谱。实验结果表明：运用旁路滤波改进小波包对双馈风电机组齿轮箱复合故障振动信号进行分析,可有效避免传统小波包分析振动信号的频谱混叠现象,准确提取每种故障状态的特征能量谱。     

基于声发射检测的阀门泄漏故障模式诊断技术研究

为了实现对阀门泄漏故障更直观、准确的诊断,采用理论与试验相结合的方法,对工程实际中常见的阀门泄漏模式进行了归纳和分类,分析了典型泄漏模式下阀门漏孔处流场及能量计算方法;对具有不同泄漏模式的阀门泄漏状态进行了模拟试验研究,并对阀门泄漏声发射信号进行了小波包分析,获得不同泄漏模式下阀门泄漏产生的声发射信号能量的变化规律.结果表明:不同泄漏模式下阀门发生泄漏产生的声发射信号各子频率段能量比具有规律性差异;该诊断标准为阀门泄漏故障的可视化诊断奠定了一定的基础.     

小波包能量谱在内燃机噪声信号故障诊断中的应用

基于噪声信号的故障诊断方法具有易于实现离机、无损检测的优越性,将噪声信号故障诊断方法引入内燃机气门间隙故障诊断中。阐述了小波包能量谱提取的基本原理和方法,通过对内燃机在不同转速不同状态下运行时的噪声信号小波包能量谱的提取研究,确定了噪声信号能量波动范围。并在对比分析中发现,利用内燃机噪声信号在特征频带内的能量波动特征研究方法,可以有效识别气门间隙故障。     

尺度--小波能量谱在内燃机故障诊断中的应用

为了对内燃机气门及活塞-连杆组故障进行有效地诊断,通过试验测取内燃机在不同故障下的振动信号,利用连续小波变换得到信号在尺度上的平均能量分布,即信号的平均尺度-小波能量谱.根据不同故障下振动信号在尺度上的能量分布差异,提出了基于尺度-小波能量的标准特征向量,并以此作为标准,结合欧氏距离方法,对待检故障信号进行诊断,定量判断出了内燃机的故障类型,取得了很好的结果,该方法为内燃机故障诊断提供了一种有效途径.     

基于图像处理与神经网络的内燃机故障诊断研究

在提出应用图像处理进行故障诊断的基础上,探讨了利用内燃机振动信号产生图像的方法。在对振动信号进行重抽样处理的基础上,实现了振动信号的灰度图像表示,从缸盖振动信号的灰度图像中提取图像特征并对图像特征进行模糊化处理,设计神经网络分类器,实现了对气阀机构故障的诊断与分类。     

基于小波包能量特征向量神经网络的旋转机械故障诊断

为精确诊断旋转机械的故障,提出一种基于小波包特征向量的神经网络故障诊断方法。用转子台信号模拟旋转机械故障,并对采集到的信号进行3层小波包分解,构造小波包特征向量,并以此为故障样本对3层BP网络进行训练,实现智能化故障诊断。实验结果表明训练好的神经网络能够很好地诊断出转子台故障类型,为旋转机械的故障诊断提供了新方向。     

Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia

This paper utilizes artificial neural networks for the prediction of hourly mean values of ambient temperature 24 h in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. This neural network is trained off-line using back propagation and a batch learning scheme. The trained neural network is successfully tested on temperatures for years other than the one used for training. It requires only one temperature value as input to predict the temperature for the following day for the same hour. The predicted hourly temperature values are compared with the corresponding measured values. The mean percent deviation between the predicted and measured values is found to be 3.16, 4.17 and 2.83 for three different years. These results testify that the neural network can be a valuable tool for hourly temperature prediction in particular and other meteorological predictions in general.     

ANN-GA based optimization of a high ash coal-fired supercritical power plant

The efficiency of coal-fired power plant depends on various operating parameters such as main steam/reheat steam pressures and temperatures, turbine extraction pressures, and excess air ratio for a given fuel. However, simultaneous optimization of all these operating parameters to achieve the maximum plant efficiency is a challenging task. This study deals with the coupled ANN and GA based (neuro-genetic) optimization of a high ash coal-fired supercritical power plant in Indian climatic condition to determine the maximum possible plant efficiency. The power plant simulation data obtained from a flow-sheet program, “Cycle-Tempo” is used to train the artificial neural network (ANN) to predict the energy input through fuel (coal). The optimum set of various operating parameters that result in the minimum energy input to the power plant is then determined by coupling the trained ANN model as a fitness function with the genetic algorithm (GA). A unit size of 800 MWe currently under development in India is considered to carry out the thermodynamic analysis based on energy and exergy. Apart from optimizing the design parameters, the developed model can also be used for on-line optimization when quick response is required. Furthermore, the effect of various coals on the thermodynamic performance of the optimized power plant is also determined.     

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

The most important theme in this study is to obtain equations based on economic indicators (gross national product—GNP and gross domestic product—GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968–2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R² values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections.     

Application of artificial neural network in performance prediction of PEM fuel cell

Investigations on using artificial neural networks to predict the performance of single proton exchange membrane fuel cell has been carried out. Two sets of polarization data obtained at different temperatures and flow rates are used to create and simulate the network. Cell temperature, humidification temperatures, H₂/air flow rates and current density have been used as inputs, and voltage is used as observed (output) value to train and simulate the network. This nonlinear data are batch trained, and artificial neural network has been constructed using feed forward backpropagation algorithm. Performance of the training has been improved by increasing the number of neurons to reduce the error. Simulation results are in agreement with experimental data, and the corresponding networks are used to predict the polarization behavior for unknown inputs. Copyright © 2011 John Wiley & Sons, Ltd.     

Single phase nanofluid thermal conductivity and viscosity prediction using neural networks and its application in a heated pipe of a circular cross section

This study investigates the single-phase simulation of nanofluid with a neural network incorporated into the thermophysical properties in governing equations for the single-phase treatment. The thermophysical properties affected are the viscosity, and the thermal conductivity, as both properties have been the area of contention in the study of nanofluid. The neural network is trained from experimental data gleaned from the available literature. The single phase and neural network are set up and solved using the finite element method in available commercial code. Grid independence was carried out, and the results were validated with experimental data that the neural networks were not trained with. It was observed that the lowest accuracy from the several simulations was 0.679% average percentage error. The results obtained agreed that nanofluids' thermal conductivity and viscosity can be accurately modeled for most single-material nanofluids and hence reducing the error in the simulations of nanofluids using the single-phase model which assumes the nanofluids are homogeneous and their properties are enhanced and effective.     

An adaptive wavelet-network model for forecasting daily total solar-radiation

The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet-networks are feed-forward networks using wavelets as activation functions. Wavelet-networks have been used successfully in various engineering applications such as classification, identification and control problems. In this paper, the use of adaptive wavelet-network architecture in finding a suitable forecasting model for predicting the daily total solar-radiation is investigated. Total solar-radiation is considered as the most important parameter in the performance prediction of renewable energy systems, particularly in sizing photovoltaic (PV) power systems. For this purpose, daily total solar-radiation data have been recorded during the period extending from 1981 to 2001, by a meteorological station in Algeria. The wavelet-network model has been trained by using either the 19 years of data or one year of the data. In both cases the total solar radiation data corresponding to year 2001 was used for testing the model. The network was trained to accept and handle a number of unusual cases. Results indicate that the model predicts daily total solar-radiation values with a good accuracy of approximately 97% and the mean absolute percentage error is not more than 6%. In addition, the performance of the model was compared with different neural network structures and classical models. Training algorithms for wavelet-networks require smaller numbers of iterations when compared with other neural networks. The model can be used to fill missing data in weather databases. Additionally, the proposed model can be generalized and used in different locations and for other weather data, such as sunshine duration and ambient temperature. Finally, an application using the model for sizing a PV-power system is presented in order to confirm the validity of this model.     

Optimal configuration of discrete heat sources in a vertical duct under conjugate mixed convection using artificial neural networks

This paper reports the results of a numerical investigation of the problem of finding the optimum configuration for five discrete heat sources, mounted on a wall of a three-dimensional vertical duct under mixed convection heat transfer, using artificial neural networks (ANN). The objective is to locate the positions for the five heat sources in such a way that the maximum temperature of any of the heat sources in a given configuration is a minimum. The three-dimensional governing equations of mass, momentum and energy equations for the fluid flow and the energy equation for the solid regime have been solved by using FLUENT 6.3 and a database of temperature versus configuration was generated. The temperature database developed from CFD simulations is used to train the neural network. The trained neural network predicts the temperature of the heat sources very accurately and much faster than the CFD software. With the use of this network, an exhaustive search for all possible configurations was done that resulted in a global optimum for the problem.     

Artificial neural networks used for the performance prediction of a thermosiphon solar water heater

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly effective in system modelling as well as for system identification. The objective of this work is to train an artificial neural network (ANN) to learn to predict the performance of a thermosiphon solar domestic water heating system. This performance is measured in terms of the useful energy extracted and of the stored water temperature rise. An ANN has been trained using performance data for four types of systems, all employing the same collector panel under varying weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were, the storage tank heat loss coefficient (U-value), the type of system (open or closed), the storage volume, and a total of fifty-four readings from real experiments of total daily solar radiation, total daily diffuse radiation, ambient air temperature, and the water temperature in storage tank at the beginning of the day. The network output is the useful energy extracted from the system and the water temperature rise. The statistical coefficient of multiple determination (R²-value) obtained for the training data set was equal to 0.9914 and 0.9808 for the two output parameters respectively. Both values are satisfactory because the closer R²-value is to unity the better is the mapping. Unknown data for all four systems were subsequently used to investigate the accuracy of prediction. These include performance data for the systems considered for the training of the network at different weather conditions. Predictions with maximum deviations of 1 MJ and 2.2°C were obtained respectively. Random data were also used both with the performance equations obtained from the experimental measurements and with the artificial neural network to predict the above two parameters. The predicted values thus obtained were very comparable. These results indicate that the proposed method can successfully be used for the estimation of the performance of the particular thermosiphon system at any of the different types of configuration used here. The greatest advantage of the present model is the capacity of the network to learn from examples and thus gradually improve its performance. This is done by embedding experimental knowledge in the network.