基于小波分析的柴油机振动信号降噪处理

针对柴油发动机缸盖振动信号信噪比低且呈非平衡特性,对柴油机在工作过程中的缸盖振动信号进行小波噪处理,突出了缸盖在不同时刻受到的激振,为柴油机在线控制和故障诊断提供了进一步的处理和识别的依据。本文对消除柴油机振动信号的随机成分,提高信噪比提出了一种新的方法－小波变换方法。分析中利用3次B样条小波对2135G柴油机的缸盖振动信号按Mallat算法进行分解,运用小波的滤波性能去除随机干扰,对振动信号进行了重构,获得了预计良好的分析结果,显示了小波技术在柴油机信号处理－控制领域的广阔应用前景。     

基于小波软阈值降噪的内燃机缸盖振动信号时域识别

针对内燃机缸盖振动信号信噪比低且呈宽带、非平稳、时变特性,本文首先采用了基于软阈值的小波紧缩降噪方法进行处理,从而有效地突出了缸盖在不同时刻所受到激励的响应信号;然后,对降噪后的时域响应信号分别建立二阶AR模型,以模型参数作为模式识别特征参数;最后,采用感知器神经网络对特征参数进行识别与分类.由于缸盖振动信号信噪比的有效提高,使得对缸盖振动信号的时域识别和分类取得了很好的结果.通过对实验数据的处理,进一步验证了方法的有效性.     

基于BP神经网络的信息融合发动机故障诊断的研究

本文为了解决发动机喷油器故障诊断中基于单传感器信息的方法诊断精度低的缺点。在神经网络分析的基础上,提出了一种基于气缸压和,缸盖振动信号和燃油压力等多传感器信息融合的喷油器故障诊断新方法该方法能有效地提高其故障诊断精度。     

混沌分形在内燃机振动信号分析中的应用

从理论上分析了柴油发动机燃烧过程的分形特性，运用混沌和分形理论对实际测得的发动机缸盖振动信号进行了分析，计算了信号的关联维数。结果表明：缸盖的燃爆冲击振动是一种混沌运动。而且，随着喷油器工作状态的不同，信号的关联维数也发生变化。系统振动不确定性成分增多，关联维数变大，系统振动确定性成分增多．关联维数变小。通过选择合适的嵌入维数，计算的关联维数能够反映系统的真实特性，关联维数可以作为发动机状态监测和故障诊断的一个特征参量。     

基于MIMU的FastICA算法钻头故障振动信号分析

为了解决钻头在钻进过程中由于采集到的信息存在信噪比低和钻头与钻柱振动信号互相混叠而难以对钻头故障振动状态进行有效识别的问题。提出一种基于MIMU的FastICA算法来对钻头与钻柱振动数据进行处理分析,从而放大了钻头振动信号特征(正常、松动),提高了钻头振动信号的识别度。首先对经MIMU采集而来的原始数据进行误差分析和预处理,接着使用FastICA算法进行仿真实验,最后采用预处理后的松动状态下钻头与钻柱的振动信号进行实例实验验证。经验证,此方法有效放大了钻头的振动信号特征,有一定的实际意义。     

小波分析理论的信号变尺度阈值去噪算法探究

当前对于信号辨识控制系统的研究分析领域中,整数阶信号辨识系统本身对于信号的去噪处理能够获得相对有效的理想效果,提出基于小波分析理论的信号变尺度阈值去噪算法,加入变尺度阈值的方法进行相应的处理。通过仿真模拟实验进行有效合理统计分析,在各种程度的信噪比状况进行算法改进,对有用信号的去噪效果实现理想的处理结果。     

基于稀疏表示的系统辨识方法

基于信号的稀疏表示理论提出一种线性时不变系统辨识方法.该方法利用线性调频信号作为线性时不变系统激励输入信号,在利用传统方法进行系统辨识前利用稀疏分解算法对系统输出信号进行噪声处理.线性调频信号具有较好的时频聚集特性,线性时不变系统的输出也将具有很好的时频特征,利用基于Gabor字典的稀疏分解将能有效地提取输出信号中的有效分量,滤除其中的噪声成分,提高系统辨识的精度.仿真实验表明,本文提出的方法在低信噪比情况下,辨识效果好于传统方法.     

一种基于信噪比最大化的信号恢复方法

针对平稳噪声下的信号恢复问题,提出一种信噪比最大化指标函数,利用混合信号中噪声的协方差矩阵先验知识,通过多通道信号的线性组合,恢复出需要检测的信号,并使其信噪比最大化。对方法进行了推导和仿真,结果表明,这种基于信噪比最大化的信号恢复方法能够在信号和噪声频谱有较大重叠的情况下,增强信号同时抵消噪声,以最大信噪比恢复出需要的信号,效果明显,具有良好的实际应用价值。     

EMD滤波在煤矿电动机故障诊断中的应用

针对煤矿电动机信号故障特征难以提取的问题,提出将EMD滤波方法应用于煤矿电动机的振动信号分析,通过分析电动机故障特征频率的分布特征,采取不同的EMD滤波方法来去除振动信号中的噪声。现场应用结果表明,该方法可以有效降低噪声,提高信号的信噪比。     

基于融合分析的感应电机轴承故障检测方法

根据感应电机轴承发生故障时的振动信号特性以及定子电流特性，求出三相电流的Park矢量模信号，并将其与电机滚动轴承振动信号经解调处理后的包络信号进行融合分析。可以从振动信号与电流信号的融合谱图中有效地提取轴承故障特征信息，并将其作为故障识别的依据。实验结果表明，本文检测方法具有较高的信噪比，提高了诊断的可靠性。     

Combustion timing control of HCCI engine based on NNPC and Elman-NN model under complex conditions

Homogeneous Charge Compression Ignition (HCCI) combines the characteristics of gasoline engine and diesel engine with high thermal efficiency and low emissions. However, since there is no direct initiator of combustion, it is difficult to control the combustion timing in HCCI engines under complex working conditions. In this paper, Neural Network Predictive Control (NNPC) for combustion timing of the HCCI engine is designed and implemented. First, the black box model based on Elman neural network is designed and developed to estimate the combustion timing. The fuel equivalence ratio, intake valve closing timing, intake manifold temperature, intake manifold gas pressure, and engine speed are chosen as the system inputs. Then, a NNPC controller is designed to control combustion timing by controlling the intake valve closing timing. Simulation results show that the Elman neural network black box model is capable of estimating the HCCI engine combustion timing. In addition, regardless of whether the HCCI engine is in constant or complex condition, the designed NNPC controller is capable of keeping the combustion timing within the ideal range. In particular, under New European Driving Cycle (NEDC) working conditions, the maximum overshoot of the controller is 28.95% and the average error is 1.03 crank angle degree. It is concluded that the controller has good adaptability and robustness.     

基于FOA优化GRNN的船用柴油机涡轮增压系统故障诊断

为了及时有效的发现并排除船用柴油机涡轮增压系统的故障,文中采用果蝇优化算法（FOA）对广义回归神经网络（GRNN）的分布密度SPREAD进行优化选取,提出了一种果蝇优化算法和广义回归神经网络相结合的故障诊断新方法。收集某型号船用柴油机的样本集,采用相同的训练样本分别对FOA优化GRNN和RBF神经网络进行训练,并用相同的测试样本对以上两种模型进行验证。结果表明,与RBF神经网络故障诊断方法相比,FOA优化GRNN对柴油机涡轮增压系统故障模式的识别准确率更高。     

Closed-loop diesel engine combustion phasing control based on crankshaft torque measurements

Methods for closed-loop combustion phasing control in a diesel engine, based on measurements of crankshaft torque, are developed and evaluated. A model-based method for estimation of cylinder individual torque contributions from the crankshaft torque measurements is explained and a novel approach for identification of crankshaft dynamics is proposed. The use of the combustion net torque concept for combustion phasing estimation in the torque domain is also described. Two different control schemes, one for individual cylinder control and one for average cylinder control, are studied. The proposed methods are experimentally evaluated using a light-duty diesel engine equipped with a crankshaft integrated torque sensor. The results indicate that it is possible to estimate and control on a cylinder individual basis using the measurements from the crankshaft torque sensor. Combustion phasing is estimated with bias levels of less than 0.5 crank angle degrees (CAD) and cycle-to-cycle standard deviations of less than 0.7 CAD for all cylinders and the implemented combustion phasing controllers manage to accurately counteract disturbances in both fuel injection timing and EGR fraction.     

基于IMF复杂度和RBF网络的配气机构故障诊断

针对柴油机振动信号的非平稳特性,提出一种经验模态分解(Empirical Mode Decomposition,EMD)、复杂度和RBF(radical basis function)神经网络相结合的故障诊断方法;运用经验模态分解方法对特定时段的振动信号进行分析,计算前5个固有模式分量(Intrinsic Mode Functions,IMF)的Lempel-Ziv相对复杂度作为故障特征向量,并利用RBF神经网络可以快速逼近任意非线性函数及良好分类能力的特点,来实现对柴油机工作状态和故障类型的判别;最后,利用实际柴油机试验数据的诊断和对比试验验证了该方法的有效性。     

基于神经网络的故障诊断应用研究

文中根据神经网络的相关原理，运用神经网络领域的相关知识，对于工业生产中经常出现的设备故障，提出了一种基于RBF神经网络的诊断方法，并详细讨论了RBF神经网络的结构、训练算法及用于设备故障诊断的步骤，最后以某系统的故障诊断为例，证明了此方法的优越性。     

Microprocessor control of ignition advance angle

The ignition control requirements of an internal combustion petrol engine are reviewed and the benefits of accurate ignition control are discussed. A design for a microprocessor-based open-loop ignition controller is described and the experimental results obtained with this controller presented. Various means of achieving further improvements are suggested, including a closed-loop controller for optimum ignition timing under all conditions of engine wear. This strategy uses peak cylinder pressure angle as the feedback signal on which the adaptive control is based.     

Neural networks and fuzzy logic-based spark advance control of SI engines

In SI engines, spark advance (SA) needs to be controlled to get Maximum Brake Torque (MBT) timing. Spark advance can be controlled either by open loop or by closed loop controller. The open loop controller requires extensive testing and calibration of engine, to develop look up tables. In closed loop controller, empirical rules relating variables deduced from cylinder pressure are used. One of such empirical rules is to fix location of peak pressure (LPP) at a desired value of the crank angle. In the present work, a combined neural network and fuzzy logic-based control scheme is designed for SA control to get MBT timing. The fuzzy logic controller is designed to maintain LPP of SI engine close to 16° ATDC. The controller works in conjunction with Recurrent Neural Network model for cylinder pressure identification. LPP is estimated from cylinder pressure curve reconstructed using neural network model and is used as feedback signal to fuzzy logic controller. The simulations have been carried out to test the performance of the combined neural network and fuzzy logic-based control strategy. The simulation results show that the proposed strategy can quite satisfactorily control LPP to its desired value.     

基于LabVIEW的柴油机高速数据采集系统

为了验证船舶动力系统故障诊断系统的数据处理、故障诊断等功能,搭建智能机舱故障诊断试验平台,开展动力系统故障模拟试验,采集瞬时转速、柴油机气缸缸内压力等关键参数。由于瞬时转速与柴油机缸压信号的采集速率不同,为满足各信号采集的高速性和同步性,提出通过LabVIEW建立高速数据采集系统,结合运用光电编码器和磁电式转速传感器,以光电编码器Z相输出信号触发各信号同步开始采集, 以光电编码器B相输出信号和磁电式转速传感器输出信号分别触发采集曲轴转角信号,作为各信号对应基准的方法。试验证明,此高速采集系统可实现各信号周期对应,并能够保证数据采集的高速性和同步性,有利于故障诊断服务平台提取和分析故障特征参数。