汽车发动机瞬时转速信号的小波分析

采用小波变换这一工具对瞬时转速信号进行处理。在介绍小波变换基本原理的基础上,对曲轴瞬时转速信号进行了多分辨率分析,即把转速信号分解为多个层次的细节部分和平滑部分。曲轴瞬时转速信号中的噪声被分解到了各层的细节部分,由剩余的平滑部分呆判断发动机的失火和检测各缸工作均匀性,从而为实现失火检测和各缸工作均匀性检测提供了一个新的信号处理方法。     

柴油机飞轮端与自由端测量瞬时转速波动的差异分析

柴油机瞬时转速监测法则是一种新兴的状态分析方法,其一般利用磁电式传感器在柴油机飞轮齿圈或者自由端加装齿轮进行测量。传感器的安装位置对瞬时转速的测量值波动存在差异,影响状态分析的结果。结合仿真和测量实例对柴油机飞轮端与自由端测量瞬时转速波动的差异进行分析,得出了在飞轮端安装传感器能直接反映机器的动力学性能,测量位置较为理想。     

基于瞬时转速的柴油机早期弱故障诊断实验研究

瞬时转速蕴含了大量柴油机运行状态信息,已经成功用于失火严重故障的诊断,但对于早期弱故障的诊断尚缺乏进一步的研究。针对6-135型柴油机,通过在不同程度上调整气缸喷油量、调整气阀间隙、模拟气阀泄漏等方法模拟柴油机早期故障,在不同工况下进行实验数据采集,计算柴油机瞬时转速信号,并分析其不同状态下的变化规律。发现柴油机瞬时转速信号可以用于判断多种早期故障,各缸瞬时转速升程归一化比值P是较直接的特征参数。正常状态下,各缸瞬时转速P值在0.9~1.1以内,若某缸升程P值下降超过10%,可认为该气缸存在故障。该方法对于影响缸内压力的故障如各气缸喷油量不均匀、气阀泄漏等比较敏感,对于活塞-缸套间隙过大等对缸内压力影响程度小的故障不敏感。     

水电机组飞轮力矩的在线检测方法

为实现水电机组飞轮力矩的在线检测,推导了飞轮力矩的计算公式,对水电机组甩负荷过程中转速变化情况进行了研究,提出了利用机组甩负荷的转速数据监测飞轮力矩的方法。该方法将转速信号来源分成监控系统的模拟转速信号和调速器系统的齿盘测速信号。针对模拟转速信号,首先,采用小波变换滤波对转速数据进行滤波处理;然后,在指定相关系数水平上,采用自适应时长计算转速与时间的相关系数确定转速线性上升段,对齿盘获得的转速信号采用定时长方法计算转速与时间的最大相关系数确定转速线性上升段;最后,根据标准GB/T1029—2005规定的发电机甩负荷加速试验方法计算飞轮力矩,采用实测数据对该方法的有效性进行了验证。     

汽车发动机瞬时转速测量中的噪声分析及补偿方法

对汽车发动机的瞬时转速信号中的量测噪声进行补偿。试验测量结果表明,由飞轮齿圈的齿形误差引起的噪声是瞬时转速信号中的主要噪声源。并且该噪声的幅值与发动机的平均转速成正比。由于这种噪声的频带与飞轮瞬时转速信号物频带可能重叠,因此采用传统的滤波等方法无法完全肖除齿形误差引起的噪声,提出采用２个传感器的补偿新方法,可将齿形误差引起的噪声从瞬时转速信号中消除,试验结果验证了该方法的可行性,训利用瞬时转速检测     

曲轴瞬时转速在发动机控制中的应用研究

曲轴瞬时转速在一定程度上直接反映发动机的真实工作状态,采集一个工作循环的曲轴瞬时转速信号,利用曲轴瞬时转速和发动机气缸在工作时域上的严格对应关系,提取特定角度范围的瞬时转速,通过分析瞬时转速的波动大小和变化率,可以用来识别发动机各气缸做功状态,是否存在气缸失火,密封性差,或喷油器磨损等故障,同时也是实现单曲轴降级起动的一个重要参数。     

船用内燃机扭振测试分析研究

本文测试分析目的是通过内燃机系统轴系扭振测试分析,利用轴系扭振共振频率等测试结果,确定、调整各设定工况的转速。在柴油机转速从360r/min到750r/min范围内的16个工况下,对曲轴自由端进行扭振测试。将测试所得的电压信号转化为瞬时转速信号,然后将瞬时转速信号进行预处理得到转速波动曲线,对转速波动曲线积分得到轴系的角位移曲线,最后通过频谱分析得到各谐次的扭振幅值随转速变化曲线。通过分析可以得到内燃机曲轴飞轮端综合扭振角位移幅值、自由端综合扭振角位移幅值、内燃机的共振转速等。     

柴油机曲柄连杆机构动力学仿真与瞬时转速分析

利用ADAMS软件对柴油机曲柄连杆机构进行动力学仿真,通过对各气缸添加气体压力使曲轴匀速转动,并且对各气缸往复惯性力转矩和气体压力转矩进行合成,分析了转矩的变化特点。最后把曲轴上所受的转矩与瞬时转速变化进行对比说明内部激励转矩与瞬时转速的波动有密切联系。     

基于YN30高压共轨柴油机曲轴的扭转振动测试分析

针对 YN30高压共轨柴油机,借助于 LabVIEW 软件及硬件平台,设计开发了一套曲轴扭转振动测试分析系统,并通过对发动机曲轴两端瞬时转速信号的同步测量,分析了 YN30柴油机曲轴旋转方向的振动。通过对振动角位移各主谐次的幅值和相位分析,可以获得 YN30发动机不同频率曲轴扭转振动的临界转速为2200和2750 r/min,固有频率为367 Hz,自由端扭振幅值远大于飞轮端扭振幅值,振动节点靠近飞轮端等重要信息,并且验证了该发动机曲轴的设计及相应减振措施的合理性。     

非平稳信号瞬时特征提取的谐波小波方法

分析了非平稳信号的瞬时特征(瞬时频率和瞬时相位),对非平稳信号进行谐波小波变换,建立非平稳信号的谐波小波系数与该信号的瞬时特征之间的关系,提出非平稳信号瞬时特征提取的谐波小波模型和提取方法.通过算例中的线性调频信号和应用实例中的轴承座振动信号的验证表明,该模型与方法具有较好的抗噪能力,对非平稳信号的瞬时特征具有更高的分析精度,能实现非平稳信号中特殊成分的瞬时特征提取.该方法能通过傅里叶变换实现其快速算法,具有算法简单、快速的特点,实现非平稳信号瞬时特征的实时分析.     

WEIS wheel speed real-time measuring method for VOSM

Wheel speed is one of the key parameters of vehicle operating attitude. To solve the problems in traditional wheel speed measuring methods, such as low measurement precision and the lack of real-time monitoring of the vehicle’s operating attitude, a wheel embedded intelligent sensors (WEIS) wheel speed measuring method for vehicle operating safety states monitoring (VOSM) is innovatively proposed. Radial acceleration signal is obtained through a WEIS module embedded in the hub. Using wavelet packet to implement wavelet de-noising for the non-stationary acceleration signals, and adopting short-time Fourier transform (STFT) algorithm to extract the signal characteristics, the wheel speed measurement can be achieved. The experimental result shows that under experimental conditions the speed measurement error is − 2.05%, and the speed measuring response time is 0.45 s.     

WEIS wheel speed real-time measuring method for VOSM

Wheel speed is one of the key parameters of vehicle operating attitude. To solve the problems in traditional wheel speed measuring methods, such as low measurement precision and the lack of real-time monitoring of the vehicle’s operating attitude, a wheel embedded intelligent sensors (WEIS) wheel speed measuring method for vehicle operating safety states monitoring (VOSM) is innovatively proposed. Radial acceleration signal is obtained through a WEIS module embedded in the hub. Using wavelet packet to implement wavelet de-noising for the non-stationary acceleration signals, and adopting short-time Fourier transform (STFT) algorithm to extract the signal characteristics, the wheel speed measurement can be achieved. The experimental result shows that under experimental conditions the speed measurement error is −2.05%, and the speed measuring response time is 0.45 s.     

小波神经网络在柴油机异响故障诊断中的应用

由于活塞敲缸响和活塞销响是两种常见的、却难以区分的柴油机异响故障，这里对EQ6BT柴油机这两种故障的缸体振动信号进行Morlet连续小波变换，作出小波变换系数的尺度-能量谱，并提取出尺度为3～20范围内的最大尺度能量作为BP神经网络的输入向量，实现了对该柴油机两种异响故障的诊断。结果表明，利用文中所设计的小波神经网络能非常准确地诊断出EQ6BT柴油机活塞敲缸响、活塞销响两种异响故障及其故障的严重程度。     

基于遗传算法的商用车传动系的参数优化

研究了综合考虑动力性和燃油经济性指标的汽车动力传动系数学模型。以燃油经济性最佳为优化目标以及整车动力性作为约束条件,根据指定柴油机和整车参数,运用遗传算法对某商用货车的传动系参数进行优化计算。结果显示,优化后的传动系参数,使得整车在原地起步加速时间基本不变的情况下最高车速提高了2.98%,使得整车混合百公里油耗降低了0.67%。表明优化参数使整车的动力性和燃油经济性得到了不同程度的改善。     

Detection and monitoring of crack in a coast-down rotor supported on fluid film bearings

A Jeffcott rotor with a transverse crack has been considered in the present study. Equations of motion for transient response have been developed and dynamic analysis has been carried out to consider the effects of fluid film bearings. The coast-down phenomenon was analysed by considering the dissipation through the journal film and by evaluating the deceleration for each speed. Characteristic sub-critical response peaks have been found when the cracked rotor decelerates through its critical speed. The continuous wavelet transform (CWT) has been used as a tool to extract the above sub-harmonics from the time domain signals. The use of CWT has been suggested for crack detection and monitoring in a rotor system coasting down through its critical speed.     

电动轮传动系统齿根动应力试验研究

本文利用有限元理论，建立了电动轮传动系统的数学模型；分析了传动系统中承载斜齿轮轮齿的应力；并采用遥测方法对电动轮样机进行了动态应力测试。研究结果揭示了电动轮传动系统在不同行驶速度下的齿根应力变化规律，指出了最高机械效率下的电动轮转速即最佳车速，并根据试验结果对改进电动轮加工工艺提出了建议。     

基于小波变换的车轮力传感器信号的去噪研究

在汽车道路试验中，通过多维车轮力传感器（WFT）可以测量每个轮所受的各维力和力矩。在测量过程中，信号会不可避免地受到各种噪声的干扰，而且，在将测量数据从车轮坐标系转换到车辆坐标系时，车轮转角的误差使测量结果产生了更严重的噪声。这些宽带随机噪声严重影响了车辆性能的分析。小波分析是一种信号的时间-尺度分析方法，特别适合于非平稳信号的分析，具有多分辨率分析特性，而且在时频两域都具有表征信号局部特征的能力。针对车轮力信号的特点，在MATLAB环境下编程进行车轮力信号小波变换去噪研究，试验结果表明，在选择了适当的小波基本函数和阈值的情况下，采用小波变换的闻值去噪方法对多维车轮力信号进行去噪处理，可以取得良好的效果。     

Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel

Grinding burn is a discoloration phenomenon according to the thickness of oxide layer on the ground surface. This study tries to establish an automatic grinding burn detection system with robust burn features that are caused by burn and not by the design parameters. To address this issue, a method based on acoustic emission sensor, accelerator, electric current transducers, and voltage transducers was proposed in an attempt to extract burn signatures. A trial-and-error experimental procedure was presented to find out burn threshold. Vitrified aluminum oxide grinding wheel and AISI 1045 steel workpiece were used in the grinding test, as they were the most commonly used wheel–workpiece combinations in conventional grinding process. With the help of fast Fourier transform and discrete wavelet transform, the spectral centroid of AE signal, the maximum value of power signal, and the RMS of the AE wavelet decomposition transform from wavelet decomposition levels d1 to d5 were extracted as burn features. The spectral centroid of AE signal was believed not to be affected by grinding parameters. A classification and prediction system based on support vector machine was established in order to identify grinding burn automatically. Results indicate that the classification system performs quite well on grinding burn classification and prediction.     

Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.     

高速铁路车轮扁疤激扰响应的小波包分解与限值研究

扁疤是轨道交通车辆车轮踏面的典型故障之一,其对于列车运行的平稳性和安全性有很大影响。目前尚未对不同速度下的扁疤限值制定统一的标准。本文对动力学仿真计算得到的轮轨力随机响应采用一种全局性的小波包分解处理方法,提取轮轨力随机信号的能量系数作为评判指标,从能量特征的角度研究了不同速度等级下的车轮扁疤安全限值,并与在时域信号中提取轮轨力随机响应的最大值、均值和脉冲因子作为评判扁疤安全限值的方法相比较。结果表明：小波包能量系数从全局性角度描述轮轨力特性,节点能量系数呈线性规律,可作为高速铁路车轮扁疤安全限值的评判指标。研究得出当列车速度在150～250 km/h范围内,扁疤长度应控制在30 mm以内;当列车速度在250～350 km/h范围内,扁疤长度应控制在25 mm以内。