两挡纯电动汽车多目标参数解耦优化方法

提出了一种采用分层方法的解耦优化算法。外层采用多目标粒子群算法对整车动力部件参数进行优化，同时引入对电池以及逆变器的效率优化，将得到的不同优化结果实时提取并传递给内层；内层采用Bellman动态规划算法，根据外层优化得到的动力部件参数求解，建立优化后的换挡策略。在此基础上，通过广义回归神经网络提取动态规划的换挡优化结果，利用所得到的换挡策略建立了自适应驾驶员模型和整车正向仿真模型，以动力性和经济性为目标，通过整车正向仿真分析对分层优化结果进行进一步选择。研究结果表明，该优化算法实现了换挡控制策略与动力部件参数的解耦，有效提高了优化效率，同时能够获得全局优化结果，明显提高了整车经济性。     

单时频谱非负矩阵编码与解调的特征提取

非负矩阵分解(NMF)作为一种矩阵分解以及非线性维数约简工具,被广泛用于多样本振动时频谱的分解编码以及特征提取,但单样本振动时频谱的NMF编码、尤其NMF分解向量与振动时频谱分量间关联关系尚缺乏探讨。阐述了单时频谱编码与解调的特征提取原理,重点分析了NMF对单时频谱基于部分的特征表示能力、单时频谱NMF基向量的带通滤波幅频特性(BFAC)、以及NMF编码向量与时频谱分量的同步变化特性。提出单时频谱NMF编码与解调的两种特征提取新方法,即基于NMF基向量的滤波解调和NMF编码向量直接解调,定义一种BFAC指数指标和基向量归一化的NMF编码优化迭代规则分别用于NMF低维参数自适应选取和优化求解过程。将所提方法用于仿真信号以及齿轮箱振动信号分析,6.4 k长度数据在给定因子分解秩和NMF最大迭代300次终止条件设定下的特征提取用时约3.5 s,同时实现了对信噪比为-10 dB仿真信号以及多故障齿轮箱振动信号中故障特征的提取。     

弧齿锥齿轮弯曲应力计算

提出了一种计算弧齿锥齿轮弯曲应力过程的应力影响矩阵方法。此方法应用应力叠加原理,将二次插值得到的接触迹线应力影响矩阵与载荷分布矩阵相乘,建立齿根弯曲应力影响矩阵,从而得到随载荷过程而变化的齿根过渡曲面上的弯曲应力过程。只需一次有限元计算,便可获得整个啮合周期的齿根过渡曲面上弯曲应力分布变化过程。该方法计算精确且极大地简化了计算过程。     

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, the time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.     

A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder

Helical gears are widely used in gearboxes due to its low noise and high load carrying capacity, but it is difficult to diagnose their early faults based on the signals produced by condition monitoring systems, particularly when the gears rotate at low speed. In this paper, a new concept of Root Mean Square (RMS) value calculation using angle domain signals within small angular ranges is proposed. With this concept, a new diagnosis algorithm based on the time pulses of an encoder is developed to overcome the difficulty of fault diagnosis for helical gears at low rotational speeds. In this proposed algorithm, both acceleration signals and encoder impulse signal are acquired at the same time. The sampling rate and data length in angular domain are determined based on the rotational speed and size of the gear. The vibration signals in angular domain are obtained by re-sampling the vibration signal of the gear in the time domain according to the encoder pulse signal. The fault features of the helical gear at low rotational speed are then obtained with reference to the RMS values in small angular ranges and the order tracking spectrum following the Angular Domain Synchronous Average processing (ADSA). The new algorithm is not only able to reduce the noise and improves the signal to noise ratio by the ADSA method, but also extracts the features of helical gear fault from the meshing position of the faulty gear teeth, hence overcoming the difficulty of fault diagnosis of helical gears rotating at low speed. The experimental results have shown that the new algorithm is more effective than traditional diagnosis methods. The paper concludes that the proposed helical gear fault diagnosis method based on time pulses of encoder algorithm provides a new means of helical gear fault detection and diagnosis.     

转矩激励对二级齿轮系统脱啮行为的影响

在考虑齿轮时变啮合刚度、阻尼、转矩波动及齿侧间隙的情况下,建立了具有5自由度的二级齿轮传动系统的动力学模型.为了便于用算法分解算法求解,用多项式拟合齿侧间隙,而将啮合刚度、输入及输出转矩波动用Fourier级数表示.利用Adomian分解算法的思想,用AOM得到了系统对转矩激励的动力学响应.仿真结果表明,在某些转速区段,转矩激励会导致强烈振动;在低速齿轮对中,可能同时伴随脱齿现象,并导致较大的动载荷.     

Automatic surface inspection for directional textures using nonnegative matrix factorization

A global image restoration scheme using nonnegative matrix factorization (NMF) is proposed in this paper. This NMF-based image restoration scheme can be used for inspecting the defects in directional texture surfaces automatically. Decomposing the gray level of image pixels into an ensemble of row vectors, we first reduce the data set from original data space into a lower-dimensional NMF space. The repetitive and periodical primitives are well reconstructed by two lower-dimensional basis and weight matrices with nonnegative elements, named nonnegative matrix approximation (NMA). Then the local defects will be revealed by applying image subtraction between the original image and the NMA. As a consequence, the directional textures are eliminated, and only local defects are preserved if they initially are embedded in the surface. A supervised heuristic, elbow of residual curve rule, is devised which helps users to determine a proper basis space size of a specific image. Experiments on a variety of directional texture surfaces are given to demonstrate the effectiveness and robustness of the proposed method.     

基于正交距离回归齿面的齿轮误差评定

为了通过测量齿面拓扑轮廓来获取特征线误差,提出了一种基于正交距离回归齿面的误差计算方法。对该方法涉及的实际齿面与理论齿面匹配算法、拓扑轮廓误差的计算与分解及齿面特征线误差的评定算法进行了研究。首先,通过坐标测量方法获取的齿面拓扑数据,建立包含回归齿面参数的非线性方程。然后,求解非线性方程得到回归齿面参数的最优近似解,从而得到与实际齿面匹配的理论齿面,拓扑测量点相对理论齿面的正交距离即为齿面拓扑误差。最后,基于齿轮误差多自由度理论,对实际齿面进行局部自由度及全局自由度回归,进一步分解出齿面的齿廓误差和螺旋线误差。以一标准圆柱直齿轮的齿面拓扑测量点数据为例进行了误差计算,结果显示:计算的结果与直接进行特征线测量的结果差值小于0.5μm,表明提出的基于正交距离回归齿面进行齿轮误差评定的方法是有效的,可以应用于坐标类仪器检测齿轮误差。     

齿面误差激励对二级齿轮系统脱啮行为的影响

在考虑齿轮时变啮合刚度、阻尼、齿轮误差及齿侧间隙的情况下,建立了具有5自由度的二级齿轮传动系统的动力学模型。为了便于用算法分解算法求解,用多项式拟合齿侧间隙,而将啮合刚度、齿轮综合误差用Fourier级数表示。利用Adomian分解算法的思想,用AOM得到了系统对齿轮啮合误差激励的动力学响应。仿真结果表明,在研究的转速范围内,齿面误差激励会导致强烈振动;在低速齿轮对中,可能同时伴随脱齿对象,并导致较大的动载荷。当脱啮现象发生时,响应频谱变得复杂起来,并导致系统产生准周期运动。     

非对称齿廓齿轮弯曲疲劳强度理论分析与试验

为提高齿轮承载能力设计齿轮两侧压力角不等的非对称渐开线新齿形,推导双压力角非对称齿廓齿轮工作齿侧与非工作齿侧的渐开线齿廓方程和齿根过渡曲线方程,通过迭代计算和优化策略提出非对称齿廓齿轮疲劳强度解析法计算公式。编制生成非对称齿轮齿廓的参数化程序,在此基础上建立非对称齿廓齿轮有限元分析模型。通过解析法对不同压力角组合的非对称齿廓齿轮弯曲应力和危险截面位置计算得出,随着工作齿侧压力角的增大齿根最大弯曲应力逐渐降低,单齿啮合区向齿顶偏移;通过对有限元模型进行计算得出的结果与解析法一致,应用最小二乘法拟合出非对称齿廓齿轮齿根弯曲应力随工作齿侧压力角变化的计算公式。采用数控电火花线切割方法加工制造非对称与标准齿廓齿轮,在高频疲劳试验机上采用双齿脉动加载方法对其进行疲劳强度试验。试验结果表明,非对称齿廓齿轮在相同寿命下比对称齿轮极限载荷提高了50%,非对称齿廓齿轮的应力值变化趋势与前两种方法是一致的。     

基于聚类优化的非负矩阵分解方法及其应用

针对不断增加的机电系统运行状态信息,传统的特征提取和选择方法已无法满足需求。根据非负矩阵分解典型算法的特点,基于非负矩阵分解的聚类特性,提出了一种面向故障诊断的分解方法。通过分类能力和迭代效率的对比分析,选择了相关性约束和稀疏性约束的改进型交替最小二乘迭代算法,确定了低维嵌入维数及迭代初始化方法,在UCI测试数据集和TEP系统的特征选择应用中验证了该方法的有效性。     

齿轮误差三维评定方法

研究了齿轮误差三维评定方法,以消除测量仪器定位误差对齿轮误差评定的影响,提高齿轮测量仪器的测量精度。分析了传统齿轮二维评定方法的弊端,提出将齿轮误差评定从二维评定模式转变为三维评定模式。该评定模式不再要求齿轮各项误差测量点位于特征面内,实现了三维空间内的齿轮误差测量与评定。为提高齿轮三维误差评定算法的效率,通过螺旋降维方法将三维数据降至二维数据,再对二维测量数据进行各项误差的评定。以特大型齿轮激光跟踪在位测量系统作为实验对象,对4级标准齿轮的齿廓误差进行了测量,并与传统的齿轮二维误差评定方法以及德国ZEISS公司InvolutePro软件的评定结果进行了对比。结果表明:传统齿轮二维误差评定方法有较大误差,三维齿廓评定方法与德国ZEISS公司InvolutePro软件评定结果一致,精度达到0.1μm,证明了提出的齿廓误差三维评定方法完全正确,可以消除仪器定位误差对测量结果的影响,提高了测量仪器的测量精度。     

Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble

It is well known that the vibration signals are unstable when there is some failure in machinery. So in this paper, the cone-shaped kernel distributions (CKD) of vibration acceleration signals acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images. Meanwhile, non-negative matrix factorization (NMF) was used to decompose multivariate data, and neural network ensemble (NNE), which is of better generalization capability for classification than a single neural network, was used to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time–frequency distributions. It is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method.     

基于中间轴制动器AMT换挡品质控制

针对自动机械变速器(Automated Mechanical Transmission,AMT)换挡过程存在动力中断的问题,分析了换挡过程各阶段的特点,确定了中间轴制动器在转速同步阶段的作用,并在此基础上提出了中间轴制动器参数的计算方法。结合气缸式执行器气压的滞后特性,提出了一种以同步器结合套和待结合齿轮转速差为控制目标,从而控制中间轴制动器介入时间的控制策略。利用AMESim搭建了中间轴制动器的动力学仿真模型,利用Simulink软件搭建了控制策略仿真模型。通过模型仿真,分析了由于气压的滞后特性,策略采用不同目标转速差时对换挡品质的影响,并且论证了顺序升挡和跳跃升挡时控制策略对提高换挡品质的作用。     

柴油机振动信号快速稀疏分解与二维特征编码

针对柴油机故障诊断方法中的信号时频表征及特征提取问题,提出一种基于振动信号快速稀疏分解与二维时频特征编码识别的柴油机智能故障诊断方法。首先,为了获得时、频聚集性优良的时频图像,提出一种随分解残差信号自适应更新Gabor字典的改进匹配追踪(adaptive matching pursuit,简称AMP)算法,利用AMP算法将柴油机振动信号分解后叠加各原子分量的Wigner-Ville分布,获取原信号的稀疏分解时频图像;然后,为提取时频图像的特征参量,提出了双向二维非负矩阵分解(two-directional,2-dimensional non-negative matrix factorization,简称TD2DNMF)算法,用于对时频图像的幅值矩阵进行特征编码,获取蕴含在时频图像内部的低维特征,并利用最近邻分类器实现了时频图像的自动分类识别。将提出的方法应用于4种不同状态柴油机气门故障的诊断试验中,结果表明,该方法能够获得无交叉项干扰、聚集性好的时频图像,使各时频分量的物理意义更加明确,并改进了传统图像模式识别中的特征参数提取方法,是一种有效的柴油机故障诊断方法。     

A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM

In the condition monitoring of gear reducer, the labeled fault samples are sparse and expensive, while the unlabeled samples are plentiful and cheap. How to diagnose the faults occurring in complex and special gear reducer effectively becomes a troublesome problem in case of insufficient labeled samples or excess unlabeled samples. This paper presents a novel model for fault diagnosis based on empirical mode decomposition (EMD) and multi-class transductive support vector machine (TSVM), which is applied to diagnose the faults of the gear reducer. The experimental results obtain a very high diagnosis accuracy. Even though the number of unlabeled samples is 50 times as that of labeled samples, the mean of testing accuracy of the proposed novel method can reach at 91.62%, which distinctly precedes the testing success rates of the other similar models in the same experimental condition.     

Performance studies on salt bath treated low-carbon steel gears

Tufftride TF1 treated low-carbon steel gears were tested in a back-to-back gear test rig. The purpose was to generate design data and to obtain information on the mechanism of failure. Contact stress versus cycles to failure (S-N) curve has been plotted, and the results correlated with data obtained from rolling contact fatigue tests. In an attempt to obtain a clear understanding of events at the contact surfaces of gear teeth, a complete analysis of gear operating conditions was made and the results are presented. Failure investigations have been performed using optical and scanning electron microscopes     

齿轮性能退化评估的时序重构模型

齿轮性能退化评估是预诊断的提前和基础,针对概率相似度量评估方法存在模型复杂,容易过早饱和等现象,提出一种基于AR (Autoregressive model)模型和字典学习的齿轮性能退化评估的重构模型方法,其中AR模型用于提取齿轮振动信号的状态特征,字典学习通过正常状态下构建的字典模型(Dictionary learning, DL)对测试样本进行AR模型系数重构。首先提取正常运行状态下振动信号的AR模型系数构建过完备字典模型,然后将待测信号的AR系数作为特征向量输入字典模型中得到重构后的AR模型系数。最后由原始AR系数和重构AR系数分别构造自回归模型,并各自完成对待测信号的时序建模,将两自回归模型所得残差序列的均方根误差作为性能劣化程度指标。全寿命疲劳实验数据分析结果表明,与传统时域指标相比该方法对早期故障更敏感且具有与齿轮故障发展趋势一致性更好等优点。     

复杂周转轮系同构识别的等效电路方法

周转轮系的同构识别可剔除综合过程中出现的重复结构,对于周转轮系的构型综合具有重要意义。针对周转轮系同构识别问题,提出了一种建立等效电路识别周转轮系同构的新方法。运用双色拓扑图描述周转轮系拓扑结构,避免伪同构情形。根据电路网络与周转轮系拓扑图的相似拓扑约束特性,将拓扑图转化为具有相同约束的等效电路,根据等效电路元素进行初步同构识别。应用回路电流法列出等效电路基本回路电流的齐次线性方程组,其系数矩阵保留有等效电路的结构特征。证明系数矩阵特征值相同可作为等效电路同构的充要条件。结合实例验证了该方法的简便性和可靠性。     

基于LCD的自适应小波脊线解调及齿轮故障诊断

针对最佳小波参数的设定和齿轮裂纹故障振动信号频率成分复杂、信噪比低等问题,将遗传优化算法、小波脊线解调与局部特征尺度分解(local characteristic-scale decomposition,简称LCD)相结合,提出了基于LCD的自适应小波脊线解调方法。首先,采用LCD方法将原始信号分解为若干个内禀尺度分量(intrinsic scale component,简称ISC),并通过选择蕴含特征信息的ISC来实现信号降噪;然后,以小波能量熵为目标函数,采用遗传算法优化小波参数,得到自适应小波;最后,通过自适应小波分析提取ISC的小波脊线,从而实现对原始信号的解调分析。通过齿轮裂纹故障诊断实例验证了该方法的有效性和优越性。