非线性齿轮系统单齿故障动力学特性

针对齿轮系统运行过程中具有非线性动力学特性,借鉴混沌振子检测理论中根据系统相轨变化检测信号的原理,分析了齿轮单齿故障冲击信号出现的成因及其出现故障后非线性动力学特性的变化,建立了基于冲击分析的非线性齿轮系统单齿故障动力学模型。通过分析发现,齿轮系统模型在一定的参数条件下,其动力学特性会进入混沌状态。而在相同参数条件下,出现单齿冲击故障并达到一定程度后,齿轮系统会在故障冲击的激励下进入大周期运动,从而表现出明显异于无故障条件下齿轮系统的动力学特性。仿真结果表明,该方法能有效区别齿轮系统单齿故障状态。     

齿轮全齿磨损的胞映射全局动力学分析

含故障齿轮非线性系统的全局性态分析是对齿轮正确故障诊断的状态依据,运用能够反映非线性系统复杂全局性态的简单胞映射方法,把故障齿轮非线性系统所处的状态空间转化为胞空间,对齿轮非线性系统在不同程度磨损故障状态下的动力系统的全局特性进行了分析,通过对比不同程度故障下系统的吸引域及其吸引胞,得到磨损故障齿轮非线性系统全局特性的变化规律,为齿轮非线性系统的故障诊断提供了另一种研究思路和方法。     

含故障的齿轮系统非线性动力学特性分析

综合考虑轻微磨损故障和裂纹故障等因素,建立含故障的直齿轮副的非线性动力学模型,并利用4～5阶变步长Runge-Kutta法对含故障的直齿轮模型的动力学方程进行数值求解;得到了单级齿轮系统在无故障、轻微磨损故障和裂纹故障状态下系统的分岔图、相图和Poincaré映射图;研究了齿轮系统在无故障和故障时的动力学行为,研究结果为齿轮系统的故障诊断、动态设计和安全运行等提供了理论参考。     

齿侧间隙对齿轮系统动力学行为的影响

为分析齿轮传动系统在齿侧间隙变化条件下的非线性动力学变化机理,对不同齿侧间隙参数下非线性齿轮传动系统的动力学行为进行了研究,建立了全齿齿侧间隙变化的齿轮传动系统非线性动力学模型,探讨了不同齿侧间隙参数条件下齿轮传动系统吸引子的变化。研究表明,齿侧间隙的变化不仅能够影响齿轮传动系统振动幅值的变化,同时,齿侧间隙的变化也能够显著改变齿轮传动系统的动力学行为,使齿轮传动系统在混沌状态与周期状态间发生跃变。研究结果能够为齿轮传动系统的设计和故障诊断提供一定的参考。     

变载荷激励下齿轮传动系统齿根裂纹故障动力学特性分析

根据齿轮裂纹会引起齿轮系统的时变啮合刚度变化这一特性,建立了含齿根裂纹故障单级齿轮传动系统的四自由度动力学模型,对裂纹故障的非线性动力学机理进行了研究。采用变步长的四阶龙格—库塔法对齿轮裂纹故障进行仿真分析,运用时频分析方法研究了裂纹信号特征,为齿轮裂纹故障诊断提供了理论支持。在此基础上,分析了齿轮裂纹在变载荷激励下的动力学特性。分析表明:在变载荷激励下,齿轮振动波形图与变载荷激励变化趋势极其相似,裂纹的存在引起波形图上出现冲击现象并且啮合频率及其倍频附近出现边频带,边频带为故障齿轮的转频;变载荷激励下,低频的边频成分明显只与外载荷激励有关,与齿轮故障无关。     

斜齿轮疲劳点蚀故障动力学仿真与诊断研究

齿面点蚀是斜齿轮的主要故障之一。不同齿面及不同程度的点蚀故障诊断需要大量的实验数据支撑。提出一种仿真与实验相结合的方法，对斜齿轮齿面点蚀程度和数量进行故障识别。建立了单齿面点蚀和多齿面点蚀等不同点蚀故障类型的斜齿轮动力学模型，对动态响应信号进行包络谱分析，得到了不同点蚀故障类型时域和频域的特征响应规律。对齿轮接触疲劳实验振动信号进行特性分析，验证了斜齿轮点蚀故障动力学模型的准确性。结果表明，齿轮点蚀故障动力学模型可以对单齿面不同点蚀程度和多齿面点蚀进行诊断识别，为传动齿轮故障诊断与健康预测提供诊断依据。     

齿轮故障的混沌诊断识别方法

研究了Ｈｏｌｍ ｅｓ型Ｄｕｆｆｉｎｇ 振子的混沌及间歇混沌运动,发现参考信号与摄动信号间的微小角频率差是振子产生间歇混沌的原因。得出振子相变对与参考信号具有微小角频率差的小信号敏感,而对随机噪声和与参考信号角频率差较大的周期干扰信号具有免疫力的结论。通过辨别振子对的间歇混沌运动可对齿轮偏心及单齿缺陷故障进行诊断与识别。     

松动-裂纹耦合故障转子系统的非线性响应

基于现代非线性动力学和转子动力学理论,采用Newmark-β法和Poincaré映射对裂纹和一端支座松动耦合故障转子系统进行了数值模拟研究,给出了系统响应随转速比、松动质量和裂纹深度变化的分岔图,分析了一些主要参数变化的影响以及典型的Poincaré截面图。研究发现,系统存在拟周期环面破裂、阵发性分岔和多倍T周期运动失稳进入混沌三条混沌道路。这些结论为旋转机械的故障诊断提供了理论基础和参考。     

基于ADAMS的行星轮磨损故障仿真研究

以实体为例,提出了一种行星轮磨损故障诊断的方法,该方法利用SolidWorks三维建模软件建模出高精度的行星齿轮机构虚拟样机模型,并利用ADAMS软件进行动力学仿真,对其啮合频率及行星轮磨损故障频率进行分析,将仿真的结果与理论值对比,为行星齿轮机构磨损故障诊断提供精确的数据,利用此数据可以有效地避免因齿轮磨损故障造成的机械事故。     

考虑外部激励作用下的齿轮系统非线性动力学分析

研究了在外部激励作用下,齿轮传动系统参数对系统动力学的影响,为避免齿轮在混沌状态下工作提供了理论依据。它以单自由度直齿圆柱齿轮传动系统为研究对象,建立了非线性动力学模型,在模型中综合考虑了内部激励及和外部激励等因素。利用Runge)Kutta数值计算方法对系统的非线性动力学方程进行了求解,分析了齿轮系统在外部激励作用下,所表现出的周期和混沌运动状态,为齿轮的设计及制造和获得更好的性能提供了理论指导。     

The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.     

Evaluation of the strength,wear, and durability of a corrected cylindrical involute gearing,with due regard for the tooth engagement conditions

A method for calculating the wear and durability of helical gearings is used to investigate the effect of double–single–double tooth engagement on the maximum contact pressures, tooth wear, and durability of a corrected cylindrical involute gearing under constant tooth contact conditions. A technique is presented for determination of the angles of transition from the doubleto the single-tooth contact and vice versa. Gear tooth profile correction is found to reduce the maximum contact pressures by up to 20%. The teeth of the gear wheel reach their wear allowance faster than those of the pinion do. Depending on the degree of correction, the maximum wear occurs at different characteristic contact points: at the entry to the double-tooth mesh, at the entry to the single-tooth mesh, or at the exit out of the mesh. The durability of the gearing is found to be at its maximum at certain overlap ratios, its magnitude being observed to increase substantially (by up to 50%). The results obtained are presented in graphic form, which enables one to reveal the laws governing the effect of the engagement conditions.     

应用Hilbert-Huang变换的齿轮磨损故障诊断研究

提出了一种基于H ilbert-Huang变换的齿轮磨损故障诊断的新方法。H ilbert-Huang变换是先把时间序列信号,用经验模态分解方法分解成不同特征时间尺度的固有模态函数,然后经过H ilbert变换获得频谱的信号处理新方法。介绍了该方法的基本原理,并将H ilbert-Huang变换应用于齿轮箱中齿轮磨损故障诊断的研究,通过选取表征齿轮磨损故障的IM F分量进行边际谱和能量谱分析,就可提取齿轮故障振动信号的特征。齿轮故障实验信号的研究结果表明,H ilbert-Huang变换时频分析方法,能有效地诊断齿轮的磨损故障。     

齿面早期复合故障诊断

针对齿轮系统在复杂工况下轮齿表面早期复合故障难诊断的问题,分析了轮齿齿面不同故障引起的瞬时传动误差变化的规律,建立了含不同齿面故障的齿轮系统传动误差模型.根据齿轮系统传动误差信号的阶次特征,采用改进陷波滤波的方法,从传动误差信号中分离出轮齿故障引起的传动误差分量信号.经齿轮传动系统实验台实测传动误差信号实验验证,结果证明:传动误差信号信噪比高,轻载变速工况下,经陷波滤波分离的故障传动误差分量信号能够准确反映不同类型的齿面早期故障.     

Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester

To ensure the safety, continuity of production, make a reasonable maintenance plan, save the cost of maintenance for hydraulic tube tester, it is needed to quickly identify an assignable cause of a fault. This paper is concerned with early fault diagnosis of hydraulic pump which are the heart of hydraulic tube tester. Considering that the signal of the hydraulic pump early fault is a periodic weak signal, an intermittent chaos, sliding window symbol sequence statistics-based method is proposed to detect the early fault of one single piston loose shoes of hydraulic pump on a hydraulic tube tester. The approach presented is based on the insight that the phase transition of chaos oscillator, for example, the Duffing oscillator, is very sensitive to a periodic weak signal having little angular frequency difference with the referential signal of the oscillator. While observing the intermittent chaos phenomenon through figure is not easy for computer, a sliding window symbol sequence statistics is developed to realize real-time computer observation of this phenomenon. Rather more, this paper takes a trick to decreasing the computational complexity of the sliding window symbol sequence statistics method, also analyzes the influences of different window size, depths of the symbol tree on the information entropy. At last, a control limit is introduced to realize automatic early fault alarm. The resultant approach is experimented with data simulated from an AMESim model of hydraulic tube tester. The results indicate that the proposed approach is capable of detecting the signal of hydraulic pump early fault on hydraulic tube tester.     

基于混沌与模糊聚类的机械故障自动识别

针对大型设备旋转部件故障模式复杂难以识别的特点,给出一种基于混沌与模糊最大似然估计(Fuzzy maximum likelihood estimates,FMLE)聚类相结合的机械故障自动识别方法。利用混沌振子在非平衡相变对小信号非常敏感,而对噪声和高频信号具有强免疫力的特点,可检测出微弱的周期故障特征信号的频率信息,并将其作为故障特征矢量输入模糊聚类分类器进行聚类分析。同时针对传统的模糊C均值(Fuzzy center means,FCM)聚类算法只适用于球形或者类球形数集分布的缺陷,将基于最大似然估计的距离测度引入故障特征聚类中,实现对不同形状、大小和密度的故障数据集模糊聚类,达到对机械故障自动识别的效果。试验及工程实例结果证明了方法的有效性,同时证明FMLE聚类具有更好的聚类效果。     

Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS

The small size, low weight, and large transmission ratio of planetary gear have resulted in large-scale use, low speed, and heavy-duty mechanical systems. Poor working conditions of planetary gear lead to frequent occurrence of faults. A method is proposed for diagnosing faults in planetary gear based on fuzzy entropy of Local mean decomposition (LMD) and Adaptive neuro-fuzzy inference system (ANFIS). The original vibration signal is decomposed into six Product function (PF) components and a residual using LMD. Given that decomposed PF components contain the main fault feature information, fuzzy entropy is used to reflect the complexity and irregularity of each PF component. The fuzzy entropies of each PF component are defined as the input of the ANFIS model, and its parameters and membership functions are adaptively adjusted based on training samples. Finally, fuzzy inference rules are determined, and the optimal ANFIS model is obtained. Testing samples are used to verity the trained ANFIS model. The overall fault recognition rate reaches 88.8%, and the fault recognition rate for gear with wear reaches 96%. Therefore, the proposed method is effective at diagnosing planetary gear faults.