【作者】 李林；

【导师】 王利明；

【作者基本信息】 重庆大学 ， 机械（专业学位）， 2024， 硕士

【摘要】 齿轮传动是一种典型的动力传输系统,在各种领域都承担着十分重要的作用,得益于其稳定的动力传输特性,从航天、船舶等大型装备到各种小型精密器械中均有广泛的应用。然而,齿轮传动系统中的旋转零部件在一些环境恶劣、高速重载的运行条件下极易发生故障,随着这些旋转零部件的失效或者损坏,可能会直接导致整个设备的瘫痪。因此,齿轮传动系统的故障诊断一直是学术界的研究热点,由于齿轮和轴承早期故障引起的振动冲击十分微弱,故障特征信号很容易被传动系统中的设备运转和背景噪声所掩盖,加大了从振动加速度信号中提取出故障特征的难度。研究齿轮传动系统中强噪声干扰消除,实现早期微弱故障特征的增强和提取,能极大的提升齿轮传动系统的故障诊断能力和效率,便于相关人员及时发现设备的安全隐患,具有重要的工程价值和理论意义。本文围绕齿轮传动系统中齿轮和轴承的早期微弱故障信号的消噪算法以及特征提取开展研究,提出了一种变步长多分辨率子带自适应滤波方法,对齿轮传动系统中的早期微弱故障信号进行消噪,通过故障仿真信号和实际故障信号验证了所提算法的有效性,并基于该算法开发了一套齿轮传动系统健康监测系统。论文的主要工作为:(1)针对传统自适应滤波算法在多频带尺度噪声的消除中收敛性差、消噪能力下降的问题,建立了一种多分辨率子带自适应滤波算法,该方法基于子带滤波器组的自适应滤波方法,通过串连多个长度不一的自适应滤波器对噪声进行消除,提高了传统自适应滤波算法对多尺度噪声消除的能力,并改善了算法的收敛性能。此外,通过构建系统识别模型,验证了多分辨率子带自适应滤波算法在模型中收敛速度、稳态误差的优越表现。(2)针对齿轮传动系统中非平稳、多尺度噪声下故障特征的提取问题,仿真建立了齿轮和轴承的故障信号,并在故障信号中加入不同比例的随机噪声、有色噪声,对比验证了传统自适应滤波算法和本文所提出的多分辨率子带自适应滤波算法的噪声消除能力,以及不同信噪比条件下的消噪性能提升效果,证明了本文所提消噪算法的有效性和优越性。(3)搭建了齿轮传动系统故障实验平台,通过植入齿轮故障和轴承故障,获取了齿轮故障信号(齿面剥落、齿根裂纹)和轴承故障信号(轴承外圈剥落),对比了传统消噪算法和本文所提算法在实际旋转零部件故障信号中噪声的消除效果以及故障特征增强能力,证明了多分辨率子带自适应滤波方法在齿轮传动系统中噪声消除的可行性,并且较其余传统算法的故障冲击包络特征提升明显。(4)基于所提的多分辨率子带自适应滤波方法建立了一套齿轮传动系统运行状态监测系统,并将其应用于轻轨转向架传动系统的健康监测中,实现了振动信号的采集和多分辨率自适应消噪,完成了轻轨转向架传动系统的运行状态评价。更多还原

【Abstract】 Gear transmission is a typical power transmission system,plays a very important role in various fields,thanks to its stable power transmission characteristics,from aerospace,ships and other large equipment to a variety of small precision instruments have a wide range of applications.However,the rotating parts in the gear transmission system are prone to failure under some harsh environment,high-speed and heavy load operating conditions,and the failure or damage of these rotating parts may directly lead to the paralysis of the entire equipment.Therefore,the fault diagnosis of gear transmission system has always been a research hotspot in the academic circle.Because the vibration and shock caused by the early failure of gear and bearing are very weak,the characteristic signal generated by the fault shock is easily covered by the equipment operation and background noise in the transmission system,which increases the difficulty of extracting fault characteristics from the vibration acceleration signal.Research on the elimination of strong noise interference in the gear transmission system and the enhancement and extraction of weak fault features can greatly improve the fault diagnosis ability and efficiency of the gear transmission system,and facilitate the relevant personnel to timely discover the safety risks of the equipment,which has important engineering value and theoretical significance.In this paper,the noise reduction algorithm and feature extraction of early weak fault signals of gears and bearings in gear transmission system are studied,and a variable step size multi-resolution subband adaptive filtering method is proposed to denoise early weak fault signals in gear transmission system.The effectiveness of the proposed algorithm is verified by fault simulation signals and actual fault signals.Based on this algorithm,a gear transmission system health monitoring system is developed.The main work of this paper is as follows:(1)Aiming at the problems of poor convergence and reduced noise cancellation ability of traditional adaptive filtering algorithms in the elimination of multi-band noise,a multi-resolution subband adaptive filtering algorithm is established.Based on the adaptive filtering method of subband filter banks,multiple adaptive filters of different lengths are connected to eliminate noise.The ability of the traditional adaptive filtering algorithm to eliminate multi-scale noise is improved,and the convergence performance of the algorithm is improved.In addition,by constructing the system identification model,the superior performance of the multi-resolution subband adaptive filtering algorithm in the convergence speed and steady-state error of the model is verified.(2)Aiming at the problem of fault feature extraction under non-stationary and multi-scale noise of gear transmission system,the fault signal of gear and bearing is simulated and established,and different proportions of random noise and colored noise are added to the fault signal,and the noise elimination ability of traditional adaptive filtering algorithm and multi-resolution subband adaptive filtering algorithm proposed in this paper is compared and verified.As well as the improvement effect of noise reduction performance under different SNR conditions,it proves the effectiveness and superiority of the noise reduction algorithm proposed in this paper.(3)A gear transmission system fault experiment platform was built,and the gear fault signals(tooth surface peeling,root crack)and bearing fault signals(bearing outer ring fault)were obtained by implanting the gear fault and bearing fault fault.The noise elimination effect and fault feature enhancement capability of the traditional noise reduction algorithm and the proposed algorithm in the actual rotating component fault signal were compared.It is proved that the multi-resolution subband adaptive filtering method is feasible to eliminate noise in gear transmission system,and the fault shock envelope characteristics are improved significantly compared with other traditional algorithms.(4)Based on the proposed multi-resolution subband adaptive filtering method,a gear transmission system operating condition monitoring system is established,and it is applied to the health monitoring of light rail bogie transmission system.The acquisition of vibration signals and multi-resolution adaptive noise reduction are realized,and the operating condition evaluation of light rail bogie transmission system is completed.更多还原