齿轮箱复合故障诊断方法研究

发布时间：2018-04-28 02:17

本文选题：齿轮箱 + 复合故障　；参考：《湖南大学》2013年博士论文

【摘要】：齿轮箱是机械设备中必不可少的动力传输部件,其运行状态将直接影响到整个机械设备能否正常工作,因此,研究齿轮箱故障诊断技术对保障机械设备的正常运行具有重要意义。采用各种信号处理方法从齿轮箱振动信号中提取故障特征信息是齿轮箱故障诊断的关键。大量工程实践表明,机械设备中的故障通常不止一处,往往表现为复合故障。不同部位、不同形式、不同程度的复合故障会对机械设备产生不同的影响,且各故障成分相互影响、彼此干扰,特别是在转速变化情况下,故障特征相互重叠,给机械设备故障的全面诊断带来了挑战,因此机械设备的复合故障诊断是当前故障诊断的难点。针对上述问题,本文在国家自然科学基金项目(项目编号：51275161)和湖南省科技计划(项目编号：2012SK3184)的资助下,以齿轮箱为研究对象,以现代信号处理方法为研究手段,以复合故障诊断为研究目标,重点对变转速齿轮箱复合故障振动信号中的故障特征成分分离和故障特征提取进行了深入系统地研究。论文的主要研究工作和创新性成果有 (1)在分析齿轮箱中各零部件失效比重的基础上,对其主要失效部件一齿轮和滚动轴承的失效形式、失效原因、失效表现及振动机理进行了分析,并建立了齿轮和滚动轴承的局部故障振动信号模型。研究表明,当齿轮出现局部故障时,其振动信号中会产生调幅调频成分,而当滚动轴承出现局部故障时,其振动信号中会产生周期性的振荡衰减冲击成分。 (2)针对齿轮箱复合故障振动信号中齿轮故障成分和轴承故障成分的分离和故障调制信息的提取问题,提出了基于形态分量分析(Morphological component analysis, MCA)与能量算子解调的齿轮箱复合故障诊断方法。该方法先用MCA方法分离齿轮箱复合故障振动信号中的齿轮故障成分和轴承故障成分；再对分离后的齿轮故障成分和轴承故障成分进行能量算子解调分析,以提取两成分中的故障调制信息。利用该方法对包含齿轮和滚动轴承局部故障的齿轮箱复合故障振动信号进行了算法仿真和应用实例分析,分析结果表明,对齿轮箱复合故障振动信号中的各故障成分进行分离后,再进行能量算子解调分析,可有效凸显各故障特征。 (3)针对变转速齿轮箱复合故障振动信号中的故障特征提取与分离问题,提出了基于MCA与阶次跟踪的变转速齿轮箱复合故障诊断方法。该方法先用MCA方法分离变转速齿轮箱复合故障振动信号中的各故障成分；再对分离后的各故障成分进行等角度重采样,将其转变为角域信号；最后对重采样后的各故障成分进行Hilbert包络解调分析,以提取各故障调制信息。通过算法仿真和应用实例对变转速下的齿轮局部故障和滚动轴承局部故障进行了分析,结果表明,该方法可有效分离变转速下的齿轮和滚动轴承故障特征。 (4)针对循环平稳解调方法不适合提取变转速齿轮箱复合故障振动信号中故障调制信息的问题,提出了基于线调频小波路径追踪(Chirplet path pursuit, CPP)与循环平稳解调的齿轮箱复合故障诊断方法。该方法先用CPP方法自适应地从变转速齿轮箱复合故障振动信号中估计出转速信息；再依据该转速信息对信号进行等角度重采样；最后对重采样后的角域信号进行循环平稳解调分析,以提取信号中的故障调制信息。利用该方法对变转速下的齿轮箱复合故障振动信号进行了算法仿真和应用实例分析,结果表明,该方法可在无转速计的情况下有效提取变转速齿轮箱复合故障振动信号中的故障调制信息。 (5)在转速大范围变化情况下,用EEMD方法分析齿轮箱振动信号会产生模态混淆。针对这一问题,提出了基于CPP与EEMD的齿轮箱复合故障诊断方法,并将其应用于变转速下的齿轮箱复合故障诊断中。该方法先用CPP方法从变转速齿轮箱复合故障振动信号中提取转速信息；然后依据该转速信息对变转速齿轮箱复合故障振动信号进行等角度重采样,获取其角域信号；再对角域信号进行EEMD分解,获取各IMF分量,并根据各IMF分量与角域信号的相关系数选取包含故障信息的IMF分量；最后对选取的IMF分量进行Hilbert包络解调分析,以提取各故障调制信息。算法仿真和应用实例表明,该方法可有效地提取变转速齿轮箱复合故障振动信号中的故障特征。机械设备的复合故障诊断是目前机械故障诊断领域的一大难点。本文以齿轮箱为研究对象,对其恒定转速和变转速下的齿轮和滚动轴承复合故障振动信号进行分析。算法仿真和应用实例表明,将MCA、能量算子解调、CPP、阶次跟踪、循环平稳解调等方法相结合,可弥补单一信号分析方法在诊断复合故障时的不足,以实现优势互补,具有良好的应用前景。
[Abstract]:Gear box is an essential power transmission component in mechanical equipment . Its operation state will directly affect the normal operation of the whole mechanical equipment . Therefore , it is very important to study the gear box fault diagnosis technology to guarantee the normal operation of mechanical equipment .

A large number of engineering practice shows that the fault in mechanical equipment is usually more than one place and often appears as a composite fault . Different parts , different forms and different degrees of composite faults have different effects on the mechanical equipment , and the fault features overlap each other . In particular , under the condition of rotating speed , the fault features overlap each other . Therefore , the complex fault diagnosis of the mechanical equipment is the research object , and the fault feature component separation and fault feature extraction in the composite fault vibration signal of the variable speed gearbox are studied systematically .

The main research work and innovative results of the thesis are as follows :

( 1 ) On the basis of analyzing the failure proportion of each component in the gear box , the failure mode , the failure reason , the failure performance and the vibration mechanism of the gear and the rolling bearing of the main failure part are analyzed , and the local fault vibration signal model of the gear and the rolling bearing is established .

( 2 ) Aiming at the problem of the separation of gear fault components and the fault modulation information in the composite fault vibration signal of the gear box , a complex fault diagnosis method of gear box based on morphological component analysis ( MCA ) and energy operator demodulation is proposed .
In this paper , the fault component and the fault component of the bearing are demodulated and analyzed to extract the fault modulation information in the two components . By using the method , the complex fault vibration signal of the gear box containing the local fault of the gear and the rolling bearing is simulated and analyzed . The results show that after the fault components in the composite fault vibration signal of the gear box are separated , the energy operator demodulation analysis can be carried out , and the fault characteristics can be effectively highlighted .

( 3 ) Aiming at fault feature extraction and separation in complex fault vibration signal of variable speed gear box , a complex fault diagnosis method of variable speed gear box based on MCA and order tracking is put forward .
carrying out equal - angle resampling on the separated fault components , and converting the fault components into angular domain signals ;
and finally , Hilbert envelope demodulation analysis is carried out on each fault component after re - sampling so as to extract the fault modulation information .

The local faults of gears and the local faults of the rolling bearing are analyzed by the algorithm simulation and the application example . The results show that the method can effectively separate the fault features of gears and rolling bearings under the variable rotation speed .

( 4 ) A complex fault diagnosis method based on line frequency modulation wavelet path tracking ( CPP ) and cyclostationary demodulation is proposed .
re - sampling the signal according to the rotation speed information ;
The method can effectively extract the fault - modulated information in the complex fault vibration signal of the variable - speed gear box under the condition of no tachometer .

( 5 ) In the case of a large rotating speed range , the modal confusion can be generated by using the EEMD method to analyze the vibration signal of the gear box . According to this problem , a gear box composite fault diagnosis method based on CPP and EEMD is proposed , and the method is applied to the complex fault diagnosis of gear box under variable rotation speed .
then carrying out equal angle resampling on the composite fault vibration signal of the variable rotating speed gearbox according to the rotating speed information , and obtaining the angular domain signal thereof ;
performing EEMD decomposition on the re - diagonal domain signals to obtain each IMF component , and selecting the IMF component containing fault information according to the correlation coefficient of each IMF component and the angular domain signal ;
Finally , Hilbert envelope demodulation analysis is carried out on the selected IMF component to extract fault modulation information . The algorithm simulation and application examples show that the method can effectively extract fault features in the composite fault vibration signal of the variable speed gearbox .

The composite fault diagnosis of mechanical equipment is one of the most difficult problems in the field of mechanical fault diagnosis .

【学位授予单位】：湖南大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：TH165.3

【参考文献】