齿轮箱复合故障诊断方法研究
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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。
Gearbox is an indispensible power transmission component, whose working condition will directly affect the performance of the whole mechanical equipment. Therefore, the research on the fault diagnosis technology for gearbox is of great significance to the normal operation of mechanical equipment. Extracting fault feature information from the gearbox's vibration signal by using various kinds of signal processing methods has been the key to the fault diagnosis for gearbox.
Hundreds of engineering practices show that there is usually more than one fault in a mechanical unit, which demonstrate as compound faults. The compound faults in different positions,, with different failure modes and degrees have different effects onto the mechanical equipment. Besides, the interaction and the mutual interference among the different fault components, especially the overlap of fault features under changing rotating speed situation, bring great challenge to the comprehensive fault diagnosis of mechanical equipment, thus, the compound fault diagnosis has become a hard problem in the fault diagnosis of mechanical equipments. Aiming at the problems above, sponsored by National Natural Science Foundation (project number:51275161) and Hunan Science and Technology Plan (project number:2012SK3184), taking gearbox as the research object, modern signal processing methods as the research tool, and compound fault diagnosis as the research target, the dissertation has carried on a profound and systematic research mainly on the fault features separation and the fault characteristics extraction from the vibration signal of a rotating speed changing gearbox with compound faults.
The main research work and innovative achievements of the dissertation are as follows
(1) On the basis of failure ratio analysis among the components in gearbox, the failure modes, reasons, appearances and vibration mechanism of the main failure components-gears and rolling bearings are analyzed, and the local fault vibration signal model of gears and rolling bearings have been set up. The study shows that, when a local fault occurs in a gear, there will be AM-FM component in the vibration signal, whereas, when a local fault occurs in rolling bearings, there will be periodic impulse component with damped oscillation.
(2) Aiming at the separation of gear fault component and bearing fault component and the extraction of fault modulation information from the vibration signal of a gearbox with compound faults, a compound fault diagnosis method for gearbox based on morphological component analysis (MCA) and energy operator demodulation is proposed. Firstly, the fault components of gears and bearings are separated by MCA; then, energy operator demodulation is carried onto the separated fault components of gears and bearings to extract the fault modulation information. Algorithm simulation and application examples show that the fault component separation, followed by the energy operator demodulating, can effectively highlight the fault characteristic of each fault component.
(3) Aiming at the separation and extraction of fault characteristics from the vibration signal of a rotation speed changing gearbox with compound faults, a compound fault diagnosis method for gearbox based on MCA and order tracking is proposed. Firstly, each fault component is separated by MCA; then, the separated fault components are transformed to angular domain signals through even angle resampling; finally, Hilbert envelop demodulation will be carried out onto each resampled fault component to extract the fault modulation information. By algorithm simulation and application examples, the local faults of gears and rolling bearings are analyzed. The results show that the proposed method can effectively separate the fault characteristics of gears and rolling bearings from the vibration signal of a gearbox under the condition of rotating speed changing.
(4) Aiming at the unsuitableness of cyclostationary demodulating method in extracting fault modulation information from the vibration signal of a rotating speed changing gearbox with compound faults, a compound fault diagnosis method for gearbox based on chirplet path pursuit (CPP) and cyclostationary demodulating is proposed. Firstly, the rotating speed is estimated adaptively from the vibration signal by using CPP; then, with the estimated rotating speed, even angle resampling is carried on to the vibration signal; finally, the resampled angular domain signal is analyzed with cyclostationary demodulating to extract the fault modulation information. Algorithm simulation and application examples show that the proposed method, without the presence of a tachometer, can effectively extract the fault modulation information from the vibration signal of a rotating speed changing gearbox with compound faults.
(5) When rotating speed is changing dramatically, analyzing gearbox vibration signal with ensemble empirical mode decomposition (EEMD) will result in mode confusion. Aiming at this problem, a compound fault diagnosis method for gearbox based on CPP and EEMD is proposed. Firstly, rotating speed information is extracted from the vibration signal of a rotating speed changing gearbox with compound faults using CPP; secondly, according to the extracted rotating speed, the vibration signal is resampled with even angle to obtain its angular domain signal; then, the intrinsic mode function (IMF) components can be acquired by the EEMD analysis to the angular domain signal, and the IMF component containing fault information is selected in terms of the correlation coefficient for IMF component and angular signal; finally, Hilbert envelop spectrum analysis is carried on to the IMF component selected to extract each fault modulation information. Algorithm simulation and application examples show that, under the condition of rotating speed changing, the proposed method can extract fault characteristics from the vibration signal of a gearbox with compound faults effectively.
Currently, the compound fault diagnosis has been a hard problem in the fault diagnosis of mechanical equipments. This dissertation, taking gearbox as the research object, makes an exhaustive study on the fault diagnosis of gearbox with compound faults consisting of gear and rolling bearing local failures under constant rotating speed and changing rotating speed. Algorithm simulation and application examples indicate that the combination methods, obtained by combining MCA with energy operator demodulating, CPP, order tracking, cyclostationary demodulating, etc, take complementary advantage of each single signal processing method, and can effectively extract and separate the compound faults characteristics from the gearbox vibration signal. Therefore, the combination methods have a brilliant application prospect in the fault diagnosis of mechanical equipments.
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。
Gearbox is an indispensible power transmission component, whose working condition will directly affect the performance of the whole mechanical equipment. Therefore, the research on the fault diagnosis technology for gearbox is of great significance to the normal operation of mechanical equipment. Extracting fault feature information from the gearbox's vibration signal by using various kinds of signal processing methods has been the key to the fault diagnosis for gearbox.
Hundreds of engineering practices show that there is usually more than one fault in a mechanical unit, which demonstrate as compound faults. The compound faults in different positions,, with different failure modes and degrees have different effects onto the mechanical equipment. Besides, the interaction and the mutual interference among the different fault components, especially the overlap of fault features under changing rotating speed situation, bring great challenge to the comprehensive fault diagnosis of mechanical equipment, thus, the compound fault diagnosis has become a hard problem in the fault diagnosis of mechanical equipments. Aiming at the problems above, sponsored by National Natural Science Foundation (project number:51275161) and Hunan Science and Technology Plan (project number:2012SK3184), taking gearbox as the research object, modern signal processing methods as the research tool, and compound fault diagnosis as the research target, the dissertation has carried on a profound and systematic research mainly on the fault features separation and the fault characteristics extraction from the vibration signal of a rotating speed changing gearbox with compound faults.
The main research work and innovative achievements of the dissertation are as follows
(1) On the basis of failure ratio analysis among the components in gearbox, the failure modes, reasons, appearances and vibration mechanism of the main failure components-gears and rolling bearings are analyzed, and the local fault vibration signal model of gears and rolling bearings have been set up. The study shows that, when a local fault occurs in a gear, there will be AM-FM component in the vibration signal, whereas, when a local fault occurs in rolling bearings, there will be periodic impulse component with damped oscillation.
(2) Aiming at the separation of gear fault component and bearing fault component and the extraction of fault modulation information from the vibration signal of a gearbox with compound faults, a compound fault diagnosis method for gearbox based on morphological component analysis (MCA) and energy operator demodulation is proposed. Firstly, the fault components of gears and bearings are separated by MCA; then, energy operator demodulation is carried onto the separated fault components of gears and bearings to extract the fault modulation information. Algorithm simulation and application examples show that the fault component separation, followed by the energy operator demodulating, can effectively highlight the fault characteristic of each fault component.
(3) Aiming at the separation and extraction of fault characteristics from the vibration signal of a rotation speed changing gearbox with compound faults, a compound fault diagnosis method for gearbox based on MCA and order tracking is proposed. Firstly, each fault component is separated by MCA; then, the separated fault components are transformed to angular domain signals through even angle resampling; finally, Hilbert envelop demodulation will be carried out onto each resampled fault component to extract the fault modulation information. By algorithm simulation and application examples, the local faults of gears and rolling bearings are analyzed. The results show that the proposed method can effectively separate the fault characteristics of gears and rolling bearings from the vibration signal of a gearbox under the condition of rotating speed changing.
(4) Aiming at the unsuitableness of cyclostationary demodulating method in extracting fault modulation information from the vibration signal of a rotating speed changing gearbox with compound faults, a compound fault diagnosis method for gearbox based on chirplet path pursuit (CPP) and cyclostationary demodulating is proposed. Firstly, the rotating speed is estimated adaptively from the vibration signal by using CPP; then, with the estimated rotating speed, even angle resampling is carried on to the vibration signal; finally, the resampled angular domain signal is analyzed with cyclostationary demodulating to extract the fault modulation information. Algorithm simulation and application examples show that the proposed method, without the presence of a tachometer, can effectively extract the fault modulation information from the vibration signal of a rotating speed changing gearbox with compound faults.
(5) When rotating speed is changing dramatically, analyzing gearbox vibration signal with ensemble empirical mode decomposition (EEMD) will result in mode confusion. Aiming at this problem, a compound fault diagnosis method for gearbox based on CPP and EEMD is proposed. Firstly, rotating speed information is extracted from the vibration signal of a rotating speed changing gearbox with compound faults using CPP; secondly, according to the extracted rotating speed, the vibration signal is resampled with even angle to obtain its angular domain signal; then, the intrinsic mode function (IMF) components can be acquired by the EEMD analysis to the angular domain signal, and the IMF component containing fault information is selected in terms of the correlation coefficient for IMF component and angular signal; finally, Hilbert envelop spectrum analysis is carried on to the IMF component selected to extract each fault modulation information. Algorithm simulation and application examples show that, under the condition of rotating speed changing, the proposed method can extract fault characteristics from the vibration signal of a gearbox with compound faults effectively.
Currently, the compound fault diagnosis has been a hard problem in the fault diagnosis of mechanical equipments. This dissertation, taking gearbox as the research object, makes an exhaustive study on the fault diagnosis of gearbox with compound faults consisting of gear and rolling bearing local failures under constant rotating speed and changing rotating speed. Algorithm simulation and application examples indicate that the combination methods, obtained by combining MCA with energy operator demodulating, CPP, order tracking, cyclostationary demodulating, etc, take complementary advantage of each single signal processing method, and can effectively extract and separate the compound faults characteristics from the gearbox vibration signal. Therefore, the combination methods have a brilliant application prospect in the fault diagnosis of mechanical equipments.
引文
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