强噪声背景下滚动轴承故障诊断的关键技术研究
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摘要
旋转机械工况监测是系统维护和自动化生产中的一个重要环节,滚动轴承故障是造成旋转机械损坏最主要因素之一,有时还会带来灾难性的生产事故。因此,开展滚动轴承故障诊断,尤其是对早期故障诊断研究具有重要的理论价值和实际意义。实践表明：对于具有调制现象的滚动轴承故障诊断,基于Hilbert变换的包络解调方法是一种可靠的诊断方法。本文以强噪声背景下滚动轴承故障诊断为目标,针对故障特征提取的问题提出了几种包络解调的新方法,’主要研究工作如下：
在滚动轴承的故障机理方面,完善了现有滚动轴承振动模型,建立了综合考虑轴承元件表面波纹度、局部损伤、径向游隙等因素的轴承振动模型。由于生产工艺的原因,轴承元件表面不可避免的存在波纹度,为使模型更符合滚动轴承的实际工作情况,在模拟轴承损伤故障振动信号时必须考虑波纹度等其它因素的影响。通过实验分析模拟轴承的振动特性,证明该模型是有效的。
从信号分析角度看,Morlet小波和Harmonic小波是复解析带通滤器,常用于振动信号的包络解调。但是,它们在时域或频域都存在自身的不足,为此,构造了一种复解析带通滤波器,不但克服了Harmonic小波Gibbs的现象,还具有几乎“盒形”的频谱特征、时域衰减较快的特性,综合性能优于二者。在此基础上,提出了一种基于组合复解析带通滤波器的梳状包络解调方法,实现了信号梳状滤波和包络解调的统一。实验结果表明该方法具有抗噪声、容错能力强的优点,可获得信号的简明解调谱特征。
S变换(S-Transform, ST)是短时Fourier变换和小波变换的一种延伸和扩展,可用于提取信号的包络。为了更有效的分析具有周期性调幅特征的轴承损伤振动信号,提出了多分辨率ST包络谱的概念,并给出了其计算方法。为了进一步抑制带内噪声,采用了一种基于多分辨率ST包络谱和奇异值比谱的方法,自动提取、重构调制信号包络的主周期分量,由于采用了改进的相空间矩阵重构方法,使获取的主周期分量频率更准确。实验结果证明,该方法可以很好的降低噪声,有效地提取信号中的周期成分,在不同强度背景噪声下,实现了轴承故障特征频率的提取。
与各种抑制噪声方法相比,随机共振能够利用噪声来增强信号,使其在微弱信号的增强和检测方面有着独特的优势。在随机共振系统的数值解法方面,提出了一种改进的数值解法以增强共振效果；在系统参数优化方面,设计了一种基于遗传算法同时优化系统参数和噪声强度的自适应随机共振方法；针对经典的绝热近似小参数随机共振难以满足实际工程大参数条件下的微弱信号检测问题,深入研究了变尺度、移频—变尺度等大参数信号随机共振实现方法,提出了一种更适用于调幅信号的大参数随机共振系统,即S变换—变尺度随机共振。实验结果证明,该方法对于微弱周期调制信号的解调分析效果明显优于FFT谱分析和常用解调方法,可用于强噪声背景下提取轴承故障特征。
通过对模拟和实测轴承故障振动信号的实验分析,证明了以上所提出包络解调新方法的有效性以及某些方面独特的优越性。有理由相信,这些方法在滚动轴承故障诊断方面有着良好的应用前景。
The condition monitoring of rotating machinery is important in terms of system maintenance and process automation. Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. Such bearing failure can be catastrophic in certain situations such as in automatic processing machines. So it is very significant to research on bearing condition monitoring and fault diagnosis techniques, especially for bearing incipient fault. The practice shows that the envelope demodulation method based on Hilbert transformation offers a reliable method for the bearing fault diagnosis with modulation phenomenon. Aiming to address bearing fault diagnosis in heavy noise, new envelope demodulation methods were explored in this dissertation. The main contributions are described as follows:
In terms of fault mechanism of bearing, dynamic behaviors of rolling element bearings with surface waviness, local defects, radial clearances and other factors considered were further perfected based on some existing models. Since any bearing has surface waviness to a certain degree due to the manufacturing process, surface waviness and other factors should be taken into account in simulating bearing local defects vibration signals, and this was well coincide with the. real-life situations. The accordance of vibration properties obtained from the mathematical model with those from the experimental data verified the validity of the proposed model.
In the viewpoint of signal analysis, Morlet wavelet and Harmonic wavelet, in nature, belong to the complex analytical band-pass filter, and are usually used to extract vibration signal envelopes. Aiming at the insufficiency on Morlet wavelet and Harmonic wavelet, a novel complex analytical band-pass filter was constructed with the Gibbs phenomenon occurring slightly, the characteristics of nearly box on frequency domain, and rapid decay on time domain. On this basis, a comb-filter and envelope-demodulator method based on combining complex analytical band-pass filters was proposed. This technique integrates comb-filtering and envelope-demodulating. The experiment results show that a clear, noiseproof and fault-tolerant envelope spectrum could be obtained with the proposed method.
The S-transform (ST), as extension to the ideas of the short time Fourier transform and the wavelet transform, could be applied in the envelope demodulation. Multi-resolution ST envelope spectrum was proposed to analyze bearing vibration signal with cycle-modulating feature efficiently, and the calculation method was presented correspondingly. In order to restrain noise within pass-band, the novel method for detection, enhancement and reconstruction of principal periodic component in envelope based on Multi-resolution ST envelope spectrum and singular value ratio (SVR) spectrum was adopted. Owing to the improved approach was employed in reconstructing the matrix of singular value decomposition (SVD), the precision frequency determination was enhanced. It was shown that the new de-noising method could reduce the noise and extract the period of the signal effectively, and could be effectively applied in the vibration envelope extraction for a roller bearing system in different noise level.
In comparison with other suppression noise methods, because signals could be strengthened by noise in stochastic resonance (SR) system, the SR effect has particular advantages on enhancing and detecting weak signals. In terms of numerical solution for a SR model, an improved solution based on a fourth order Runge-Kutta algorithm was presented to enhance the resonance effect. However, the optimization of system parameter and noise intensity is complicated and difficult in a SR system. A novel genetic adaptive SR algorithm, used to optimize system parameter and noise intensity, was presented here. Aiming at traditional adiabatic elimination SR in small parameters is not adapt to engineering weak signal detection in large parameters, based on the researches of re-scaling, frequency-shifted and re-scaling methods, a method based on the S-transform and rescaling was presented for amplitude modulated signal detection. The effectiveness of the proposed method was demonstrated on both simulation signals and real vibration signals of bearing. It was shown to be superior to the spectrum analysis and the common envelope demodulation analysis. The method could extract bearing fault feature in heavy noise.
It was shown that the proposed envelope-demodulation methods in this dissertation were demonstrated on their validity and superiority to the common envelope demodulation methods. It's worth reling that they would have a promising application for the fault diagnosis of rolling element bearings.
在滚动轴承的故障机理方面,完善了现有滚动轴承振动模型,建立了综合考虑轴承元件表面波纹度、局部损伤、径向游隙等因素的轴承振动模型。由于生产工艺的原因,轴承元件表面不可避免的存在波纹度,为使模型更符合滚动轴承的实际工作情况,在模拟轴承损伤故障振动信号时必须考虑波纹度等其它因素的影响。通过实验分析模拟轴承的振动特性,证明该模型是有效的。
从信号分析角度看,Morlet小波和Harmonic小波是复解析带通滤器,常用于振动信号的包络解调。但是,它们在时域或频域都存在自身的不足,为此,构造了一种复解析带通滤波器,不但克服了Harmonic小波Gibbs的现象,还具有几乎“盒形”的频谱特征、时域衰减较快的特性,综合性能优于二者。在此基础上,提出了一种基于组合复解析带通滤波器的梳状包络解调方法,实现了信号梳状滤波和包络解调的统一。实验结果表明该方法具有抗噪声、容错能力强的优点,可获得信号的简明解调谱特征。
S变换(S-Transform, ST)是短时Fourier变换和小波变换的一种延伸和扩展,可用于提取信号的包络。为了更有效的分析具有周期性调幅特征的轴承损伤振动信号,提出了多分辨率ST包络谱的概念,并给出了其计算方法。为了进一步抑制带内噪声,采用了一种基于多分辨率ST包络谱和奇异值比谱的方法,自动提取、重构调制信号包络的主周期分量,由于采用了改进的相空间矩阵重构方法,使获取的主周期分量频率更准确。实验结果证明,该方法可以很好的降低噪声,有效地提取信号中的周期成分,在不同强度背景噪声下,实现了轴承故障特征频率的提取。
与各种抑制噪声方法相比,随机共振能够利用噪声来增强信号,使其在微弱信号的增强和检测方面有着独特的优势。在随机共振系统的数值解法方面,提出了一种改进的数值解法以增强共振效果；在系统参数优化方面,设计了一种基于遗传算法同时优化系统参数和噪声强度的自适应随机共振方法；针对经典的绝热近似小参数随机共振难以满足实际工程大参数条件下的微弱信号检测问题,深入研究了变尺度、移频—变尺度等大参数信号随机共振实现方法,提出了一种更适用于调幅信号的大参数随机共振系统,即S变换—变尺度随机共振。实验结果证明,该方法对于微弱周期调制信号的解调分析效果明显优于FFT谱分析和常用解调方法,可用于强噪声背景下提取轴承故障特征。
通过对模拟和实测轴承故障振动信号的实验分析,证明了以上所提出包络解调新方法的有效性以及某些方面独特的优越性。有理由相信,这些方法在滚动轴承故障诊断方面有着良好的应用前景。
The condition monitoring of rotating machinery is important in terms of system maintenance and process automation. Rolling element bearing failure is one of the foremost causes of breakdown in rotating machinery. Such bearing failure can be catastrophic in certain situations such as in automatic processing machines. So it is very significant to research on bearing condition monitoring and fault diagnosis techniques, especially for bearing incipient fault. The practice shows that the envelope demodulation method based on Hilbert transformation offers a reliable method for the bearing fault diagnosis with modulation phenomenon. Aiming to address bearing fault diagnosis in heavy noise, new envelope demodulation methods were explored in this dissertation. The main contributions are described as follows:
In terms of fault mechanism of bearing, dynamic behaviors of rolling element bearings with surface waviness, local defects, radial clearances and other factors considered were further perfected based on some existing models. Since any bearing has surface waviness to a certain degree due to the manufacturing process, surface waviness and other factors should be taken into account in simulating bearing local defects vibration signals, and this was well coincide with the. real-life situations. The accordance of vibration properties obtained from the mathematical model with those from the experimental data verified the validity of the proposed model.
In the viewpoint of signal analysis, Morlet wavelet and Harmonic wavelet, in nature, belong to the complex analytical band-pass filter, and are usually used to extract vibration signal envelopes. Aiming at the insufficiency on Morlet wavelet and Harmonic wavelet, a novel complex analytical band-pass filter was constructed with the Gibbs phenomenon occurring slightly, the characteristics of nearly box on frequency domain, and rapid decay on time domain. On this basis, a comb-filter and envelope-demodulator method based on combining complex analytical band-pass filters was proposed. This technique integrates comb-filtering and envelope-demodulating. The experiment results show that a clear, noiseproof and fault-tolerant envelope spectrum could be obtained with the proposed method.
The S-transform (ST), as extension to the ideas of the short time Fourier transform and the wavelet transform, could be applied in the envelope demodulation. Multi-resolution ST envelope spectrum was proposed to analyze bearing vibration signal with cycle-modulating feature efficiently, and the calculation method was presented correspondingly. In order to restrain noise within pass-band, the novel method for detection, enhancement and reconstruction of principal periodic component in envelope based on Multi-resolution ST envelope spectrum and singular value ratio (SVR) spectrum was adopted. Owing to the improved approach was employed in reconstructing the matrix of singular value decomposition (SVD), the precision frequency determination was enhanced. It was shown that the new de-noising method could reduce the noise and extract the period of the signal effectively, and could be effectively applied in the vibration envelope extraction for a roller bearing system in different noise level.
In comparison with other suppression noise methods, because signals could be strengthened by noise in stochastic resonance (SR) system, the SR effect has particular advantages on enhancing and detecting weak signals. In terms of numerical solution for a SR model, an improved solution based on a fourth order Runge-Kutta algorithm was presented to enhance the resonance effect. However, the optimization of system parameter and noise intensity is complicated and difficult in a SR system. A novel genetic adaptive SR algorithm, used to optimize system parameter and noise intensity, was presented here. Aiming at traditional adiabatic elimination SR in small parameters is not adapt to engineering weak signal detection in large parameters, based on the researches of re-scaling, frequency-shifted and re-scaling methods, a method based on the S-transform and rescaling was presented for amplitude modulated signal detection. The effectiveness of the proposed method was demonstrated on both simulation signals and real vibration signals of bearing. It was shown to be superior to the spectrum analysis and the common envelope demodulation analysis. The method could extract bearing fault feature in heavy noise.
It was shown that the proposed envelope-demodulation methods in this dissertation were demonstrated on their validity and superiority to the common envelope demodulation methods. It's worth reling that they would have a promising application for the fault diagnosis of rolling element bearings.
引文
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