A study of bispectral analysis in gearbox condition monitoring is presented.The theory of bispectrum and quadratic phase coupling (QPC) is first introduced, and then equationsfor computing bispectrum slices are obtain...A study of bispectral analysis in gearbox condition monitoring is presented.The theory of bispectrum and quadratic phase coupling (QPC) is first introduced, and then equationsfor computing bispectrum slices are obtained. To meet the needs of online monitoring, a simplifiedmethod of computing bispectrum diagonal slice is adopted. Industrial gearbox vibration signalsmeasured from normal and tooth cracked conditions are analyzed using the above method. Experimentsresults indicate that bispectrum can effectively suppress the additive Gaussian noise andchracterize the QPC phenomenon. It is also shown that the 1-D bispectrum diagonal slice can capturethe non-Gaussian and nonlinear feature of gearbox vibration when crack occurred, hence, this methodcan be employed to gearbox real time monitoring and early diagnosis.展开更多
A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other com...A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.展开更多
Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early ...Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong back?ground noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non?Gaussianity and quadratic phase coupling(QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non?Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non?Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on?line monitoring and diagnosis of valve train faults.展开更多
Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new a...Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.展开更多
Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical ...Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.展开更多
The application ofbispectrum analysis in fault diagnosis o f gears is studied in this paper. Bispectrum analysis is capable of removing Gau ssian or symmetric non-Gaussian noise and providing more information than pow...The application ofbispectrum analysis in fault diagnosis o f gears is studied in this paper. Bispectrum analysis is capable of removing Gau ssian or symmetric non-Gaussian noise and providing more information than power spectrum analysis.The results of the research show that normal gear sig nals, cracked gear signals and broken gear signals can be easily distinguished b y using bispectrumas the signal features. The bispectrum diagonal slice B_x(ω_1,ω_2) can be used to identifythe gear condition automatically.展开更多
One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimen...One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimensional feature extraction subsystem shows low recognition rate for identifying an utterance speech signal under harsh noise conditions, we have developed a speaker identification system based on two-dimensional Bispectrum data that was theoretically more robust to the addition of Gaussian noise. As the processing sequence of ID-MFCC method could not be directly used for processing the two-dimensional Bispectrum data, in this paper we proposed a 2D-MFCC method as an extension of the 1D-MFCC method and the optimization of the 2D filter design using Genetic Algorithms. By using the 2D-MFCC method with the Bispectrum analysis method as the feature extraction technique, we then used Hidden Markov Model as the pattern classifier. In this paper, we have experimentally shows our developed methods for identifying an utterance speech signal buried with various levels of noise. Experimental result shows that the 2D-MFCC method without GA optimization has a comparable high recognition rate with that of 1D-MFCC method for utterance signal without noise addition. However, when the utterance signal is buried with Gaussian noises, the developed 2D-MFCC shows higher recognition capability, especially, when the 2D-MFCC optimized by Genetics Algorithms is utilized.展开更多
Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous...Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.展开更多
In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distort...In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.展开更多
【目的】相比直线振动信号,行星轮扭振信号不受行星轮通过效应和信号传递路径的影响,频谱结构更加简单。因此,基于扭振信号开展行星齿轮箱故障诊断有望得到更好的诊断结果。提出一种基于扭振信号调制信号双谱(Modulation Signal Bispect...【目的】相比直线振动信号,行星轮扭振信号不受行星轮通过效应和信号传递路径的影响,频谱结构更加简单。因此,基于扭振信号开展行星齿轮箱故障诊断有望得到更好的诊断结果。提出一种基于扭振信号调制信号双谱(Modulation Signal Bispectrum,MSB)分析的行星齿轮箱故障诊断新方法。【方法】首先,对编码器信号使用希尔伯特(Hilbert)变换方法求解瞬时转速信号;然后,对瞬时转速信号进行MSB分析,寻找最优载波频带;最后,对选取的最优载波频带构建MSB最优载波频带复合谱,并作为行星齿轮箱的故障诊断特征。【结果】试验结果表明,所提出方法可以更直观地反映行星轮的故障状态以及故障信息,验证了该方法在行星轮故障诊断方面的有效性和优越性。展开更多
在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊...在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊断方法。首先,使用FDM按照高频到低频的方式搜寻傅里叶固有模态函数分量(Fourier Intrinsic band Functions,FIBFs);以加权峭度指标作为评判标准,对信号进行重构,确保得到最佳的信号;然后对新的信号利用MSB分析方法进行解调处理,最终通过复合切片谱实现故障特征频率的提取。最后,通过上述方法对模拟信号和滚动轴承外圈故障信号进行分析,其研究结果表明:该方法能够有效地提取故障特征频率,并且与常规双谱进行对比,验证所提方法的优越性。展开更多
风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击...风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击信号的干扰大;覆盖多个往复运转行程的长信号不具有周期性,等等。为了解决上述问题,提出一种基于调制信号双谱(Modulation Signal Bispectrum,MSB)切片总体平均的低速往复运转轴承故障诊断方法。首先,利用转速跟踪过零点对振动信号进行信号重采样处理,并依据编码器信号从重采样信号中分离出单个行程的短信号集合;然后,对每一个短信号进行MSB分析,生成MSB的载波切片谱,根据载波切片谱寻找最优载波频率及其对应的调制信号切片谱;最后,对短信号集合的MSB调制信号切片谱进行总体平均,生成切片谱总体平均特征。故障试验数据验证结果表明,MSB切片总体平均特征能够有效诊断低速往复运转轴承的故障。展开更多
基金This project is supported by 95 Pan Deng Program of China (No.PD952l908) National Key Basic Research Special Foundation of China (No.Gl998020320)Provincial Natural Science Foundation of Hubei, China (No.2000J125)
文摘A study of bispectral analysis in gearbox condition monitoring is presented.The theory of bispectrum and quadratic phase coupling (QPC) is first introduced, and then equationsfor computing bispectrum slices are obtained. To meet the needs of online monitoring, a simplifiedmethod of computing bispectrum diagonal slice is adopted. Industrial gearbox vibration signalsmeasured from normal and tooth cracked conditions are analyzed using the above method. Experimentsresults indicate that bispectrum can effectively suppress the additive Gaussian noise andchracterize the QPC phenomenon. It is also shown that the 1-D bispectrum diagonal slice can capturethe non-Gaussian and nonlinear feature of gearbox vibration when crack occurred, hence, this methodcan be employed to gearbox real time monitoring and early diagnosis.
基金Supported by the National Natural Sciences Foundation of China (No. 50975213 and No. 50705070)Doctoral Fund for the New Teachers of Ministry of Education of China (No. 20070497029)the Program of Introducing Talents of Discipline to Universities (No. B08031)
文摘A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.
基金Supported by National Science and Technology Support Program of China(Grant No.2015BAF07B04)
文摘Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong back?ground noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non?Gaussianity and quadratic phase coupling(QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non?Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non?Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on?line monitoring and diagnosis of valve train faults.
文摘Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.
基金This work was supported in part by National Defence Science and Technology Foundation (413220402)
文摘Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.
文摘The application ofbispectrum analysis in fault diagnosis o f gears is studied in this paper. Bispectrum analysis is capable of removing Gau ssian or symmetric non-Gaussian noise and providing more information than power spectrum analysis.The results of the research show that normal gear sig nals, cracked gear signals and broken gear signals can be easily distinguished b y using bispectrumas the signal features. The bispectrum diagonal slice B_x(ω_1,ω_2) can be used to identifythe gear condition automatically.
文摘One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimensional feature extraction subsystem shows low recognition rate for identifying an utterance speech signal under harsh noise conditions, we have developed a speaker identification system based on two-dimensional Bispectrum data that was theoretically more robust to the addition of Gaussian noise. As the processing sequence of ID-MFCC method could not be directly used for processing the two-dimensional Bispectrum data, in this paper we proposed a 2D-MFCC method as an extension of the 1D-MFCC method and the optimization of the 2D filter design using Genetic Algorithms. By using the 2D-MFCC method with the Bispectrum analysis method as the feature extraction technique, we then used Hidden Markov Model as the pattern classifier. In this paper, we have experimentally shows our developed methods for identifying an utterance speech signal buried with various levels of noise. Experimental result shows that the 2D-MFCC method without GA optimization has a comparable high recognition rate with that of 1D-MFCC method for utterance signal without noise addition. However, when the utterance signal is buried with Gaussian noises, the developed 2D-MFCC shows higher recognition capability, especially, when the 2D-MFCC optimized by Genetics Algorithms is utilized.
文摘Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.
基金supported by the National Natural Science Foundation of China under Grant No.61671185 and 62071153.
文摘In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.
文摘【目的】相比直线振动信号,行星轮扭振信号不受行星轮通过效应和信号传递路径的影响,频谱结构更加简单。因此,基于扭振信号开展行星齿轮箱故障诊断有望得到更好的诊断结果。提出一种基于扭振信号调制信号双谱(Modulation Signal Bispectrum,MSB)分析的行星齿轮箱故障诊断新方法。【方法】首先,对编码器信号使用希尔伯特(Hilbert)变换方法求解瞬时转速信号;然后,对瞬时转速信号进行MSB分析,寻找最优载波频带;最后,对选取的最优载波频带构建MSB最优载波频带复合谱,并作为行星齿轮箱的故障诊断特征。【结果】试验结果表明,所提出方法可以更直观地反映行星轮的故障状态以及故障信息,验证了该方法在行星轮故障诊断方面的有效性和优越性。
文摘在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊断方法。首先,使用FDM按照高频到低频的方式搜寻傅里叶固有模态函数分量(Fourier Intrinsic band Functions,FIBFs);以加权峭度指标作为评判标准,对信号进行重构,确保得到最佳的信号;然后对新的信号利用MSB分析方法进行解调处理,最终通过复合切片谱实现故障特征频率的提取。最后,通过上述方法对模拟信号和滚动轴承外圈故障信号进行分析,其研究结果表明:该方法能够有效地提取故障特征频率,并且与常规双谱进行对比,验证所提方法的优越性。
文摘风电机组的偏航轴承和变桨轴承、航天发射塔架的回转支承轴承、起重机和挖掘机的转盘轴承等,都具有低速往复运转的特点。低速往复运转轴承的故障诊断极具挑战:低速工况下损伤接触的冲击力小,损伤冲击信号弱;减速换向冲击信号对故障冲击信号的干扰大;覆盖多个往复运转行程的长信号不具有周期性,等等。为了解决上述问题,提出一种基于调制信号双谱(Modulation Signal Bispectrum,MSB)切片总体平均的低速往复运转轴承故障诊断方法。首先,利用转速跟踪过零点对振动信号进行信号重采样处理,并依据编码器信号从重采样信号中分离出单个行程的短信号集合;然后,对每一个短信号进行MSB分析,生成MSB的载波切片谱,根据载波切片谱寻找最优载波频率及其对应的调制信号切片谱;最后,对短信号集合的MSB调制信号切片谱进行总体平均,生成切片谱总体平均特征。故障试验数据验证结果表明,MSB切片总体平均特征能够有效诊断低速往复运转轴承的故障。
