WO2020057066A1 - 基于增强调制双谱分析的滚动轴承故障诊断方法 - Google Patents

基于增强调制双谱分析的滚动轴承故障诊断方法 Download PDF

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WO2020057066A1
WO2020057066A1 PCT/CN2019/077955 CN2019077955W WO2020057066A1 WO 2020057066 A1 WO2020057066 A1 WO 2020057066A1 CN 2019077955 W CN2019077955 W CN 2019077955W WO 2020057066 A1 WO2020057066 A1 WO 2020057066A1
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rolling bearing
vibration signal
msb
noise reduction
fault
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PCT/CN2019/077955
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French (fr)
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甄冬
郭俊超
谷丰收
张琛
张�浩
师占群
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河北工业大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis

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  • the invention relates to the technical field of mechanical equipment condition monitoring and fault diagnosis, and in particular to a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis.
  • Rolling bearings are the most widely used mechanical parts in rotating machinery and one of the most vulnerable components.
  • vibration signals of rotating machinery a large number of signals are non-stationary and non-Gaussian distribution signals, especially when faults occur.
  • traditional power spectrum analysis and time-frequency analysis cannot reflect the phase information between frequency components, and generally cannot handle non-minimum phase systems and non-Gaussian signals.
  • Modulation bispectral analysis is used to analyze non-stationary and non-Gaussian signals. Powerful tool. MSB makes up for the shortcomings of second-order statistics that do not contain phase information and has modulation characteristics. Therefore, it is easier to obtain useful fault characteristic information by modulating bispectral vibration signals.
  • MSB can completely suppress Gaussian noise and is powerless to non-Gaussian noise.
  • the existence of these non-Gaussian noises interferes with the higher-order spectrum of the signal, which adversely affects the extraction and analysis of fault features.
  • the mechanical fault signal often contains various noises, and the signal to noise ratio of the signal is generally low, especially when the machine has an early fault, the fault signal is very weak. How to effectively extract the fault characteristic information from the strong noise background directly affects The accuracy of fault diagnosis and the reliability of early fault prediction.
  • an autoregressive (AR) model and MSB are combined to propose a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis.
  • This research idea is derived from the respective characteristics of the two signal analysis methods.
  • the AR model can effectively deal with the non-Gaussian noise in the signal, and the MSB analysis suppresses the Gaussian noise.
  • the technical solution of the present invention to solve the technical problem is to design a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis, and the specific steps are as follows:
  • Step 1 measuring the vibration signal of the detected rolling bearing through a vibration sensor
  • Step 2 Perform an AR model on the obtained vibration signal to perform noise reduction processing to obtain a noise reduction vibration signal x (t);
  • Step 3 Separate the modulation component of the noise reduction vibration signal x (t) with MSB to extract the characteristic frequency of the fault;
  • the present invention has the following beneficial effects:
  • Embodiment 1 is a time-domain waveform diagram of a vibration signal of an inner ring of a rolling bearing according to Embodiment 1;
  • FIG. 4 is a frequency domain diagram of a rolling bearing fault diagnosis method of the rolling bearing inner ring according to the embodiment 1 using the enhanced modulation bispectrum analysis of the present invention
  • FIG. 5 is a frequency domain diagram obtained by using the MSB for the vibration signal of the inner ring of the rolling bearing of Embodiment 1.
  • FIG. 5 is a frequency domain diagram obtained by using the MSB for the vibration signal of the inner ring of the rolling bearing of Embodiment 1.
  • the invention provides a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis.
  • the specific steps are as follows:
  • Step 1 measuring the vibration signal of the detected rolling bearing through a vibration sensor
  • Step 2 Perform an AR model on the obtained vibration signal to perform noise reduction processing to obtain a noise reduction vibration signal x (t);
  • the step two specifically includes the following steps:
  • Step 101 Determine an appropriate order range of the AR model.
  • the general signal can be taken within 100.
  • Step 103 Compare the kurtosis values of the vibration signals calculated at different orders to find the maximum kurtosis value, and the corresponding order is the optimal order to be determined, and then obtain the noise reduction vibration signal x (t);
  • Step 3 Separate the modulation component of the noise reduction vibration signal x (t) with MSB to extract the characteristic frequency of the fault;
  • the step three specifically includes the following steps:
  • Step 104 In the frequency domain, the MSB of the noise reduction vibration signal x (t) expressed in the form of a discrete Fourier transform X (f) can be defined as:
  • B MS (f c , f x ) represents the bispectrum of the signal x (t)
  • E ⁇ > represents the expectation
  • f c is the modulation frequency
  • f x is the carrier frequency
  • (f c + f x ) and (f c- f x ) are the upper and lower sideband frequencies, respectively.
  • Step 104 MSB of the resulting improvements to modify the carrier frequency f c by eliminating substantial influence component, in order to more accurately quantify sideband amplitude.
  • the improved MSB is MSB-SE, which is defined as follows:
  • Step 106 Calculate the average value of MSB in the f x increment direction to obtain f c slice:
  • ⁇ f represents the resolution of f x .
  • Step 107 Calculate the average value of multiple optimal MSB slices to obtain the fault characteristic frequency of the rolling bearing, which is expressed as:
  • N is the total number of selected f c slices.
  • Step 1 The vibration signal of the inner ring of the rolling bearing is measured by a vibration sensor.
  • the sampling frequency of the vibration signal is 96kHz, the sampling length is 2.880000, and the frequency of the bearing outer ring failure is 65.17Hz.
  • the waveform and frequency domain diagrams of the vibration signal are shown in Figures 1 and 2, respectively. It can be seen that there is a lot of noise and the component of the fault characteristic frequency cannot be extracted.
  • Step 2 Use the principle of maximum kurtosis to adaptively determine the optimal order of the AR model, as shown in Figure 3. By selecting the order of the best AR model, the vibration model is denoised to obtain noise reduction. Vibration signal
  • the third step the noise reduction vibration signal is subjected to MSB separation and modulation components, and the characteristic frequency of the fault is extracted to obtain a frequency domain diagram as shown in FIG. Accurately extracted the fault feature information of the rolling bearing outer ring.
  • the vibration signals of the inner ring of the rolling bearing in Example 1 are compared using MSB.
  • the structure obtained using MSB is shown in Figure 5.
  • the spectrum and noise are mixed, and the effects of harmonics still exist.
  • the method designed by the invention can obtain more accurate results in the diagnosis of rolling bearing faults, and is suitable for popularization and application.

Abstract

一种基于增强调制双谱分析的滚动轴承故障诊断方法，针对调制双谱分析从理论上仅能抑制高斯噪声，但对非高斯噪声无能为力的不足提出，具体为：首先，通过振动传感器测量被检测滚动轴承的振动信号；其次，对所得的振动信号进行AR模型降噪处理，得到降噪振动信号；最后，将降噪振动信号进行MSB分离调制成分，提取故障特征频率。该增强调制双谱分析的滚动轴承故障诊断方法，能够有效地提取强背景噪声中有故障轴承的微弱特征信息，有利于发现轴承的早期故障。

Description

基于增强调制双谱分析的滚动轴承故障诊断方法 技术领域
本发明涉及机械设备状态监测和故障诊断技术领域，具体是基于增强调制双谱分析的滚动轴承故障诊断方法。
背景技术
滚动轴承是旋转机械中应用最为广泛的机械零件，也是最容易损坏的元件之一。在旋转机械振动信号中，大量的信号是非平稳和非高斯分布的信号，尤其是在发生故障时更是如此。然而，传统的功率谱分析以及时频分析不能反应频率成分间的相位信息，通常也就无法处理非最小相位系统和非高斯信号，而调制双谱分析(MSB)是分析非平稳和非高斯信号的有力工具。MSB弥补了二阶统计量不包含相位信息的缺陷并且具有调制特性，因此用调制双谱振动信号更容易获得有用的故障特征信息。但是，MSB理论上能完全抑制高斯噪声，对非高斯类噪声缺无能为力，而这些非高斯类噪声的存在对信号的高阶谱造成干扰，从而对故障特征的提取和分析造成不利影响。而机械故障信号中往往含有各种噪声，信号的信噪比一般较低，尤其是机器发生早期故障时，其故障信号非常微弱，如何从强噪声背景中有效地提取出故障特征信息，直接影响着故障诊断的准确性以及故障早期预报的可靠性。
发明内容
为了解决上述问题，将自回归(AR)模型和MSB相结合，提出 了基于增强调制双谱分析的滚动轴承故障诊断方法。这一研究思路中来源于两种信号分析方法的各自特点，AR模型可以有效处理信号中存在的非高斯噪声，而MSB分析抑制高斯噪声。
本发明解决所述技术问题的技术方案是，设计基于增强调制双谱分析的滚动轴承故障诊断方法，其具体步骤如下：
步骤一：通过振动传感器测量被检测滚动轴承的振动信号；
步骤二：对所得的振动信号进行AR模型进行降噪处理，得到降噪振动信号x(t)；
步骤三：将降噪振动信号x(t)进行MSB分离调制成分，提取故障特征频率；
与现有技术相比，本发明有益效果在于：
(1)鉴于MSB的不足，其对高斯噪声具有良好的不敏感性但不能避免非高斯噪声的干扰，使用AR模型来改善MSB的性能。
(2)考虑到AR模型阶数直接影响到AR的降噪性能，利用峭度最大原则自适应地的确定AR模型最优阶数。
(3)实验分析结果表明，增强的调制双谱分析方法在提取故障特征方面具有优于MSB的优越性能，对滚动轴承故障诊断具有可行性和有效性。
附图说明
图1为实施例1的滚动轴承内圈的振动信号时域波形图；
图2为实施例1的滚动轴承内圈的振动信号的频域图；
图3为实施例1的滚动轴承内圈的振动信号的AR模型最优阶次 及相应的最大峭度值；
图4为实施例1的滚动轴承内圈的振动信号采用本发明增强调制双谱分析的滚动轴承故障诊断方法所得的频域图；
图5为实施例1的滚动轴承内圈的振动信号采用MSB所得的频域图。
具体实施方式
下面给出本发明的具体实施例。具体实施例仅用于进一步详细说明本发明，不限制本申请权利要求的保护范围。
本发明提供基于增强调制双谱分析的滚动轴承故障诊断方法，其具体步骤如下：
步骤一：通过振动传感器测量被检测滚动轴承的振动信号；
步骤二：对所得的振动信号进行AR模型进行降噪处理，得到降噪振动信号x(t)；
所述步骤二具体包括如下步骤：
步骤101：确定AR模型的一个合适的阶次范围，事实上一般信号取到100以内都可以。
步骤102：利用最小二乘法确定对应阶次下AR模型的加权a i(i＝1，2，...，p),利用此参数和阶次对应的AR模型对振动信号进行预处理，然后计算得到相应的峭度值；
步骤103：对不同阶次下计算得到的振动信号峭度值进行比较，找到最大峭度值，对应的阶次即是需要确定的最优阶次，进而得到降 噪振动信号x(t)；
步骤三：将降噪振动信号x(t)进行MSB分离调制成分，提取故障特征频率；
所述步骤三具体包括如下步骤：
步骤104：在频域中，以离散傅立叶变换X(f)的形式表示的降噪振动信号x(t)的MSB可以被定义为：
B MS(f c，f x)＝E<X(f c+f x)X(f c-f x)X *(f c)X *(f c)>
其中B MS(f c，f x)表示信号x(t)的双谱，E<>表示期望，f c为调制频率，f x为载波频率，(f c+f x)和(f c-f x)分别为上、下边带频率。
步骤105：对步骤104所得的MSB进行改善，通过消除实质影响来修改载波频率f c分量，以便更精确地量化边带幅度。改进后的MSB为MSB-SE，定义如下：
Figure PCTCN2019077955-appb-000001
其中B MS(f c，0)表示f x＝0时的平方功率谱。
步骤106：计算在f x增量方向上MSB的平均值，以得到f c切片：
Figure PCTCN2019077955-appb-000002
其中Δf表示f x的分辨率。
步骤107：计算多个最优的MSB切片的平均值，即得滚动轴承的故障特征频率，其表示为：
Figure PCTCN2019077955-appb-000003
其中N是选定的f c切片的总数。
实施例1
本实施例提供基于增强调制双谱分析的滚动轴承故障诊断方法：
第一步：通过振动传感器测量滚动轴承内圈的振动信号，振动信号的采样频率为96kHz，采样长度为点2880000，轴承外圈故障频率为65.17Hz。振动信号的波形图和频域图分别如图1、图2所示，可以看出存在着大量的噪声而且无法提取出故障特征频率的成分。
第二步：利用峭度最大原则自适应地的确定AR模型最优阶数，如图3所示；通过选取最佳AR模型的阶数，对振动信号进行AR模型进行降噪，得到降噪振动信号；
第三步：将降噪振动信号进行MSB分离调制成分，提取故障特征频率，得到如图4所示频域图，主要频率是65.17Hz等多倍频，与计算的外圈故障特征频率吻合，准确的提取了滚动轴承外圈故障特征信息。
为了充分证明本发明基于增强调制双谱分析的滚动轴承故障诊断的优越性，将实施例1中的滚动轴承内圈的振动信号采用MSB来进行对比。采用MSB所得的结构如图5所示。从图5中可以看到，频谱与噪声混合，谐波的影响仍然存在。而本发明设计的方法能够在滚动轴承故障的诊断中获得更准确的结果，适于推广应用。
本发明未述及之处适用于现有技术。

Claims (3)

  1. 基于增强调制双谱分析的滚动轴承故障诊断方法，其特征在于，其具体步骤如下：
    步骤一：通过振动传感器测量被检测滚动轴承的振动信号；
    步骤二：对所得的振动信号进行AR模型进行降噪处理，得到降噪振动信号x(t)；
    步骤三：将降噪振动信号x(t)进行MSB分离调制成分，提取故障特征频率。
  2. 根据权利要求1所述的基于增强调制双谱分析的滚动轴承故障诊断方法，其特征在于，所述步骤二具体包括如下步骤：
    步骤101：确定AR模型的一个合适的阶次范围；
    步骤102：利用最小二乘法确定对应阶次下AR模型的加权a i(i＝1，2，...，p),利用此参数和阶次对应的AR模型对振动信号进行预处理，然后计算得到相应的峭度值；
    步骤103：对不同阶次下计算得到的振动信号峭度值进行比较，找到最大峭度值，对应的阶次即是需要确定的最优阶次，进而得到降噪振动信号x(t)。
  3. 根据权利要求1所述的基于增强调制双谱分析的滚动轴承故障诊断方法，其特征在于，所述步骤三具体包括如下步骤：
    步骤104：在频域中，以离散傅立叶变换X(f)的形式表示的降噪振动信号x(t)的MSB可以被定义为：
    B MS(f c，f x)＝E＜X(f c+f x)X(f c-f x)X *(f c)X *(f c)＞
    其中B MS(f c，f x)表示信号x(t)的双谱，E<>表示期望，f c为调制频 率，f x为载波频率，(f c+f x)和(f c-f x)分别为上、下边带频率；
    步骤105：对步骤104所得的MSB进行改善，通过消除实质影响来修改载波频率f c分量，以便更精确地量化边带幅度；改进后的MSB为MSB-SE，定义如下：
    Figure PCTCN2019077955-appb-100001
    其中B MS(f c，0)表示f x＝0时的平方功率谱；
    步骤106：计算在f x增量方向上MSB的平均值，以得到f c切片：
    Figure PCTCN2019077955-appb-100002
    其中Δf表示f x的分辨率；
    步骤107：计算多个最优的MSB切片的平均值，即得滚动轴承的故障特征频率，其表示为：
    Figure PCTCN2019077955-appb-100003
    其中N是选定的f c切片的总数。
PCT/CN2019/077955 2018-09-19 2019-03-13 基于增强调制双谱分析的滚动轴承故障诊断方法 WO2020057066A1 (zh)

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CN113391244A (zh) * 2021-06-13 2021-09-14 河海大学 一种基于vmd的变压器合闸振动信号特征频率计算方法
CN113391244B (zh) * 2021-06-13 2024-01-12 河海大学 一种基于vmd的变压器合闸振动信号特征频率计算方法

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