A Visualization Method for Synthesized Vibration Signal Spectrum

Abstract

This invention provides a method for visualizing the synthesized spectrum of vibration signals, comprising the following steps: First, the acceleration signal is integrated twice to obtain the velocity and displacement signals respectively; Fast Fourier Transform (FFT) is performed on the acceleration, velocity, and displacement signals to obtain the spectra of the three signals. Based on the spectra of the acceleration, velocity, and displacement signals, the cutoff frequency range of the synthesized spectrum is determined using the Akaike Information Criterion (AIC). The amplitudes of the three spectra are appropriately scaled according to the cutoff frequency range, finally yielding a synthesized spectrum containing information from the acceleration, velocity, and displacement signals. This invention overcomes the problem that analyzing a single high-frequency acceleration signal and low-frequency velocity and displacement signals makes it difficult to comprehensively capture spectral information when analyzing vibration signals. According to practical needs, the synthesized spectrum visualization provides a more comprehensive display of the spectral information of vibration signals, making vibration signal analysis more convenient and efficient.

Description

Technical Field

[0001] This invention relates to a method for visualizing the synthesized spectrum of vibration signals. It is a method that solves the problem that the spectrum of a single acceleration signal is difficult to fully capture spectral information, and enables more convenient and comprehensive analysis of vibration signal spectrum display based on the visualization of the synthesized spectrum. Background Technology

[0002] In mechanical vibration measurement, three parameters are involved: vibration acceleration, velocity, and displacement. From a practical perspective, the measurement of vibration velocity and displacement is easily limited by environmental conditions or sensor installation, making direct measurement impossible. Accelerometers, due to their small size, light weight, and ease of installation, have gained wider application. However, acceleration signals contain only a large amount of high-frequency information, while crucial frequency information such as rotational frequency is mainly concentrated in the low-frequency range. Therefore, many engineering applications involve detailed analysis of the low-, mid-, and high-frequency spectra, i.e., processing the spectrum of acceleration, velocity, and displacement signals to provide a more comprehensive representation of the spectral information. Summary of the Invention

[0003] This invention provides a method for visualizing the synthesized spectrum of vibration signals, overcoming the problem that analyzing only high-frequency acceleration signals and low-frequency velocity and displacement signals is insufficient to comprehensively capture spectral information. Based on practical needs, this invention provides a more comprehensive display of the spectral information of vibration signals through synthesized spectrum visualization, making vibration signal analysis more convenient and efficient.

[0004] The technical solution adopted in this invention is as follows: a method for visualizing the synthesized spectrum of vibration signals, the method comprising the following steps:

[0005] (1) The bearing acceleration signal obtained by the five-cylinder water pump is used as the analysis object. The acceleration signal is integrated to obtain the velocity signal, and the displacement signal is obtained by double integration.

[0006] (2) Perform Fast Fourier Transform (FFT) on the three signals of acceleration, velocity and displacement to obtain the spectrum of the three signals;

[0007] (3) Based on the spectrum of the three signals, the frequency cutoff range of the three signals is determined using the Akaike Information Criterion (AIC);

[0008] The displacement signal determines the low-frequency cutoff range of the synthesized spectrum, the velocity signal determines the mid-frequency cutoff range of the synthesized spectrum, and the acceleration signal determines the high-frequency cutoff range.

[0009] (4) Based on the cutoff frequency range of the three signals, multiply the amplitude of the three signals in the corresponding cutoff frequency range by the corresponding scaling factor, adjust the amplitude of the synthesized spectrum in different frequency ranges of low frequency, medium frequency and high frequency, and finally add the spectrum of the three signals after amplitude scaling point by point to visualize the synthesized spectrum.

[0010] As a further aspect of the present invention, the data acquisition parameters in step (1) are: the sampling unit is m / s. 2 The sampling rate is 51.2 kHz and the sampling duration is 10 seconds.

[0011] As a further aspect of the present invention, in step (1), the acceleration signal is integrated to obtain a velocity signal with the unit mm / s. Based on the unit m / g of the acceleration signal, the amplitude of the integrated velocity signal is multiplied by a coefficient of 1000. The displacement signal is obtained by a second integration with the unit mm, and the amplitude of the displacement signal is multiplied by a coefficient of 1000.

[0012] As a further aspect of the present invention, when using the Akaike Information Criterion (AIC) to determine the cutoff frequencies of the three signal spectra in step (3), the spectra of the three signals are first inverted, and a characteristic function (CF) is introduced into the spectra. The purpose is to amplify the difference in amplitude of the inverted spectra. The AIC equation can be used to quickly search for locations where the difference in amplitude of the spectrum is obvious.

[0013] As a further aspect of the present invention, when determining the cutoff frequencies of the three signal spectra using the Akaike Information Criterion (AIC) in step (3), the window width parameter of the AIC needs to be determined based on the amplitude of the spectrum. The length of the window width is set with the start point of the inverted spectrum and the end point at the maximum value of the inverted spectrum. In step (3), by combining the characteristic function of the spectrum and the AIC function, the cutoff range of the three signal frequencies can be adaptively calculated, thus determining the frequency band range of the three signals.

[0014] As a further embodiment of the present invention, the scaling factor of the cutoff frequency band in step (4) is set such that the scaling factor for low frequency is 1.0, the scaling factor for mid-frequency and high-frequency bands is 0.5, and the scaling factor for high frequency and above is 0.01.

[0015] The beneficial effects of this invention are: it overcomes the problem that analyzing single high-frequency signals of acceleration and low-frequency signals of velocity and displacement is insufficient to comprehensively capture spectral information when analyzing vibration signals. Based on actual needs, it provides a more comprehensive display of the spectral information of vibration signals through synthesized spectral visualization, making vibration signal analysis more convenient and efficient. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the present invention;

[0017] Figure 2 This is the time-domain waveform of acceleration in this invention;

[0018] Figure 3 This is the velocity time-domain waveform of the present invention;

[0019] Figure 4 This is the displacement time-domain waveform of the present invention;

[0020] Figure 5 The three signal spectra of this invention;

[0021] Figure 6 This is a flowchart for determining the cutoff frequency of the three signals in this invention;

[0022] Figure 7 The spectrum of the three signals after inversion according to the present invention;

[0023] Figure 8 The characteristic functions of the spectra of the three inverted signals in this invention;

[0024] Figure 9 Determine the cutoff frequencies for the three signals of this invention;

[0025] Figure 10 This is a synthesized spectrum diagram of the present invention;

[0026] Figure 11 This is a comparison between the synthesized spectrum and the acceleration spectrum of this invention. Detailed Implementation

[0027] The present invention will be further described below with reference to embodiments, but the scope of protection of the present invention is not limited thereto:

[0028] Example 1: As Figures 1-11 As shown, a method for visualizing the synthesized spectrum of vibration signals is presented. In this example, the bearing acceleration signal obtained from a five-cylinder water pump is used as vibration data, with the sampling unit being m / s². 2 The sampling rate is 51200Hz and the sampling duration is 10s.

[0029] The overall process of this invention is as follows: Figure 1 As shown, the specific implementation steps include the following:

[0030] Step 1: Integrate the acceleration signal to obtain the velocity signal, and perform a second integration to obtain the displacement signal; Figure 2 , Figure 3 , Figure 4 These are the time-domain waveforms of acceleration, velocity, and displacement, respectively.

[0031] (1) The integration process is as follows:

[0032]

[0033]

[0034] Where a(t) is the acceleration signal, and v(t) and s(t) are the integrated velocity and displacement signals, respectively.

[0035] (2) The velocity signal obtained by integrating the acceleration signal is in mm / s. Based on the unit of the acceleration signal, m / s... 2 The amplitude of the integrated velocity signal is multiplied by a coefficient of 1000. The displacement signal, in mm, is obtained by double integration, and its amplitude is multiplied by a coefficient of 1000.

[0036] Step 2: Perform Fast Fourier Transform (FFT) on the acceleration, velocity, and displacement signals to obtain their spectra, such as... Figure 5 As shown.

[0037] Step 3: Flowchart for determining the cutoff frequency for three signals as follows Figure 6 As shown; the spectra of the three signals are inverted, and the results are as follows. Figure 7 As shown, an eigenfunction (CF) is introduced into the inverted spectrum, such as... Figure 8 As shown, the purpose is to amplify the difference in amplitude of the inverted spectrum in order to accurately obtain the cutoff frequency; and to determine the frequency cutoff range of the three signals using the Akaike Information Criterion (AIC). The results of calculating the cutoff frequencies for the three signals are as follows. Figure 9 As shown; where the displacement signal determines the low-frequency cutoff range of the synthesized spectrum, the velocity signal determines the mid-frequency cutoff range of the synthesized spectrum, and the acceleration signal determines the high-frequency cutoff range.

[0038] (1) The process of introducing the characteristic function is as follows:

[0039] CF(i) = x(i) 2 +[x(i)-x(i-1)] 2

[0040] Where CF(i) represents the characteristic function of the inverted spectrum, x(i) represents the inverted spectrum, and i represents the i-th sample of the inverted spectrum.

[0041] (2) The process of determining the frequency cutoff range of the three signals using the Akaike Information Criterion (AIC) is as follows:

[0042] AIC(f) = flog 10 (var{CF(1:f)})+(Tf-1)log 10 (var{(CF(f:T))})

[0043] Where var{CF} represents the variance of the characteristic function sequence CF, and T is the window width of the selected sequence. In this example, the window width T is the starting point of the inverted spectrum and the ending point is the maximum value of the inverted spectrum. The AIC function divides the sequence into two vectors, {CF(1:f)} and {CF(f:T)}. When the AIC function reaches its minimum point f, point f is the cutoff frequency range of the signal.

[0044] Step 4: Based on the cutoff frequency ranges of the three signals, multiply the amplitudes of the three signals within their respective cutoff frequency ranges by the corresponding scaling factors. Adjust the amplitude of the synthesized spectrum in different frequency ranges (low, mid, and high). Finally, sum the amplitude-scaled spectra of the three signals point by point to visualize the synthesized spectrum. The low-frequency coefficient is set to 1.0, the mid-frequency and high-frequency coefficients are 0.5, and the coefficients for frequencies above high are 0.1. See the final result below. Figure 10 The comparison between the synthesized spectrum and the original acceleration signal spectrum is shown in the figure. Figure 11 The synthesized spectrum clearly demonstrates the low-frequency information of the signal. This low-frequency information, such as rotational frequency and other related low-frequency parameters, facilitates further fault diagnosis of the water injection pump bearing.

Claims

1. A method of visualizing a vibration signal composite spectrum, characterized in that, The method comprises the following steps: (1) using the obtained bearing acceleration signal as the analysis object, integrating the acceleration signal to obtain a velocity signal, and performing secondary integration to obtain a displacement signal; (2) performing fast Fourier transform (FFT) on the acceleration, velocity and displacement signals to obtain the frequency spectra of the three signals; (3) determining the frequency cutoff ranges of the three signals using the Akaike information criterion (AIC) according to the frequency spectra of the three signals; wherein the displacement signal determines the low-frequency cutoff range of the synthesized frequency spectrum, the velocity signal determines the medium-frequency cutoff range of the synthesized frequency spectrum, and the acceleration signal determines the high-frequency cutoff range; (4) multiplying the amplitudes of the three signals within the corresponding cutoff frequency ranges by the corresponding scaling coefficients according to the cutoff frequency ranges of the three signals, adjusting the amplitudes of the synthesized frequency spectrum in different frequency ranges, and finally adding the three signal spectra after amplitude scaling point by point to visualize the synthesized frequency spectrum.

2. The method of visualizing a vibration signal composite spectrum according to claim 1, characterized in that, The data acquisition parameters in step (1) are: sampling unit is m / s 2 , sampling rate is 51.2 kHz.

3. The method of visualizing a vibration signal composite spectrum according to claim 1, wherein, The unit of the velocity signal obtained by integrating the acceleration signal in step (1) is mm / s. According to the unit m / s of the acceleration signal 2 , the amplitude of the integrated velocity signal is multiplied by a coefficient 1000, and the displacement signal is obtained by twice integration, the unit of which is mm, and the amplitude of the displacement signal is multiplied by a coefficient 1000.

4. The method of visualizing a vibration signal composite spectrum according to claim 1, wherein, In step (3), when determining the cutoff frequencies of the three signal spectra using the Akaike information criterion (AIC), first invert the frequency spectra of the three signals, and introduce a characteristic function (CF) to the frequency spectrum, which aims to amplify the amplitude differences of the inverted frequency spectrum. The AIC equation can quickly search for positions with obvious amplitude changes.

5. The method of visualizing a vibration signal composite spectrum according to claim 1, wherein, In step (3), when determining the cutoff frequencies of the three signal spectra using the Akaike information criterion (AIC), the window width parameter of AIC needs to be determined according to the amplitude of the frequency spectrum; wherein the length of the window width is set as the starting point of the inverted frequency spectrum, and the maximum value of the inverted frequency spectrum is set as the endpoint.

6. The method of visualizing a vibration signal composite spectrum according to claim 1, wherein, In step (4), the scaling coefficients of the cutoff frequency bands are set, wherein the coefficient of the low-frequency band is 1.0, the coefficients of the medium-frequency band and the high-frequency band are 0.5, and the coefficient above the high-frequency band is 0.01.

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