A method and device for evaluating the lubrication status of rotating equipment based on vibration analysis

Abstract

The present invention discloses a method and device for evaluating the lubrication status of a rotating device based on vibration analysis, and relates to the field of mechanical equipment status testing, comprising: obtaining original waveform data of a rotating device to be tested in an operating state based on an acceleration sensor, and removing a DC component from the original waveform data to obtain an input signal; calculating a spectrum average energy diagram based on the input signal to obtain a center frequency and a frequency band bandwidth of a candidate frequency band for poor lubrication; if the center frequency is greater than a preset frequency threshold, calculating the energy proportion of a poor candidate frequency band of the rotating device to be tested based on the center frequency and the frequency band bandwidth of the candidate frequency band for poor lubrication; if the energy proportion of the candidate frequency band for poor lubrication is higher than a preset energy proportion threshold of the candidate frequency band for poor lubrication, calculating an effective background noise lift feature value in the candidate frequency band for poor lubrication based on the input signal, and if the effective background noise lift feature value is greater than the preset effective background noise lift feature threshold, determining that the rotating device to be tested has poor lubrication.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment status testing, and in particular to a method and device for evaluating the lubrication status of rotating equipment based on vibration analysis. Background Art

[0002] Equipment lubrication is an indispensable part of the operation of mechanical equipment. It plays a vital role in reducing friction, reducing wear and increasing equipment life. In the actual operation process, poor lubrication is an important cause of mechanical equipment failure. Therefore, timely and accurate diagnosis of poor equipment lubrication is of great significance for preventing failures and ensuring the normal operation of equipment.

[0003] At present, a traditional bearing lubrication status monitoring method limits the frequency band of poor bearing lubrication to 2000-3000 Hz. Although poor bearing lubrication can cause energy prominence and bottom lifting in the high-frequency band of the spectrum, this feature may not be limited to a specific frequency range, but may be reflected in the entire high-frequency band or multiple frequency bands. There is also a monitoring method that determines the bearing lubrication status by the magnitude of the acceleration value. Although acceleration measurement can be used as an indicator to determine the bearing lubrication status, it is difficult to draw an accurate conclusion based on this single factor alone. Other factors, such as other fault types, bearing load, speed, and working environment, may affect the acceleration measurement results. In short, the current rotating equipment lubrication status assessment methods are difficult to accurately achieve precise assessments. Summary of the invention

[0004] The present invention provides a method and device for evaluating the lubrication status of a rotating device based on vibration analysis, so as to solve the above-mentioned problem.

[0005] On the one hand, the present invention provides a method for evaluating the lubrication state of a rotating device based on vibration analysis, the method comprising the following steps: step S1: obtaining original waveform data of the rotating device to be tested under the operating state based on an acceleration sensor, and removing the DC component from the original waveform data to obtain an input signal; step S2: calculating a spectral average energy diagram based on the input signal to obtain the center frequency and frequency band bandwidth of a candidate frequency band for poor lubrication; wherein the spectral average energy diagram divides the signal into different frequency bands, and is used to display the energy distribution of the signal in different frequency bands, so as to facilitate the selection of a candidate frequency band for poor lubrication; step S3: if the center frequency of the candidate frequency band for poor lubrication is greater than a preset frequency threshold, calculating the energy proportion of the poor candidate frequency band of the rotating device to be tested based on the center frequency and frequency band bandwidth of the candidate frequency band for poor lubrication; step S4: if the energy proportion of the candidate frequency band for poor lubrication is higher than a preset threshold value of the energy proportion of the candidate frequency band for poor lubrication, calculating the effective background noise lift feature value in the candidate frequency band for poor lubrication based on the input signal, and if the effective background noise lift feature value is greater than the preset effective background noise lift feature threshold, determining that the rotating device to be tested has poor lubrication.

[0006] In one implementation of the present invention, in step S1, the DC component removal process is specifically: preprocessing the collected original waveform data, calculating the mean of the original waveform data and then performing a mean removal operation to obtain an input signal.

[0007] In one implementation of the present invention, step S2 specifically includes: drawing a spectral average energy diagram based on an adaptive candidate frequency band search method; wherein the adaptive candidate frequency band search method is: setting the number of layers of the tree filter group to divide the signal frequency band, and according to each frequency band, the filter group filters the input signal; calculating the spectral average energy of each frequency band signal after Fourier transformation after filtering, taking the frequency band with the largest spectral average energy as the poor lubrication candidate frequency band, and finally outputting the center frequency and frequency band bandwidth of the poor lubrication candidate frequency band.

[0008] In one implementation of the present invention, the tree filter group includes a two-segment filter and a three-segment filter. The first layer uses a two-segment tree filter to decompose the input signal into different frequency band signals in a binary tree structure, and from the second layer to the 2*N-1 layer, a three-segment tree filter is used to decompose the input signal into different frequency band signals in a 1 / 3 tree structure.

[0009] In one implementation of the present invention, the method further includes:

[0010] The average spectrum energy is calculated based on the following formula:

[0011]

[0012] Where j represents the number of layers (j=1, ... ,N_level), i represents the number of filter bands (i=0, ... ,2^N_level-1), E is the average energy value of the spectrum, Sum_band_amp(j,i) represents the total frequency amplitude of the signal in the j-th filter band after filtering, and N_band_amp is the number of amplitude points in the band;

[0013] Based on the spectrum average energy map, the center frequency and the frequency band bandwidth of the poor lubrication candidate frequency band are calculated; specifically, the following steps are performed: the frequency band where the maximum spectrum average energy is located is found from the spectrum average energy map as the poor lubrication candidate frequency band, and the center frequency and the frequency band bandwidth of the original signal are calculated based on the number of layers where the poor lubrication candidate frequency band is located and the number of filter frequency bands according to the following formula;

[0014]

[0015]

[0016] In the formula, F c is the center frequency, B w is the frequency band bandwidth, j_best is the layer number where the maximum spectral average energy is located, i_best is the filter frequency band number where the maximum spectral average energy is located, and Fs is the sampling frequency of the original waveform data Data.

[0017] In one implementation of the present invention, step S3 specifically includes:

[0018] Calculate the starting frequency of the candidate frequency band for poor lubrication and stop frequency ;

[0019]

[0020]

[0021] Calculate the starting index based on the starting frequency and ending frequency and termination indicator ;

[0022]

[0023]

[0024] Wherein, N is the number of sampling points of the original waveform data Data, and Fs is the sampling frequency of the original waveform data Data;

[0025] From the starting index and termination indicator The energy proportion r of the candidate frequency band for poor lubrication is calculated as follows:

[0026]

[0027] Among them, amp is the spectrum amplitude obtained after Fourier transform calculation of the input signal, and N_amp is the total number of spectrum points.

[0028] In one implementation of the present invention, the method also includes: if the energy proportion of the bad candidate frequency band is lower than the preset poor lubrication candidate frequency band energy proportion threshold, it indicates that the lubrication state is good; if the energy proportion of the bad candidate frequency band is higher than the preset poor lubrication candidate frequency band energy proportion threshold, then the effective background noise lifting feature value is calculated based on the input signal, specifically: the input signal spectrum amplitude is normalized, and the normalized amplitude minimum value set in the poor lubrication candidate frequency band is extracted using a sliding window, and the percentage of all amplitude minimum value sets is taken as the effective background noise lifting feature.

[0029] In one implementation of the present invention, if the energy proportion of the poor candidate frequency band is higher than the preset poor lubrication candidate frequency band energy proportion threshold, the process of calculating the effective background noise lifting feature value based on the input signal is specifically as follows: normalizing the input signal spectrum amplitude, using a sliding window to extract the normalized amplitude minimum value set in the poor lubrication candidate frequency band, and taking the percentage digit of all amplitude minimum value sets as the effective background noise lifting feature.

[0030] In one implementation of the present invention, the length of the sliding window is equal to the window moving step length.

[0031] The present invention also provides a rotating equipment lubrication status assessment device based on vibration analysis, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can complete the aforementioned rotating equipment lubrication status assessment method based on vibration analysis.

[0032] The present invention provides a method and device for evaluating the lubrication status of rotating equipment based on vibration analysis, which has the following beneficial effects:

[0033] (1) Based on the vibration characteristics of poor lubrication, a spectral average energy diagram method is proposed to achieve adaptive search of the center frequency and bandwidth of the high-frequency band of poor lubrication conditions, which solves the unreliability caused by relying on human experience to select a fixed frequency band, thereby improving the stability and accuracy of lubrication monitoring;

[0034] (2) Based on the sliding window and normalized spectrum, a calculation method for the bottom lift feature under poor lubrication conditions is proposed, which realizes the effective calculation of this feature and provides strong support for the monitoring of poor lubrication conditions of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0036] Figure 1 A flow chart of a method for evaluating the lubrication status of a rotating device based on vibration analysis provided by an embodiment of the present invention;

[0037] Figure 2 An input signal spectrum diagram provided by an embodiment of the present invention;

[0038] Figure 3 A spectrum average energy diagram provided by an embodiment of the present invention;

[0039] Figure 4 A normalized spectrum diagram of an input signal provided by an embodiment of the present invention;

[0040] Figure 5 A graph of minimum normalized amplitude values ​​of poor lubrication candidate frequency bands provided by an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of a rotating equipment lubrication status assessment device based on vibration analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] The embodiment of the present invention provides a method and device for evaluating the lubrication status of a rotating device based on vibration analysis. The technical solution proposed in the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0044] Figure 1 A flow chart of a method for evaluating the lubrication status of a rotating device based on vibration analysis provided by an embodiment of the present invention. Figure 1 As shown, the method mainly includes the following steps:

[0045] Step S1: obtaining original waveform data of the rotating device to be tested in the operating state based on an acceleration sensor, and removing the DC component from the original waveform data to obtain an input signal.

[0046] Step S2: Based on the input signal, calculate the spectral average energy map to obtain the center frequency and frequency band bandwidth of the poor lubrication candidate frequency band; wherein the spectral average energy map divides the signal into different frequency bands, and is used to display the energy distribution of the signal in different frequency bands, so as to facilitate the selection of the poor lubrication candidate frequency band.

[0047] Step S3: if the center frequency of the poor lubrication candidate frequency band is greater than a preset frequency threshold, then calculating the energy proportion of the poor candidate frequency band of the rotating equipment to be tested based on the center frequency and the frequency band bandwidth of the poor lubrication candidate frequency band.

[0048] Step S4: If the energy proportion of the poor lubrication candidate frequency band is higher than the preset poor lubrication candidate frequency band energy proportion threshold, the effective background noise lift feature value in the poor lubrication candidate frequency band is calculated based on the input signal; if the effective background noise lift feature value is greater than the preset effective background noise lift feature threshold, it is determined that the rotating equipment to be tested has poor lubrication.

[0049] In the embodiment of the present invention, in the step S1, the DC component removal process is specifically: preprocessing the collected original waveform data, calculating the mean of the original waveform data and then performing a mean removal operation to obtain an input signal.

[0050] The spectrum average energy diagram is an algorithm used to analyze the spectrum characteristics of a signal. It divides the signal into different frequency bands and calculates the spectrum average energy of each frequency band to better understand the energy distribution of the signal in different frequency bands.

[0051] The spectrum average energy map algorithm is proposed to select the candidate frequency bands for poor lubrication. By comparing the spectrum average energy values ​​on different frequency bands, the frequency band with the maximum spectrum average energy is found, which is considered to be the candidate frequency band for poor lubrication. Then, according to the number of layers and the number of filter bands where the maximum spectrum average energy is located, the center frequency and band bandwidth of the candidate frequency band for poor lubrication can be calculated.

[0052] In an embodiment of the present invention, step S2 specifically includes: drawing a spectral average energy diagram based on an adaptive candidate frequency band search method; wherein the adaptive candidate frequency band search method is: setting the number of layers of the tree filter group to divide the signal frequency band, and according to each frequency band, the filter group filters the input signal; calculating the spectral average energy of each frequency band signal after Fourier transformation after filtering, taking the frequency band with the largest spectral average energy as the poor lubrication candidate frequency band, and finally outputting the center frequency and band bandwidth of the poor lubrication candidate frequency band.

[0053] In an embodiment of the present invention, the tree filter group includes a two-segment filter and a three-segment filter. The first layer uses a two-segment tree filter to decompose the input signal into different frequency band signals in a binary tree structure, and from the second layer to the 2*N-1 layer, a three-segment tree filter is used to decompose the input signal into different frequency band signals in a 1 / 3 tree structure.

[0054] In an embodiment of the present invention, the method further includes:

[0055] The average spectrum energy is calculated based on the following formula:

[0056]

[0057] Where j represents the number of layers (j=1, ... ,N_level), i represents the number of filter bands (i=0, ... ,2^N_level-1), E is the average energy value of the spectrum, Sum_band_amp(j,i) represents the total frequency amplitude of the signal in the j-th filter band after filtering, and N_band_amp is the number of amplitude points in the band;

[0058] Based on the spectrum average energy map, the center frequency and the frequency band bandwidth of the poor lubrication candidate frequency band are calculated; specifically, the following steps are performed: the frequency band where the maximum spectrum average energy is located is found from the spectrum average energy map as the poor lubrication candidate frequency band, and the center frequency and the frequency band bandwidth of the original signal are calculated based on the number of layers where the poor lubrication candidate frequency band is located and the number of filter frequency bands according to the following formula;

[0059]

[0060]

[0061] In the formula, F c is the center frequency, B w is the frequency band bandwidth, j_best is the layer number where the maximum spectral average energy is located, i_best is the filter frequency band number where the maximum spectral average energy is located, and Fs is the sampling frequency of the original waveform data Data.

[0062] In the embodiment of the present invention, the step S3 specifically includes:

[0063] Calculate the starting frequency of the candidate frequency band for poor lubrication and stop frequency ;

[0064]

[0065]

[0066] Calculate the starting index based on the starting frequency and ending frequency and termination indicator ;

[0067]

[0068]

[0069] Wherein, N is the number of sampling points of the original waveform data Data, and Fs is the sampling frequency of the original waveform data Data;

[0070] From the starting index and termination indicator The energy proportion r of the candidate frequency band for poor lubrication is calculated as follows:

[0071]

[0072] Among them, amp is the spectrum amplitude obtained after Fourier transform calculation of the input signal, and N_amp is the total number of spectrum points.

[0073] In an embodiment of the present invention, the method also includes: if the energy proportion of the bad candidate frequency band is lower than the preset poor lubrication candidate frequency band energy proportion threshold, it indicates that the lubrication state is good; if the energy proportion of the bad candidate frequency band is higher than the preset poor lubrication candidate frequency band energy proportion threshold, then the effective background noise lifting feature value is calculated based on the input signal, specifically: the input signal spectrum amplitude is normalized, and the normalized amplitude minimum value set in the poor lubrication candidate frequency band is extracted using a sliding window, and the percentage digit of all amplitude minimum value sets is taken as the effective background noise lifting feature.

[0074] In an embodiment of the present invention, if the energy proportion of the poor candidate frequency band is higher than the preset threshold value of the energy proportion of the poor lubrication candidate frequency band, the process of calculating the effective background noise lifting feature value based on the input signal is specifically as follows: normalizing the input signal spectrum amplitude, using a sliding window to extract the normalized amplitude minimum value set in the poor lubrication candidate frequency band, and taking the percentage digit of all amplitude minimum value sets as the effective background noise lifting feature.

[0075] In the embodiment of the present invention, the length of the sliding window is equal to the window moving step length.

[0076] The following is combined with Figure 2-5 , specifically describing an example of the present invention, a rotating equipment lubrication health status assessment method based on vibration analysis is used to implement lubrication status monitoring of a centrifugal pump in a pump room of a mine.

[0077] The original waveform data Data of a poorly lubricated centrifugal pump in a pump room of a mine is collected by an acceleration sensor. The input signal Sig is obtained after removing the DC component of the original waveform data. The frequency spectrum of the input signal Sig is calculated by Fourier transform. Figure 2 As shown, by observing its spectrum, it can be found that there is an abnormal peak in the high-frequency part of the spectrum. The frequency band of the abnormal peak has a high proportion in the spectrum energy and the background noise is raised higher.

[0078] Set the number of spectral average energy map unit filter bank decomposition layers N_level to 3, the threshold Fc_threshold to 1500Hz, the threshold radio_threshold to 0.45, and the threshold feature_threshold to 0.05.

[0079] The spectrum average energy map is calculated by the spectrum average energy map unit as Figure 3 As shown, the center frequency Fc and bandwidth Bw of the poor lubrication candidate frequency band Poor_band are 2200 Hz and 400 Hz respectively.

[0080] The center frequency Fc (2200 Hz) meets the condition that it is greater than the preset frequency threshold Fc_threshold and enters the frequency band energy proportion calculation unit for subsequent calculation.

[0081] The frequency band energy ratio calculation unit obtains that its frequency band energy ratio Poor_lubrication_band_energy_ratio is approximately 0.52.

[0082] The frequency band energy ratio Poor_lubrication_band_energy_ratio (0.52) meets the condition that it is greater than the preset poor lubrication candidate frequency band energy ratio threshold radio_threshold, and enters the background noise lifting feature calculation unit for subsequent calculation.

[0083] Normalize the input signal Sig spectrum amplitude to get the normalized spectrum as follows Figure 4 . Use the sliding window to extract the minimum value set of normalized amplitude of the candidate frequency band Poor_band for poor lubrication, as shown in Figure 5 The effective bottom noise lift feature Bottom_noise_feature is calculated based on the percentage (95%) and is approximately 0.125.

[0084] The bottom noise lift feature Bottom_noise_feature (0.125) meets the condition that it is greater than the preset effective bottom noise lift feature threshold feature_threshold.

[0085] Therefore, according to this method, the results show that the centrifugal pump equipment has poor lubrication.

[0086] From the processing results of this case, it can be seen that this method can not only adaptively search for abnormal peak frequency bands of rotating equipment under poor lubrication conditions, but also effectively calculate the noise floor lift characteristics reflecting poor lubrication conditions, thereby accurately identifying poor lubrication conditions of the equipment. The application of this method helps to improve the intelligence and accuracy of poor lubrication diagnosis of equipment, and the entire process does not require human intervention.

[0087] The above is a rotating equipment lubrication status evaluation method based on vibration analysis provided by an embodiment of the present invention. Based on the same inventive concept, an embodiment of the present invention also provides a rotating equipment lubrication status evaluation device based on vibration analysis. Figure 6 A schematic diagram of a rotating equipment lubrication status assessment device based on vibration analysis provided by an embodiment of the present invention, such as Figure 6 As shown, the device mainly includes: at least one processor 601; and a memory 602 communicatively connected to the at least one processor; wherein the memory 602 stores instructions executable by the at least one processor 601, and the instructions are executed by the at least one processor 601 so that the at least one processor 601 can complete the aforementioned method for evaluating the lubrication status of rotating equipment based on vibration analysis.

[0088] The various embodiments of the present invention are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0089] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0090] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the lubrication status of rotating equipment based on vibration analysis, characterized in that: The method comprises the following steps: Step S1: obtaining original waveform data of the rotating device to be tested in the operating state based on an acceleration sensor, and removing the DC component from the original waveform data to obtain an input signal; Step S2: Based on the input signal, a spectrum average energy map is calculated to obtain the center frequency and frequency band bandwidth of the poor lubrication candidate frequency band; wherein the spectrum average energy map divides the signal into different frequency bands, and is used to display the energy distribution of the signal in different frequency bands, so as to facilitate the selection of the poor lubrication candidate frequency band; Step S3: if the center frequency of the poor lubrication candidate frequency band is greater than a preset frequency threshold, calculating the energy proportion of the poor candidate frequency band of the rotating equipment to be tested based on the center frequency and the frequency band bandwidth of the poor lubrication candidate frequency band; Step S4: If the energy proportion of the poor lubrication candidate frequency band is higher than the preset poor lubrication candidate frequency band energy proportion threshold, the effective background noise lift feature value in the poor lubrication candidate frequency band is calculated based on the input signal; if the effective background noise lift feature value is greater than the preset effective background noise lift feature threshold, it is determined that the rotating equipment to be tested has poor lubrication.

2. A method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 1, characterized in that: In the step S1, the DC component removal process is specifically: pre-processing the collected original waveform data, calculating the mean of the original waveform data and then performing a mean removal operation to obtain an input signal.

3. A method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 1, characterized in that: The step S2 specifically includes: drawing a spectrum average energy diagram based on an adaptive candidate frequency band search method; wherein the adaptive candidate frequency band search method is: The number of layers of the tree filter group is set to divide the signal frequency band, and the filter group filters the input signal according to each frequency band; The average spectrum energy of the filtered frequency band signals after Fourier transformation is calculated, the frequency band with the largest average spectrum energy is taken as the poor lubrication candidate frequency band, and finally the center frequency and frequency band bandwidth of the poor lubrication candidate frequency band are output.

4. A method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 3, characterized in that: The tree filter group includes a two-segment filter and a three-segment filter. The first layer uses a two-segment tree filter to decompose the input signal into different frequency band signals of a binary tree structure. From the second layer to the 2*N-1 layer, a three-segment tree filter is used to decompose the input signal into different frequency band signals of a 1 / 3 tree structure.

5. The method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 3, characterized in that: The method further comprises: The average spectrum energy is calculated based on the following formula: Where j represents the number of layers, and j=1, ... ,N_level, i represents the number of filter bands, and i=0, ... ,2^N_level-1, E is the average energy value of the spectrum, Sum_band_amp(j,i) represents the total frequency amplitude of the signal in the i-th filter band of the j-th layer after filtering, and N_band_amp is the number of amplitude points in the band; Based on the spectrum average energy map, the center frequency and the frequency band bandwidth of the poor lubrication candidate frequency band are calculated; specifically, the following steps are performed: the frequency band where the maximum spectrum average energy is located is found from the spectrum average energy map as the poor lubrication candidate frequency band, and the center frequency and the frequency band bandwidth of the original signal are calculated based on the number of layers where the poor lubrication candidate frequency band is located and the number of filter frequency bands according to the following formula; In the formula, F c is the center frequency, B w is the frequency band bandwidth, j_best is the layer number where the maximum spectral average energy is located, i_best is the filter frequency band number where the maximum spectral average energy is located, and Fs is the sampling frequency of the original waveform data Data.

6. A method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 1, characterized in that: The step S3 specifically includes: Calculate the starting frequency of the candidate frequency band for poor lubrication and stop frequency ; Calculate the starting index based on the starting frequency and ending frequency and termination indicator ; Wherein, N is the number of sampling points of the original waveform data Data, and Fs is the sampling frequency of the original waveform data Data; From the starting index and termination indicator The energy proportion r of the candidate frequency band for poor lubrication is calculated as follows: Among them, amp is the spectrum amplitude obtained after Fourier transform calculation of the input signal, and N_amp is the total number of spectrum points.

7. The method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 1, characterized in that: In the step S4, if the energy proportion of the poor candidate frequency band is lower than the preset poor lubrication candidate frequency band energy proportion threshold, it indicates that the lubrication state is good.

8. The method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 1, characterized in that: If the energy proportion of the bad candidate frequency band is higher than the preset bad lubrication candidate frequency band energy proportion threshold, the process of calculating the effective background noise lifting feature value based on the input signal is specifically as follows: The input signal spectrum amplitude is normalized, and the sliding window is used to extract the normalized amplitude minimum value set in the candidate frequency band of poor lubrication. The percentage of the minimum amplitude value set is taken as the effective background noise lifting feature.

9. A method for evaluating the lubrication status of rotating equipment based on vibration analysis according to claim 8, characterized in that: The length of the sliding window is equal to the window moving step length.

10. A rotating equipment lubrication status assessment device based on vibration analysis, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can complete the method for evaluating the lubrication state of rotating equipment based on vibration analysis as described in any one of claims 1-9.

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