Rolling bearing early fault diagnosis method based on pulse norm vibration intensity

Abstract

The invention provides a rolling bearing early-stage fault diagnosis method based on pulse norm vibration intensity. The rolling bearing early-stage fault diagnosis method comprises the steps that S1, a vibration data acquisition unit is used for acquiring operation vibration data of a bearing; s2, the vibration signal representing the bearing fault is converted into a specific characteristic parameter, and the specific characteristic parameter is used as a basis for judging whether the bearing fault occurs or not; s3, indicating the occurrence of the bearing vibration intensity through a pulse norm index; and S4, selecting a speed root-mean-square vibration intensity method, a frequency domain vibration intensity method, a multi-direction root-mean-square vibration intensity method and an energy intensity method to determine the vibration intensity of the rotating bearing, and further judging the occurrence of the bearing failure. The method integrates two methods of pulse norm and energy intensity, the pulse norm is used for detecting the early failure, and the energy intensity is used for detecting the early failure. The energy intensity is used for further indicating the occurrence of the fault, obtaining the vibration intensity of the bearing, rapidly predicting the early fault of the bearing, and providing technical support for fault judgment of the rotating bearing in actual engineering.

Description

Technical Field

[0001] The present application relates to the technical field of rotating machinery fault diagnosis, and in particular to a rolling bearing early fault diagnosis method based on pulse norm vibration intensity. Background Art

[0002] Centrifugal pumps are a common industrial equipment used to transport liquids or gases. During long-term use, the performance of centrifugal pumps may degrade due to impeller wear, pump casing deformation and other reasons, including reduced flow, reduced efficiency, increased noise and other problems. In order to discover and solve these problems in a timely manner, it is necessary to establish a perception system for centrifugal pump performance degradation and evaluate its reliability. At present, the perception system for centrifugal pump performance degradation usually includes sensors, data acquisition systems and data analysis algorithms. Sensors can monitor the operating status of centrifugal pumps, such as flow, pressure, temperature and other parameters. The data acquisition system collects and stores these data, and the data analysis algorithm can analyze these data to identify whether the performance of the centrifugal pump shows signs of degradation. At the same time, the reliability evaluation of centrifugal pump performance degradation requires the establishment of a complete test verification test platform. This platform needs to be able to simulate the long-term operation of the centrifugal pump and conduct comprehensive testing and evaluation of its performance. This may involve technologies in multiple aspects such as fluid mechanics, vibration analysis, and temperature analysis, and corresponding test equipment and test methods are required.

[0003] The background technologies of the integrated test and verification platform for reliability assessment of centrifugal pump performance degradation and its perception system include technologies in multiple fields such as sensing technology, data acquisition and analysis technology, fluid mechanics, vibration analysis, temperature analysis, etc. It is necessary to comprehensively apply these technologies to establish a complete system to ensure the safe and stable operation of the centrifugal pump. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a rolling bearing early fault diagnosis method based on pulse norm vibration intensity in response to the above-mentioned problems. By testing and verifying various indicators of the sensing system, problems can be discovered and repaired in a timely manner, thereby improving the reliability and stability of the sensing system and providing important technical support and guarantee for the long-term stable operation of the centrifugal pump.

[0005] The embodiment of the present application is implemented as follows:

[0006] The embodiment of the present application provides a rolling bearing early fault diagnosis method based on pulse norm vibration severity, which is characterized by comprising the following steps:

[0007] Step a, using a vibration data acquisition unit to obtain operating vibration data of the bearing;

[0008] Step b, converting the vibration signal representing the bearing fault into a specific characteristic parameter, which is then used as a basis for determining whether the bearing fault occurs;

[0009] Step c, indicating the occurrence of bearing vibration severity through a pulse norm indicator;

[0010] Step d, selecting the velocity root mean square vibration intensity method, the frequency domain vibration intensity method, the multi-directional root mean square vibration intensity method, or the energy intensity method to determine the vibration intensity of the rotating bearing, and then judging the occurrence of the bearing fault.

[0011] In some optional implementation schemes, step a specifically includes the following contents:

[0012] Step a1, for machines with a rotation speed less than 1000 RPM, displacement is used as vibration data collection;

[0013] Step a2, collecting vibration signals of a medium- and low-frequency vibration environment with a rotation speed of 1000RPM to 10000RPM, such as a shaft and a bearing seat, using speed as vibration data;

[0014] Step a3: For high-frequency vibration environments above 10,000 RPM, the acceleration sensor can better capture high-frequency signals and collect acceleration as vibration data.

[0015] In some optional implementation schemes, in step c, a proportional coefficient Ratio is first preset, and then the pulse number I is calculated based on the coefficient, and finally the pulse norm Ψ is obtained, which is used as a basis for evaluating the bearing vibration intensity and realizing fault diagnosis.

[0016] In some optional implementation schemes, the specific solution process of the pulse norm Ψ is as follows:

[0017] Step c1, obtaining the order statistics of the measured vibration signal It is expressed as:

[0018]

[0019] Among them, x i is the amplitude signal of the measured vibration signal x;

[0020] Step c2, the pulse norm Ψ is defined as the ratio of the mean of the first I maximum amplitude data points to the total signal energy ||χ||, and the calculation formula is:

[0021]

[0022] In some optional implementation schemes, the velocity root mean square vibration intensity method described in step d specifically includes the following contents:

[0023] When the discrete signal of vibration velocity is known, the vibration severity formula is written as:

[0024]

[0025] Where V rms is the vibration severity value, N is the length of the discrete signal, and v is the discrete signal of vibration velocity. When calculating the vibration severity, it is necessary to consider the signal duration T and the discrete signal length N, and ensure that the original continuous vibration signal v(t) and the discrete vibration signal v(n) are collected. The vibration severity is described in mm / s or in / s; if dB is used as the unit, mm / s is used as a reference; through precise measurement and unit conversion, the equipment status can be evaluated, providing a key basis for predictive maintenance and early fault warning.

[0026] In some optional implementation schemes, the frequency domain vibration severity method described in step d specifically includes the following contents:

[0027] For a periodic signal x(t), if the Dirichlet condition is satisfied, it can be expressed in the form of a Fourier series:

[0028]

[0029] Where a0 is a static variable, a n and b n are the amplitudes of the sine and cosine signals respectively; ω0 is the frequency of these signals.

[0030] For the collected discrete vibration data signal x(n), whose sampling frequency is fs, fast Fourier transform is used to convert it into a frequency domain signal:

[0031]

[0032] Where x(n) is the signal in the corresponding time domain, and N is the total signal length.

[0033] The single-sided amplitude spectrum obtained according to the frequency domain signal after conversion is:

[0034]

[0035] The frequency corresponding to the single-sided amplitude spectrum is:

[0036]

[0037] When the collected signal is an acceleration signal and the unit is appropriate, the vibration intensity expression is as follows:

[0038]

[0039] Among them, k a is the starting frequency, k bis the span frequency.

[0040] In some optional implementation schemes, the multi-directional root mean square vibration intensity method described in step d specifically includes the following contents:

[0041] Monitor the vibration data in the three degrees of freedom. When there are multiple measuring points on a certain degree of freedom or there are measuring points on multiple degrees of freedom, the vibration severity is expressed as follows:

[0042]

[0043] Among them, ∑V x ,∑V y ,∑V z Represents the sum of the effective values ​​of the vibration velocities of the three different degrees of freedom x, y, and z under the same working condition, N x 、N y 、N z Corresponding to the number of measuring points in the x, y, and z directions respectively.

[0044] In some optional embodiments, the energy intensity method described in step d specifically includes the following contents:

[0045] The vibration energy of the system consists of kinetic energy and potential energy. The root mean square value of the velocity is V rms It can reflect the effective value of kinetic energy and exclude the influence of system rotational kinetic energy; while the root mean square value of displacement can be used to calculate the effective value of potential energy. Combining the two, the effective value of vibration energy of the vibration system E is obtained. rms :

[0046]

[0047] Where E rms is the energy intensity, m is the average mass density, V rms is the RMS value of velocity, k is the system stiffness, x rms is the root mean square value of displacement.

[0048] The changing trend of the natural frequency reflects the transformation of vibration energy between different vibration modes. This change is obtained by analyzing the relationship between the single-segment bearing and the integral bearing, and is closely related to the stiffness k and mass distribution m of the system:

[0049]

[0050] Natural frequency p x and p y , respectively represent the inherent characteristics of the vibration system in the x and y directions. They are closely related to the effective value of the vibration energy. Based on this relationship, the effective value of the vibration energy of the vibration system is accurately quantified:

[0051]

[0052] Where E rms is the energy intensity, m is the average mass density, V rms is the RMS value of velocity, p x p y is the natural frequency in the x and y directions, x rms is the root mean square value of displacement.

[0053] The beneficial effects of the present application are as follows: the present invention provides a method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity. Compared with traditional velocity root mean square, frequency domain acceleration and multi-directional velocity root mean square methods, the energy intensity calculation method has higher sensitivity and can better evaluate the operating status of the equipment; in addition, the pulse norm method has also been proven to change at an earlier time point and foresee the occurrence of faults in advance; therefore, the vibration intensity calculation method combining energy and pulse norms can predict early bearing failures faster and more accurately, providing an important reference for equipment maintenance; by adopting the vibration intensity method based on energy and pulse norms, we can have a more comprehensive understanding of the equipment's operating status, promptly discover potential signs of faults, and provide more effective means for equipment maintenance; not only can the safety and stability of the equipment be improved, but also the maintenance cost can be reduced, the service life of the equipment can be extended, and it is efficient and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flowchart of a rolling bearing early fault diagnosis method according to an embodiment of the present application;

[0056] Figure 2a-2e A vibration severity diagram of a bearing according to an embodiment of the present application, based on definition, frequency domain acceleration, multi-directional velocity, energy and pulse norm;

[0057] Figure 3a-3e The vibration severity diagrams of the bearing 2 of the embodiment of the present application are based on the definition, frequency domain acceleration, multi-directional velocity, energy and pulse norm respectively;

[0058] Figure 4a-4e The vibration severity diagrams of the bearing three of the embodiment of the present application are based on the definition, frequency domain acceleration, multi-directional velocity, energy and pulse norm respectively;

[0059] Figure 5a-5e The vibration intensity diagrams of the bearing four of the embodiment of the present application are based on the definition, frequency domain acceleration, multi-directional velocity, energy and pulse norm. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0062] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0063] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the application is usually placed when in use. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0064] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0065] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0066] In the present application, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0067] The features and performance of the present application are further described in detail below in conjunction with the embodiments.

[0068] The core goal of the present invention is to solve the problems of low vibration intensity sensitivity and difficulty in identifying early faults in the diagnosis of traditional centrifugal pump bearing faults. The present invention combines the pulse norm and energy intensity calculation methods, selects appropriate acceleration, velocity, and displacement signal measurement methods according to the type and operating conditions of the centrifugal pump, thereby obtaining accurate vibration intensity information and realizing rapid and accurate diagnosis of centrifugal pump bearing faults.

[0069] To achieve the above object, the present invention adopts a rolling bearing early fault diagnosis method based on pulse norm vibration intensity, which includes the following key steps:

[0070] S1. Use the vibration data acquisition unit to obtain the operating vibration data of the bearing:

[0071] a. For low speed machines (speed less than 1000RPM), displacement is the best choice indicator

[0072] b. For equipment with small acceleration but significant displacement, a compromise is usually adopted, using velocity measurement as a tool.

[0073] c. For high-speed or high-frequency equipment, although the displacement is small and the speed is moderate, the acceleration may be abnormally high. At this time, acceleration measurement becomes an effective method.

[0074] S2, involves converting the vibration signal reflecting the bearing fault into specific characteristic parameters, which become the basis for us to judge whether there is a bearing fault.

[0075] S3. Indicate the occurrence of bearing vibration intensity through pulse norm index:

[0076] The pulse norm plays an important role in bearing fault diagnosis, and the key lies in the comprehensive consideration of multiple groups of vibration peaks. When implementing, the proportional coefficient Ratio must be preset first, and then the number of pulses I is calculated based on the coefficient, and finally the pulse norm value is obtained, which is used as the basis for evaluating the bearing vibration intensity and achieving accurate fault diagnosis.

[0077] a. Obtain the order statistics of the measured vibration signal It is expressed as:

[0078]

[0079] Among them, x i is the amplitude signal of the measured vibration signal x;

[0080] b. The pulse norm Ψ is defined as the ratio of the mean of the first I maximum amplitude data points to the total signal energy ||χ||, and the calculation formula is:

[0081]

[0082] This formula shows the relationship between the total signal energy ‖χ‖ and the generalized pulse number I. The pulse norm here does not focus only on the single data point with the largest amplitude, but expands the focus to the first I data points with the largest amplitude. Unlike kurtosis and D-norm, the pulse norm exhibits unique characteristics when dealing with the deconvolution process: it is not affected by the fault cycle, which makes it more robust in fault diagnosis. Compared with methods such as multi-D-norm and ICS2, the pulse norm has unique advantages in processing complex signals.

[0083] S4. In the process of judging bearing failure, we use velocity root mean square vibration intensity method, frequency domain vibration intensity method, multi-directional root mean square vibration intensity method and energy intensity method to accurately measure the vibration intensity of rotating bearings, and then accurately judge the occurrence of bearing failure.

[0084] (1) Velocity root mean square vibration intensity method:

[0085] When the discrete signal of vibration velocity is known, the vibration intensity formula can be written as:

[0086]

[0087] Where V rmsis the vibration severity value, N is the length of the discrete signal, and v is the vibration velocity discrete signal.

[0088] When calculating vibration intensity, it is necessary to consider the signal duration T and the discrete signal length N, while ensuring that the original continuous vibration signal v(t) and the discrete vibration signal v(n) are collected. The vibration intensity is described in units of mm / s or in / s. If dB is used as the unit, mm / s must be used as a reference. Through precise measurement and unit conversion, the equipment status can be evaluated, providing a key basis for predictive maintenance and early fault warning.

[0089] (2) Frequency domain vibration intensity method:

[0090] a. For a periodic signal x(t), if the Dirichlet condition is satisfied, it can be expressed in the form of a Fourier series:

[0091]

[0092] Where a0 is a static variable, a n and b n are the amplitudes of the sine and cosine signals respectively; ω0 is the frequency of these signals.

[0093] b. For the collected discrete vibration data signal x(n), whose sampling frequency is fs, fast Fourier transform is used to convert it into a frequency domain signal:

[0094]

[0095] c. The unilateral amplitude spectrum obtained according to the converted frequency domain signal is:

[0096]

[0097] d. The frequency corresponding to the unilateral amplitude spectrum is:

[0098]

[0099] e. When the collected signal is an acceleration signal and the unit is appropriate, the vibration intensity expression is as follows:

[0100]

[0101] (3) Multi-directional root mean square vibration intensity method:

[0102] The vibration velocity in a single direction is not enough to fully reflect the working status and fault conditions of the equipment. Therefore, in actual operation, vibration data in three degrees of freedom are usually monitored. When there are multiple measuring points on a certain degree of freedom or there are measuring points on multiple degrees of freedom, the expression of vibration intensity becomes particularly important:

[0103]

[0104] Where, ΣV x ,ΣV y ,ΣV z Represents the sum of the effective values ​​of the vibration velocities of the three different degrees of freedom x, y, and z under the same working condition, N x 、N y 、N z The data correspond to the number of measuring points in the x, y and z directions respectively. This data comprehensively reflects the operating status of the vibration system in each direction.

[0105] (4) Energy intensity method:

[0106] a. The vibration energy of the system consists of kinetic energy and potential energy. The velocity root mean square value Vrms can reflect the effective value of kinetic energy, excluding the influence of the system's rotational kinetic energy; while the displacement root mean square value can be used to calculate the effective value of potential energy. Combining the two, the effective value of the vibration energy of the vibration system E can be obtained. rms :

[0107]

[0108] b. The changing trend of the natural frequency reflects the transformation of vibration energy between different vibration modes. This change is obtained by analyzing the relationship between the single-segment bearing and the integral bearing, and is closely related to the stiffness k and mass distribution m of the system:

[0109]

[0110] c. Natural frequency p x and p y , respectively represent the inherent characteristics of the vibration system in the x and y directions. They are closely related to the effective value of the vibration energy. Based on this relationship, the effective value of the vibration energy of the vibration system is accurately quantified:

[0111]

[0112] Example 1

[0113] Take the Rexnord ZA-2115 double row bearing as an example:

[0114] The sampling frequency is 20kHz, and each sample covers 20,480 detailed data points. Four such bearings (bearing one, bearing two, bearing three, bearing four) are assembled on the shaft, connected to an AC motor via a friction belt to ensure a stable speed of 2000RPM. In order to simulate actual working conditions, a radial load of up to 6,000 pounds is applied to the shaft and achieved through a spring mechanism. These bearings are forced lubricated to ensure stable operation under continuous high loads.

[0115] This study uses a data set containing 2156 data files, of which the time interval of the first 43 sets of data is 5 minutes, and the time interval of the next 2113 sets of data is 10 minutes. Based on this feature, we chose the vibration intensity calculation method of discrete signals for analysis. Each set of data contains 8 columns and a total of 20480 rows of data. Every two columns represent the data measured at the X and Y axis measuring points of a bearing. The data acquisition frequency is 20kHz, and the acquisition interval between each row of data is 0.05ms. Through these data, we will explore the vibration of the bearing at different time intervals, thereby providing useful data support for research in related fields.

[0116] After the acceleration signal is collected, it is converted into instantaneous velocity data and then derived into a discrete velocity signal.

[0117] The vibration intensity of the four bearings calculated based on definition, velocity, frequency domain acceleration, multi-directional velocity, energy and pulse norm is discrete in the time domain. It is necessary to divide it into multiple time periods of length L and calculate the energy E of the vibration intensity in each time period. L :

[0118]

[0119] like Figure 2a-Figure 5e As shown in the figure, the vibration intensity energy spectrum is drawn by recording the vibration intensity in different time periods. The vibration signal in each time period corresponds to a specific energy value, and the continuous accumulation of these values ​​eventually forms the entire energy spectrum. By analyzing the energy spectrum, we can more intuitively understand the vibration conditions of structural objects in different time periods, providing an important reference for engineering design and structural evaluation.

[0120] Figure 2a-5e The vibration severity values ​​of bearing 1, bearing 2, bearing 3, and bearing 4 under different algorithms are shown respectively, among which 2a, 3a, 4a, and 5a are the vibration severity change diagrams of vibration signals processed by velocity algorithm, 2b, 3b, 4b, and 5b are the vibration severity change diagrams of vibration signals processed by frequency domain analysis algorithm, 2c, 3c, 4c, and 5c are the vibration severity change diagrams of vibration signals processed by velocity vector analysis algorithm, 2d, 3d, 4d, and 5d are the vibration severity change diagrams of vibration signals processed by energy analysis algorithm, and 2e, 3e, 4e, and 5e are the vibration severity change diagrams of vibration signals processed by pulse norm algorithm. The image trends of the methods based on velocity, frequency domain, velocity vector, and energy are basically the same, the sensitivity of energy is relatively higher, and the pulse norm is highly sensitive to the time of fault occurrence.

[0121] The figure shows the vibration severity diagram based on different parameters. By analyzing these diagrams, we can have a more comprehensive understanding of the vibration situation. Among them, definition, frequency domain acceleration, multi-directional velocity, energy and pulse norm are commonly used vibration parameters. By comparing the changes in these diagrams, we can identify the characteristics of vibration intensity and make corresponding adjustments and improvements based on the actual situation. These data are crucial for engineering design and structural safety, and can help us better prevent damage caused by vibration to buildings and equipment.

[0122] The images of the four methods show similar vibration severity trends, among which the three images based on definition, multi-directional velocity, and energy are almost identical in shape. This is because they are all vibration severity calculated based on the root mean square value of velocity, while velocity vector and energy are variants of the velocity method. Therefore, these methods show a high degree of similarity when measuring vibration severity. This discovery provides us with a deeper understanding and more ideas for future research. We hope that it can be better applied in actual engineering and achieve better results.

[0123] The peak-to-effective value ratio calculation is performed on the five curves of each bearing to quantify the sensitivity of the vibration intensity calculation method and to compare the sensitivity of the five methods to bearing faults. This method helps to reveal the advantages and disadvantages of different calculation methods and provide an objective basis for fault detection. The specific data are shown in Table 1.

[0124] Table 1 Ratio of peak value to normal value of vibration intensity of four bearings

[0125]

[0126] Data analysis shows that among the methods for calculating peak values ​​from different angles, the methods based on speed, frequency domain and multi-directional speed are all more than twice the normal value, while the energy-based calculation method is close to 4.4 times.

Claims

1. A rolling bearing early fault diagnosis method based on pulse norm vibration severity, characterized in that: The steps include: Step a, using a vibration data acquisition unit to obtain operating vibration data of the bearing; Step b, converting the vibration signal representing the bearing fault into a specific characteristic parameter, which is then used as a basis for determining whether the bearing fault occurs; Step c, indicating the occurrence of bearing vibration severity through a pulse norm indicator; Step d, selecting the velocity root mean square vibration intensity method, the frequency domain vibration intensity method, the multi-directional root mean square vibration intensity method, or the energy intensity method to determine the vibration intensity of the rotating bearing, and then judging the occurrence of the bearing fault.

2. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 1 is characterized in that: Step a specifically includes the following contents: Step a1, for machines with a rotation speed less than 1000 RPM, displacement is used as vibration data collection; Step a2, collecting vibration signals of a medium- and low-frequency vibration environment with a rotation speed of 1000RPM to 10000RPM, such as a shaft and a bearing seat, using speed as vibration data; Step a3: For high-frequency vibration environments above 10,000 RPM, the acceleration sensor can better capture high-frequency signals and collect acceleration as vibration data.

3. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 1 or 2, characterized in that: In step c, the proportional coefficient Ratio is first preset, and then the pulse number I is calculated based on the coefficient, and finally the pulse norm Ψ is obtained, which is used as the basis for evaluating the bearing vibration intensity to achieve Troubleshooting.

4. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 3 is characterized in that: The specific solution process of the pulse norm Ψ is as follows: Step c1, obtaining the order statistics of the measured vibration signal It is expressed as: Among them, x i is the amplitude signal of the measured vibration signal x; Step c2, the pulse norm Ψ is defined as the ratio of the mean of the first I maximum amplitude data points to the total signal energy ||χ||, and the calculation formula is:

5. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 1 or 4, characterized in that: The velocity root mean square vibration intensity method described in step d specifically includes the following: When the discrete signal of vibration velocity is known, the vibration severity formula is written as: Where V rms is the vibration severity value, N is the length of the discrete signal, and v is the discrete signal of vibration velocity. When calculating the vibration severity, it is necessary to consider the signal duration T and the discrete signal length N, while ensuring that the original continuous vibration signal v(t) and the discrete vibration signal v(n) are collected. The vibration severity is described in mm / s or in / s. If dB is used as the unit, mm / s is used as a reference. Through precise measurement and unit conversion, the equipment status can be evaluated, providing a key basis for predictive maintenance and early fault warning.

6. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 1 is characterized in that: The frequency domain vibration severity method described in step d specifically includes the following contents: For a periodic signal x(t), if the Dirichlet condition is satisfied, it can be expressed in the form of a Fourier series: Where a0 is a static variable, a n and b n are the amplitudes of the sine and cosine signals respectively; ω0 is the frequency of these signals; For the collected discrete vibration data signal x(n), whose sampling frequency is fs, fast Fourier transform is used to convert it into a frequency domain signal: The single-sided amplitude spectrum obtained according to the converted frequency domain signal is: The frequency corresponding to the one-sided amplitude spectrum is: When the collected signal is an acceleration signal and the unit is appropriate, the vibration intensity expression is as follows: Among them, k a is the starting frequency, k b is the span frequency.

7. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 6 is characterized in that: The multi-directional root mean square vibration intensity method described in step d specifically includes the following contents: Monitor the vibration data in the three degrees of freedom. When there are multiple measuring points on a certain degree of freedom or there are measuring points on multiple degrees of freedom, the vibration severity is expressed as follows: Among them, ∑V x ,∑V y ,∑V z Represents the sum of the effective values ​​of the vibration velocities of the three different degrees of freedom x, y, and z under the same working condition, N x 、N y 、N z Corresponding to the number of measurement points in the x, y, and z directions respectively.

8. The rolling bearing early fault diagnosis method based on pulse norm vibration severity according to claim 7 is characterized in that: The energy intensity method described in step d specifically includes the following contents: The vibration energy of the system consists of kinetic energy and potential energy. The root mean square value of the velocity is V rms It can reflect the effective value of kinetic energy and exclude the influence of system rotational kinetic energy; while the root mean square value of displacement can be used to calculate the effective value of potential energy. Combining the two, the effective value of vibration energy of the vibration system E is obtained. rms : The changing trend of the natural frequency p reflects the transformation of vibration energy between different vibration modes. This change is obtained by analyzing the relationship between the single-segment bearing and the integral bearing, and is closely related to the stiffness k and mass distribution m of the system: Natural frequency p x and p y , respectively represent the inherent characteristics of the vibration system in the x and y directions. They are closely related to the effective value of the vibration energy. Based on this relationship, the effective value of the vibration energy of the vibration system is accurately quantified:

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