Early fault diagnosis method for rolling bearing based on pulse norm vibration intensity

Abstract

The application provides a rolling bearing early fault diagnosis method based on pulse norm vibration intensity, comprising the following steps: S1, acquiring operation vibration data of the bearing by using a vibration data acquisition unit; S2, converting the vibration signal representing the bearing fault into specific characteristic parameters, which are used as the basis for judging whether the bearing fault occurs; S3, indicating the occurrence of the bearing vibration intensity by the pulse norm index; S4, determining the vibration intensity of the rotating bearing by selecting the speed root mean square vibration intensity method, the frequency domain vibration intensity method, the multi-direction root mean square vibration intensity method and the energy intensity method, and then judging the occurrence of the bearing fault. The application combines the pulse norm and the energy intensity, the pulse norm is used for detecting the early fault, the energy intensity is used for further indicating the occurrence of the fault, the vibration intensity of the bearing is obtained, the early fault of the bearing is quickly predicted, and technical support is provided for the fault judgment of the rotating bearing in the actual engineering.

Description

Technical Field

[0001] This application relates to the technical field of rotating machinery fault diagnosis, and more specifically, to a method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity. Background Technology

[0002] Centrifugal pumps are common industrial devices used to transport liquids or gases. During prolonged use, due to impeller wear, pump casing deformation, and other factors, the performance of centrifugal pumps may degrade, resulting in problems such as reduced flow rate, decreased efficiency, and increased noise. To promptly detect and address these issues, a centrifugal pump performance degradation sensing system and its reliability assessment are necessary. Currently, centrifugal pump performance degradation sensing systems typically include sensors, data acquisition systems, and data analysis algorithms. Sensors monitor the operating status of the centrifugal pump, such as parameters like flow rate, pressure, and temperature. The data acquisition system collects and stores this data, while the data analysis algorithm analyzes this data to identify signs of performance degradation. Simultaneously, reliability assessment of centrifugal pump performance degradation requires the establishment of a complete testing and verification platform. This platform needs to be able to simulate the long-term operation of the centrifugal pump, conducting comprehensive testing and evaluation of its performance. This may involve multiple technologies such as fluid mechanics, vibration analysis, and temperature analysis, requiring corresponding testing equipment and methods.

[0003] The background technologies of the integrated testing and verification platform for assessing the performance degradation of centrifugal pumps and the reliability of their sensing systems encompass multiple fields, including sensing technology, data acquisition and analysis technology, fluid mechanics, vibration analysis, and temperature analysis. A comprehensive system needs to be established by integrating these technologies to ensure the safe and stable operation of the centrifugal pumps. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide an early fault diagnosis method for rolling bearings based on pulse norm vibration intensity. By testing and verifying various indicators of the sensing system, problems can be detected 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 centrifugal pumps.

[0005] The embodiments of this application are implemented as follows:

[0006] This application provides a method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity, characterized by the following steps:

[0007] Step a: Use a vibration data acquisition unit to acquire the bearing's operating vibration data;

[0008] Step b involves converting the vibration signal representing bearing failure into specific characteristic parameters, which then serve as the basis for determining whether a bearing failure has occurred.

[0009] Step c: The intensity of bearing vibration is indicated by the pulse norm index;

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

[0011] In some alternative implementations, step a specifically includes the following:

[0012] Step a1: For machines with a rotational speed of less than 1000 RPM, displacement is used as the vibration data acquisition method.

[0013] Step a2: For low-frequency vibration environments with rotational speeds between 1000 RPM and 10000 RPM, such as vibration signal acquisition of shafts and bearing housings, speed is used as the vibration data acquisition criterion.

[0014] Step a3: For high-frequency vibration environments above 10,000 RPM, the accelerometer can capture high-frequency signals well and use acceleration as the vibration data acquisition method.

[0015] In some optional implementations, step c first presets a proportionality coefficient Ratio, then calculates the number of pulses I based on this coefficient, and finally obtains the pulse norm Ψ, which is used as the basis for evaluating the bearing vibration intensity and realizing fault diagnosis.

[0016] In some optional implementations, the specific process for solving the impulse norm Ψ is as follows:

[0017] Step c1: Obtain the order statistics of the measured vibration signal. Represented as:

[0018]

[0019] Where, x i 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 implementations, the root mean square velocity vibration intensity method described in step d specifically includes the following:

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

[0024]

[0025] Where V rms Let N be the vibration intensity value, N be the length of the discrete signal, and v be the discrete vibration velocity signal. When calculating vibration intensity, the signal duration T and the discrete signal length N must be considered. Simultaneously, it is essential to ensure that both the original continuous vibration signal v(t) and the discrete vibration signal v(n) are acquired. Vibration intensity is described in mm / s or in / s units; if in dB units, mm / s must be used as the reference standard. Through precise measurement and unit conversion, equipment condition can be assessed, providing crucial information for predictive maintenance and early fault warning.

[0026] In some optional implementations, the frequency domain vibration intensity method described in step d specifically includes the following:

[0027] For a periodically continuous signal x(t), if the Dirichlet conditions are 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 ω0 represents the amplitude of the sine and cosine signals, respectively; ω0 is the frequency of these signals.

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

[0031]

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

[0033] The single-sided amplitude spectrum obtained from the transformed frequency domain signal is as follows:

[0034]

[0035] The frequencies corresponding to the single-sided amplitude spectrum are:

[0036]

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

[0038]

[0039] Where, k a k is the starting frequency. bFor span frequency.

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

[0041] When monitoring vibration data in three degrees of freedom, and when there are multiple measuring points in one degree of freedom or multiple degrees of freedom, the vibration intensity is expressed as follows:

[0042]

[0043] Where, ∑V x ,∑V y ,∑V z N represents the sum of the effective values ​​of the vibration velocities of the three different degrees of freedom (x, y, z) under the same working condition. x N y N z The number of measurement points corresponds to the x, y, and z directions, respectively.

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

[0045] The vibrational energy of the system consists of two parts: kinetic energy and potential energy, with the root mean square value of velocity V. rms The effective value of kinetic energy can be reflected, eliminating the influence of the system's 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 the vibration energy E of the vibrating system is obtained. rms :

[0046]

[0047] In the formula E rms V represents the energy intensity, m represents the average mass density, and V represents the energy intensity. rms Let x be the root mean square value of the velocity, k be the system stiffness, and x be the value of the system stiffness. rms This is the root mean square value of the displacement.

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

[0049]

[0050] Natural frequency p x and p y , representing the inherent characteristics of the vibration system in the x and y directions, respectively, are closely related to the effective value of the vibration energy. Based on this relationship, a precise quantitative description of the effective value of the vibration energy of the vibration system is provided:

[0051]

[0052] In the formula E rms V represents the energy intensity, m represents the average mass density, and V represents the energy intensity. rms p is the root mean square value of velocity. x p y Here are the natural frequencies in the x and y directions, x rms This is the root mean square value of the displacement.

[0053] The beneficial effects of this application are as follows: This invention provides an early fault diagnosis method for rolling bearings based on pulse norm vibration intensity. Compared with traditional root mean square velocity, frequency domain acceleration, and multi-directional root mean square velocity methods, the energy intensity calculation method has higher sensitivity and can better assess the operating status of the equipment. In addition, the pulse norm method has also been proven to change at an earlier time point, predicting the occurrence of faults in advance. Therefore, the vibration intensity calculation method combining energy and pulse norm can predict early bearing faults faster and more accurately, providing an important reference for equipment maintenance. By adopting the vibration intensity method based on energy and pulse norm, we can gain a more comprehensive understanding of the equipment's operating status, detect potential fault signs in a timely manner, and provide more effective means for equipment maintenance. This not only improves the safety and stability of the equipment but also reduces maintenance costs, extends the service life of the equipment, and is highly efficient and reliable. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the early fault diagnosis method for rolling bearings according to an embodiment of this application;

[0056] Figures 2a-2e The vibration intensity diagrams of the bearing in this embodiment of the application are based on the definition, frequency domain acceleration, multi-directional velocity, energy, and impulse norm, respectively.

[0057] Figures 3a-3e The vibration intensity diagrams of the bearing 2 in this application are based on the definition, frequency domain acceleration, multi-directional velocity, energy, and impulse norm, respectively.

[0058] Figures 4a-4e The vibration intensity diagrams of the bearings in this embodiment are based on the definition, frequency domain acceleration, multi-directional velocity, energy, and impulse norm, respectively.

[0059] Figures 5a-5e The vibration intensity diagrams of the bearing four in this embodiment of the application are based on the definition, frequency domain acceleration, multi-directional velocity, energy and impulse norm. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0062] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

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

[0064] Furthermore, terms such as "horizontal," "vertical," and "sag" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal relative to "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 expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0066] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0067] The features and performance of this application will be further described in detail below with reference to the embodiments.

[0068] The core objective of this invention is to solve the problems of low vibration intensity sensitivity and difficulty in identifying early-stage faults in traditional centrifugal pump bearing fault diagnosis. This invention combines pulse norm and energy intensity calculation methods, and selects appropriate acceleration, velocity, and displacement signal measurement methods based on the type of centrifugal pump and its operating conditions, thereby obtaining accurate vibration intensity information and achieving rapid and accurate diagnosis of centrifugal pump bearing faults.

[0069] To achieve the above objectives, this invention employs an early fault diagnosis method for rolling bearings based on pulse norm vibration intensity, comprising the following key steps:

[0070] S1. Use a vibration data acquisition unit to acquire the bearing's operating vibration data:

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

[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. In this case, acceleration measurement becomes a very effective method.

[0074] S2 involves converting vibration signals reflecting bearing failures into specific characteristic parameters, which become the basis for us to determine whether the bearing has a failure.

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

[0076] The pulse norm plays a crucial role in bearing fault diagnosis, and its key lies in the comprehensive consideration of multiple vibration peak values. In implementation, a proportionality coefficient (Ratio) must first be preset, then the number of pulses (I) is calculated based on this coefficient, and finally the pulse norm value is obtained. This value serves as the basis for assessing the bearing vibration intensity, achieving accurate fault diagnosis.

[0077] a. Obtaining the order statistics of the measured vibration signal Represented as:

[0078]

[0079] Where, x i The amplitude signal of the measured vibration signal x;

[0080] b. The impulse 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 equation illustrates the relationship between the total signal energy ‖χ‖ and the number of generalized impulses I. Here, the impulse norm does not focus solely on the single data point with the largest amplitude, but rather extends the focus to the top I data points with the largest amplitudes. Unlike kurtosis and D-norm, the impulse norm exhibits unique characteristics when handling deconvolution: it is unaffected by fault cycles, making it more robust in fault diagnosis. Compared to methods such as multiple D-norm and ICS2, the impulse norm possesses unique advantages when processing complex signals.

[0083] S4. In the process of judging bearing failure, we use the 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 the rotating bearing, and thus accurately judge the occurrence of bearing failure.

[0084] (1) Root mean square velocity vibration intensity method:

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

[0086]

[0087] Where V rmsdenoted as , where N is the vibration intensity value, N is the length of the discrete signal, and v is the discrete signal of vibration velocity.

[0088] When calculating vibration intensity, the signal duration T and discrete signal length N must be considered. At the same time, it is necessary to ensure that the original continuous vibration signal v(t) and discrete vibration signal v(n) are collected. Vibration intensity is described in mm / s or in / s. If dB is used as the unit, mm / s should be used as the reference standard. Through accurate measurement and unit conversion, the equipment status can be assessed, providing key basis for predictive maintenance and early fault warning.

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

[0090] a. For a periodically continuous signal x(t), if the Dirichlet conditions are 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 ω0 represents the amplitude 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), with a sampling frequency of fs, the Fast Fourier Transform is used to convert it into a frequency domain signal:

[0094]

[0095] c. The single-sided amplitude spectrum obtained from the transformed frequency domain signal is as follows:

[0096]

[0097] d. The frequencies corresponding to the single-sided amplitude spectrum are:

[0098]

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

[0100]

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

[0102] Vibration velocity in a single direction is insufficient to fully reflect the operating status and malfunctions of equipment. Therefore, in practice, vibration data in three degrees of freedom are typically monitored. When there are multiple measuring points in a single degree of freedom or measuring points in multiple degrees of freedom, the method of representing vibration intensity becomes particularly important.

[0103]

[0104] Where, ΣV x ,ΣV y ,ΣV z N represents the sum of the effective values ​​of the vibration velocities of the three different degrees of freedom (x, y, z) under the same working condition. x N y N z These 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 two parts: kinetic energy and potential energy. The root mean square value of velocity (Vrms) reflects the effective value of the kinetic energy, eliminating the influence of the system's rotational kinetic energy; while the root mean square value of displacement can be used to calculate the effective value of the potential energy. Combining the two, the effective value of the vibration energy E of the vibration system can be obtained. rms :

[0107]

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

[0109]

[0110] c. Natural frequency p x and p y , representing the inherent characteristics of the vibration system in the x and y directions, respectively, are closely related to the effective value of the vibration energy. Based on this relationship, a precise quantitative description of the effective value of the vibration energy of the vibration system is provided:

[0111]

[0112] Example 1

[0113] Taking 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, and bearing four) are mounted on the shaft and connected to an AC motor via a friction belt to ensure a stable speed of 2000 RPM. To simulate actual working conditions, a radial load of up to 6000 pounds is applied to the shaft, achieved through a spring mechanism. These bearings are all forcibly lubricated to ensure stable operation under continuous high loads.

[0115] This study uses a dataset containing 2156 data files, with the first 43 sets of data spaced 5 minutes apart and the subsequent 2113 sets spaced 10 minutes apart. Based on this characteristic, we chose a discrete signal vibration intensity calculation method for analysis. Each dataset contains 8 columns, totaling 20480 rows, with each pair of columns representing data measured at X and Y axis measurement points of a bearing. The data acquisition frequency was 20kHz, and the acquisition interval between each row was 0.05ms. Through this data, we will explore the vibration of the bearing at different time intervals, thus providing valuable data support for research in related fields.

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

[0117] The vibration intensity of the four bearings, calculated based on definitions, velocity, frequency domain acceleration, multi-directional velocity, energy, and impulse norm, exhibits a discrete state in the time domain. It is necessary to divide this into multiple time intervals of length L and calculate the energy E of the vibration intensity within each time interval. L :

[0118]

[0119] like Figures 2a-5e As shown, the vibration intensity energy spectrum is plotted by recording the vibration intensity over different time periods. Each time period's vibration signal corresponds to a specific energy value, and the continuous accumulation of these values ​​ultimately forms the entire energy spectrum. Analyzing the energy spectrum provides a more intuitive understanding of the vibration of a structure over different time periods, offering important reference for engineering design and structural evaluation.

[0120] Figures 2a-5e The vibration intensity values ​​of bearings 1, 2, 3, and 4 under different algorithms are shown. Figures 2a, 3a, 4a, and 5a show the vibration intensity changes processed by the velocity algorithm; figures 2b, 3b, 4b, and 5b show the changes processed by the frequency domain analysis algorithm; figures 2c, 3c, 4c, and 5c show the changes processed by the velocity vector analysis algorithm; figures 2d, 3d, 4d, and 5d show the changes processed by the energy analysis algorithm; and figures 2e, 3e, 4e, and 5e show the changes processed by the impulse norm algorithm. The trends of the images based on velocity, frequency domain, velocity vector, and energy are basically the same. The energy algorithm shows relatively higher sensitivity, while the impulse norm algorithm shows high sensitivity to the time of failure occurrence.

[0121] The figures show vibration intensity diagrams based on different parameters. Analyzing these diagrams provides a more comprehensive understanding of the vibration situation. Commonly used vibration parameters include definition, frequency domain acceleration, multi-directional velocity, energy, and impulse norm. By comparing the changes in these diagrams, the characteristics of vibration intensity can be identified, allowing for appropriate adjustments and improvements based on actual conditions. This data is crucial for engineering design and structural safety, helping us better prevent vibration-induced damage to buildings and equipment.

[0122] The images from the four methods exhibit similar trends in vibration intensity, with the images based on definition, multi-directional velocity, and energy showing almost identical shapes. This is because they are all based on the root mean square value of velocity to calculate vibration intensity, while velocity vector and energy are variations of the velocity method. Therefore, these methods show a high degree of similarity in measuring vibration intensity. This finding provides us with a deeper understanding and offers more ideas for future research, hopefully leading to better applications and results in practical engineering.

[0123] The ratio of peak value to effective value was calculated for five curves for each bearing to quantify the sensitivity of the vibration intensity calculation methods, facilitating a comparison of the sensitivity of the five methods to bearing faults. This method helps reveal the advantages and disadvantages of different calculation methods, providing objective evidence for fault detection. Specific data are shown in Table 1 below.

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

[0125]

[0126] Data analysis shows that among the methods for calculating peak values ​​from different perspectives, those based on velocity, frequency domain, and multi-directional velocity all exceed the normal value by more than 2 times, while the energy-based calculation method is close to 4.4 times.

Claims

1. A method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity, characterized in that, Includes the following steps: Step a: Use a vibration data acquisition unit to acquire the bearing's operating vibration data; Step b involves converting the vibration signal representing bearing failure into specific characteristic parameters, which then serve as the basis for determining whether a bearing failure has occurred. Step c, through the impulse norm The index indicates the occurrence of bearing vibration intensity. First, a proportionality coefficient Ratio is preset, then the number of pulses I is calculated based on this coefficient, and finally the pulse norm is obtained. This serves as the basis for assessing bearing vibration intensity and enabling fault diagnosis; impulse norm. The specific solution process is as follows: Step c1: Obtain the order statistics of the measured vibration signal. , represented as: in, The vibration signal being measured The amplitude signal; Step c2, impulse norm Defined as the sum of the mean of the first I largest amplitude data points and the total signal energy. The ratio is calculated using the following formula: ； Step d: Select the root mean square vibration intensity method, frequency domain vibration intensity method, multi-directional root mean square vibration intensity method, or energy intensity method to determine the vibration intensity of the rotating bearing, and then determine the occurrence of bearing failure.

2. The method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity according to claim 1, characterized in that, Step a specifically includes the following: Step a1: For machines with a rotational speed of less than 1000 RPM, displacement is used as the vibration data acquisition method. Step a2: For low-frequency vibration environments with rotational speeds between 1000 RPM and 10000 RPM, vibration signals of the shaft and bearing housing are collected, with velocity as the vibration data. Step a3: For high-frequency vibration environments above 10,000 RPM, the accelerometer captures high-frequency signals and uses acceleration as the vibration data acquisition.

3. The method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity according to claim 1, characterized in that, The root mean square velocity vibration intensity method described in step d specifically includes the following: When the discrete vibration velocity signal is known, the vibration intensity formula is written as: ， in This represents the vibration intensity value. The length of the discrete signal, The vibration velocity signal is discrete; when calculating the vibration intensity, the signal duration T and the discrete signal length N must be considered, while ensuring that the original continuous vibration signal is acquired. and discrete vibration signals Vibration intensity is described in mm / s or in / s; if dB is used, mm / s must be used as the reference standard; through accurate measurement and unit conversion, the condition of equipment can be assessed, providing key basis for predictive maintenance and early fault warning.

4. The method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity according to claim 1, characterized in that, The frequency domain vibration intensity method described in step d specifically includes the following: For a periodically continuous signal x(t), if the Dirichlet conditions are satisfied, it can be expressed in the form of a Fourier series: in It is a static variable. and These are the amplitudes of the sine and cosine signals, respectively. These are the frequencies of these signals; For the collected discrete vibration data signal x(n) with a sampling frequency of fs, a fast Fourier transform is used to convert it into a frequency domain signal: The single-sided amplitude spectrum obtained from the transformed frequency domain signal is as follows: The frequencies corresponding to the single-sided amplitude spectrum are: When the collected signal is an acceleration signal and the units are appropriate, its vibration intensity expression is as follows: in, The starting frequency, For span frequency.

5. The method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity according to claim 1, characterized in that, The multi-directional root mean square vibration intensity method described in step d specifically includes the following: When monitoring vibration data in three degrees of freedom, and when there are multiple measuring points in one degree of freedom or multiple degrees of freedom, the vibration intensity is expressed as follows: in, , , This 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. , , The number of measurement points corresponds to the x, y, and z directions, respectively.

6. The method for early fault diagnosis of rolling bearings based on pulse norm vibration intensity according to claim 1, characterized in that, The energy intensity method described in step d specifically includes the following: The vibrational energy of the system consists of two parts: kinetic energy and potential energy, with the root mean square value of velocity V. rms The effective value of kinetic energy can be reflected, eliminating the influence of the system's rotational kinetic energy; while the root mean square value of displacement is used to calculate the effective value of potential energy. Combining the two, the effective value of the vibration energy of the vibration system is obtained. : The variation trend of the natural frequency p reflects the transformation of vibration energy between different vibration modes. This change is derived by analyzing the relationship between a single bearing segment and a complete bearing, and is closely related to the system's stiffness k and mass distribution m. Natural frequency p x and p y , representing the inherent characteristics of the vibration system in the x and y directions, respectively, are closely related to the effective value of the vibration energy. Based on this relationship, a precise quantitative description of the effective value of the vibration energy of the vibration system is provided: 。

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