A method for self-adapting estimation of micro-fault size of rolling bearing

Abstract

The application discloses a kind of rolling bearing micro-fault size self-adapting estimation method, comprising: obtaining instantaneous angular velocity signal, filter signal under different window width;According to instantaneous angular velocity signal, filter signal under different window width, obtain the residual signal under different window width;The spectral concentration index of residual signal under different window width is calculated, and statistical kurtosis index;According to spectral concentration index, statistical kurtosis index, construct joint spectrum-statistical optimization index;According to joint spectrum-statistical optimization index, select optimization window width;Instantaneous angular velocity signal is filtered using the filter with optimization window width, and the residual signal after optimization is obtained;In the residual signal after optimization, the impact caused by rolling bearing fault is detected, and the exit point of each impact is determined;The MAD value of the residual signal after optimization is calculated;According to exit point and MAD value, the entry point of each impact is determined;Based on the exit point and entry point of impact, obtain rolling bearing fault size estimation value.The application can maintain relatively stable fault size estimation result under different speed range and different radial load conditions, and has good robustness and working condition adaptability.

Description

Technical Field

[0001] This invention relates to an adaptive estimation method for the size of minor faults in rolling bearings, belonging to the fields of fault diagnosis technology and signal processing technology. Background Technology

[0002] Low-speed and variable-speed operating conditions are common for rotating machinery. As a core component, the health of rolling bearings directly affects the operating accuracy, efficiency, and service life of the machinery. Therefore, accurately assessing the degree of rolling bearing failure under low-speed and variable-speed conditions can provide a method for rolling bearing condition monitoring and remaining life prediction, which has broad application prospects and significant research value.

[0003] On the one hand, vibration signals are often limited by the lower limit of the vibration sensor frequency, and may not be able to obtain rich fault information under low speed conditions; in addition, vibration signals picked up under variable speed conditions have non-stationary characteristics, which increases the difficulty of fault size estimation; furthermore, vibration signals are susceptible to the influence of time-varying transmission paths, resulting in severe multi-source coupling of signals.

[0004] On the other hand, encoder signals have advantages such as short transmission paths, no need for external installation, no need for periodic calibration, and direct correlation with dynamics. In recent years, fault diagnosis technology based on instantaneous angular velocity signals has developed rapidly.

[0005] Most existing studies use vibration signals for size estimation. Although some have achieved adaptive size estimation, these are all under constant speed. There are relatively few fault size estimation methods in the existing literature based on IAS signals, and they do not consider applications under low speed and variable speed combined conditions. At the same time, existing methods have not yet achieved effective breakthroughs in adaptive small fault size estimation.

[0006] In summary, adaptive and accurate estimation of the degree of rolling bearing failure is particularly important under combined low-speed and variable-speed operating conditions. Summary of the Invention

[0007] This invention provides an adaptive estimation method for the size of minor faults in rolling bearings, in order to solve the problem that it is difficult to adaptively and accurately estimate the size of rolling bearing faults under combined operating conditions of low speed and variable speed.

[0008] The technical solution of this invention is:

[0009] According to a first aspect of the present invention, an adaptive estimation method for the size of minor faults in rolling bearings is provided, comprising: acquiring an instantaneous angular velocity signal and filtered signals under different window widths; obtaining residual signals under different window widths based on the instantaneous angular velocity signal and the filtered signals under different window widths; calculating the spectral concentration index and statistical kurtosis index of the residual signals under different window widths; constructing a joint spectrum-statistical optimization index based on the spectral concentration index and the statistical kurtosis index; selecting an optimized window width based on the joint spectrum-statistical optimization index; filtering the instantaneous angular velocity signal using a filter with an optimized window width to obtain an optimized residual signal; detecting impacts caused by rolling bearing faults in the optimized residual signal and determining the exit point of each impact; calculating the MAD value of the optimized residual signal; determining the entry point of each impact based on the exit point and the MAD value; and obtaining an estimated value for the size of the rolling bearing fault based on the exit point and the entry point of the impact.

[0010] Furthermore, the method specifically includes:

[0011] S1. Obtain the instantaneous angular displacement and corresponding time, and calculate the instantaneous angular velocity signal using the forward difference method. ;

[0012] S2, Use different window widths Fixed filter order The SG filter for instantaneous angular velocity signals Filtering is performed to obtain filtered signals with different window widths. Based on the instantaneous angular velocity signal and the filtered signal under different window widths, the residual signal under different window widths is obtained;

[0013] S3, regarding the first The remaining signal corresponding to each window width The spectrum is obtained by performing a Fourier transform; then the normalized spectrum of the residual signal under different window widths is obtained using the following formula:

[0014] ;

[0015] in, Indicates the first The spectrum obtained by performing a Fourier transform on the residual signal within a window width. Indicates basis The obtained normalized spectrum;

[0016] S4. Based on the barrier characteristic frequency and the set of candidate spectral lines near its harmonics constructed according to the frequency band range and normalized spectrum, calculate the spectral concentration index. ;

[0017] S5, Calculate the first The remaining signal corresponding to each window width Statistical kurtosis index ;

[0018] S6. Construct a joint spectrum-statistical optimization index based on the spectral concentration index and the statistical kurtosis index. ;

[0019] ;

[0020] In the formula: Indicates the first The spectral concentration index corresponding to each window width; Indicates the first The statistical kurtosis index corresponding to the window width; α represents the weighting coefficient;

[0021] S7. Based on the joint spectrum-statistical optimization index, select the optimal window width. ;

[0022] S8, featuring optimized window width The SG filter for instantaneous angular velocity signals Filtering is performed to obtain the optimized residual signal. ;

[0023] S9. In the optimized residual signal The system detects impacts caused by rolling bearing failures and uses the x-coordinate corresponding to the minimum impact value as the exit point. ;

[0024] S10. Calculate the remaining signal after optimization. MAD value;

[0025] S11. For the remaining signal after optimization Each impact in, to the exit point Starting from the beginning, search backwards along the time axis to find the first intersection point between the signal amplitude and the MAD value when the signal amplitude changes from decreasing to increasing, and use the x-coordinate of this intersection point as the entry point. ;

[0026] S12, based on the entry point corresponding to the j-th impact. and exit point Calculate the corresponding fault width ; All detected in the remaining signal after optimization The average of the fault widths corresponding to each impact is used to obtain the estimated value of the rolling bearing fault size. .

[0027] Furthermore, the spectral concentration index The expression is:

[0028] ;

[0029] In the formula: The set of candidate spectral lines near the barrier characteristic frequency and its harmonics, constructed based on the normalized spectrum, is expressed as: ; This indicates the characteristic frequency of failure in the outer ring of a rolling bearing; This indicates the operation of taking the maximum value; h is the harmonic order of interest; Indicates frequency tolerance; Indicates the frequency band range.

[0030] Furthermore, the statistical kurtosis index The expression is:

[0031] ;

[0032] In the formula: This indicates the operation of calculating the mean. Indicates the remaining signal The mean; Indicates the first The remaining signal corresponding to each window width; This indicates the window width parameter number of the SG filter.

[0033] Furthermore, the weighting coefficient is expressed as follows: .

[0034] Furthermore, the optimal window width is selected based on the joint spectral-statistical optimization index. The expression is:

[0035] ;

[0036] In the formula: This indicates the operation of calculating the mean. This indicates that the objective function Optimized window width corresponding to the minimum value ; This indicates the window width parameter number corresponding to the optimized window width.

[0037] Furthermore, the fault width expression is:

[0038] ;

[0039] In the formula, This represents the fault width of the j-th impact; Indicates the inner diameter of the bearing outer ring; This represents the correction factor.

[0040] According to a second aspect of the present invention, an adaptive estimation system for the size of minor faults in rolling bearings is provided, comprising: an acquisition module for acquiring an instantaneous angular velocity signal and filtered signals under different window widths; a first acquisition module for acquiring residual signals under different window widths based on the instantaneous angular velocity signal and the filtered signals under different window widths; a first calculation module for calculating the spectral concentration index and statistical kurtosis index of the residual signals under different window widths; a construction module for constructing a joint spectrum-statistical optimization index based on the spectral concentration index and the statistical kurtosis index; a selection module for selecting an optimized window width based on the joint spectrum-statistical optimization index; a second acquisition module for filtering the instantaneous angular velocity signal using a filter with an optimized window width to obtain an optimized residual signal; a first determination module for detecting impacts caused by rolling bearing faults in the optimized residual signal and determining the exit point of each impact; a second calculation module for calculating the MAD value of the optimized residual signal; a second determination module for determining the entry point of each impact based on the exit point and the MAD value; and a third acquisition module for obtaining an estimated value of the size of the rolling bearing fault based on the exit point and the entry point of the impact.

[0041] According to a third aspect of the present invention, a processor is provided for performing operations including the step of performing the adaptive estimation method for minor fault dimensions of rolling bearings as described in any one of the preceding claims.

[0042] The beneficial effects of this invention are as follows: This invention constructs a rolling bearing fault size estimation method based on encoder instantaneous angular velocity signals. Compared to traditional vibration signal methods, it can more effectively preserve fault-induced impact characteristics under low-speed and variable-speed conditions, improving the detection capability of minor faults. Furthermore, based on the fault characteristic frequency and its harmonic candidate spectral line set constructed according to the frequency band range and normalized spectrum, the spectral concentration index is calculated, which can effectively enhance the dominant fault frequency components while suppressing random noise and trend term interference, thereby improving the extraction capability of weak impact features. Moreover, through the joint spectral-statistical optimization index combining the constructed spectral concentration index and the statistical kurtosis index, the adaptive optimization selection of the SG filter window width parameter is achieved, avoiding the problem of the window width relying on manual experience setting in traditional methods, and improving the adaptive capability of parameter selection under different operating conditions. Furthermore, the adaptive optimization of the SG filter parameters... The filter window width can be flexibly adjusted according to signal characteristics, enabling accurate extraction of fault information even under non-stationary operating conditions. Based on the above, combined with the MAD index, the impact entry and exit points in the optimized remaining signal are adaptively identified, effectively quantifying abnormal changes in the signal and adaptively estimating the fault size. This overcomes the problem of traditional methods being unable to adaptively estimate the size under low-speed and variable-speed combined operating conditions. As can be seen from the above, this invention can maintain relatively stable fault size estimation results under different speed ranges and different radial load conditions, exhibiting good robustness and adaptability to operating conditions. It is particularly suitable for reliable assessment of small fault sizes in rolling bearings under low-speed and variable-speed conditions. Furthermore, this invention does not require complex training models or a large number of prior samples, has a simple calculation process, and is convenient for engineering implementation, providing effective technical support for rotating machinery condition monitoring, early fault warning, and equipment health assessment. Attached Figure Description

[0043] Figure 1 An experimental platform provided according to an embodiment of the present invention;

[0044] Figure 2 The faulty bearing has a fault width of 0.3 mm.

[0045] Figure 3 This is a schematic diagram of the instantaneous angular velocity signal;

[0046] Figure 4 The graph shows the window width selection results corresponding to the joint spectrum-statistical optimization index under the working condition of 0-30rpm and 500N radial load.

[0047] Figure 5 The remaining signal after optimization Schematic diagram;

[0048] Figure 6This is a schematic diagram of the entry and exit points of five impacts calculated using the method of this invention under a radial load condition of 0-30 rpm and 500 N.

[0049] Figure 7 The fault width results calculated by this invention are used under different rotational speeds and different radial load conditions.

[0050] Figure 8 This is a flowchart of the method of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0052] Example 1: As Figures 1-8 As shown, according to a first aspect of the present invention, an adaptive estimation method for minor fault size of rolling bearings is used to adaptively estimate the size of the outer ring fault of the rolling bearing in the test bench; wherein, minor fault size refers to fault width less than 0.5 mm.

[0053] Adopting such Figure 1 The experimental setup shown is used for verification. The setup includes a motor, motor shaft, bearing housing, radial load device, encoder, DC power supply, and data acquisition card. The DC power supply powers the encoder. Bearings are mounted on the motor shaft via the bearing housing, and the motor drives the bearings on the motor shaft to rotate. The encoder is mounted on a shaft connected to the motor; the encoder model is ETF100-H851007B, and the encoder pulses per revolution are... The value is 10000; the radial load device is used to apply radial load to the motor shaft.

[0054] like Figure 2 As shown, the faulty bearing model used is 6205. To simulate a rolling bearing outer ring fault, a fault width of 0.3mm was machined onto the bearing outer ring using wire cutting. The relevant parameters of the 6205 rolling bearing are shown in Table 1 below. The motor operates at a constant acceleration of 10m / s². 2 The speed can be adjusted from 0 to 30 rpm, and the radial load applied to the shaft is 500 N.

[0055] Table 1 Relevant parameters of 6205 rolling bearing

[0056]

[0057] refer to Figure 8 The steps for adaptively estimating the outer ring fault of the rolling bearing on the test bench using an adaptive estimation method for minor rolling bearing fault dimensions are as follows:

[0058] S1. With a fault width of 0.3mm, the sampling rate is 5×10⁻⁶. 6 The Hz Picoscope acquisition card obtains instantaneous angular displacement and corresponding time, and calculates the instantaneous angular velocity signal using the forward difference method. ,See Figure 3 ;

[0059] ;

[0060] In the formula: Indicates the first Instantaneous angular velocity (IAS) at a given moment; E represents the number of pulses per encoder revolution; This represents the instantaneous angular displacement acquired by the data acquisition card. Indicates adjacent time intervals; and They represent the first The, the That moment.

[0061] S2, Use different window widths Fixed filter order The SG filter for instantaneous angular velocity signals Filtering is performed to obtain filtered signals with different window widths. The calculation formula is:

[0062] ;

[0063] In the formula: This represents the maximum value of a fixed filtering order. H represents the filter coefficients, and their expression is: T represents the transpose operation, where A T It can be defined as: .

[0064] in, .

[0065] Furthermore, the residual signal under different window widths is obtained using the following formula. :

[0066] ;

[0067] In an embodiment of the present invention, Indicates the window width parameter number of the SG filter, window width The value is varied in increments of 2 within the range of [5,501] to satisfy the requirement that the window width of the SG filter is an odd number (e.g., Take 5. ; Take 6. And so on.

[0068] S3, Residual signal under different window widths The spectrum is obtained by performing Fourier transforms on each signal. Furthermore, the residual signal under different window widths is obtained using the following formula. Normalized spectrum:

[0069] ;

[0070] in, Indicates the first The spectrum obtained by performing a Fourier transform on the residual signal within a window width. Indicates basis The obtained normalized spectrum.

[0071] S4. Based on the barrier characteristic frequency and the set of candidate spectral lines near its harmonics constructed according to the frequency band range and normalized spectrum, calculate the spectral concentration index:

[0072] ;

[0073] In the formula: The set of candidate spectral lines near the barrier characteristic frequency and its harmonics, constructed based on the normalized spectrum, is calculated using the following formula:

[0074] ;

[0075] This indicates the characteristic frequency of failure in the outer ring of a rolling bearing; This indicates the operation of taking the maximum value; h is the harmonic order of interest; Indicates frequency tolerance (within ±2%); This represents the lower limit of the fault frequency search interval on the normalized spectral amplitude of the remaining signal corresponding to the k-th window width; This represents the upper limit of the fault frequency search interval on the normalized spectral amplitude of the remaining signal corresponding to the k-th window width.

[0076] B represents the frequency band range, which is calculated using the following formula:

[0077]

[0078] In the formula, The characteristic frequency of the outer ring failure of the rolling bearing is represented by the following formula:

[0079] ;

[0080] In the formula: Indicates the number of rolling elements; Indicates the diameter of the rolling element; Indicates the bearing pitch diameter; Indicates the bearing contact angle; This indicates the transmission ratio between the encoder mounting position and the rolling bearing (taken as 1 in this invention);

[0081] S5. The number is calculated using the following formula. The remaining signal corresponding to each window width Statistical kurtosis index :

[0082] ;

[0083] In the formula: This indicates the operation of calculating the mean. Indicates the remaining signal The mean; Indicates the first The remaining signal corresponding to each window width; This indicates the window width parameter number of the SG filter.

[0084] S6. Construct a joint spectrum-statistical optimization index based on the spectral concentration index and the statistical kurtosis index. ;

[0085] ;

[0086] In the formula: F k sp Indicates the first The spectral concentration index corresponding to the window width; F k st Indicates the first The statistical kurtosis index corresponding to each window width; α represents the weighting coefficient, calculated by the following formula:

[0087] ;

[0088] S7. Based on the joint spectrum-statistical optimization index, select the optimal window width. ;

[0089] ;

[0090] In the formula: This indicates the operation of calculating the mean. This indicates that the objective function Optimized window width corresponding to the minimum value ; This indicates the window width parameter number corresponding to the optimized window width.

[0091] The results are as follows Figure 4 As shown, the optimized window width under the conditions of 0-30 rpm and 500 N radial load is... It is 175;

[0092] S8, featuring optimized window width The SG filter for instantaneous angular velocity signal w i Filtering is performed to obtain the optimized residual signal. ,See Figure 5 ;

[0093] S9. In the optimized residual signal The system detects impacts caused by rolling bearing failures and uses the x-coordinate corresponding to the minimum impact value as the exit point. , represented as:

[0094] ;

[0095] In the formula, This represents the exit point corresponding to the j-th impact (corresponding to...). Figure 6 (Exit point); Represents the optimized residual signal The j-th impact caused by a rolling bearing failure is detected in the middle. Indicates to make The x-coordinate (angular position) at which the minimum value is obtained; N impact Represents the optimized residual signal rk op The number of valid impacts detected.

[0096] S10. Calculate the remaining signal after optimization using the following formula. MAD value:

[0097] ;

[0098] In the formula: This indicates median calculation; it calculates the optimized residual signal. The MAD value is 0.072679.

[0099] S11. For the remaining signal after optimization Each impact in, to the exit point Starting from the beginning, search backwards along the time axis to obtain the first intersection point between the signal amplitude and the MAD value when the signal amplitude changes from decreasing to increasing, and then assign the x-coordinate of that intersection point to the MAD value. As an entry point (correspond Figure 6 The middle Enter point can be represented as:

[0100] ;

[0101] pass Figure 6 It can be verified that the method of the present invention can effectively identify the impact entry point and exit point, thereby calculating the fault width of the rolling bearing.

[0102] S12, based on the entry point corresponding to the j-th impact. and exit point The width of the corresponding fault is calculated using the following formula. :

[0103] ;

[0104] In the formula, This represents the fault width of the j-th impact; Indicates the inner diameter of the bearing outer ring; As a correction factor, it is related to the cage angular velocity v. cage and shaft angular velocity v shaft Related;

[0105] Among them, the cage angular velocity and shaft angular velocity The relationship is:

[0106] ;

[0107] ;

[0108] In the formula: d is the diameter of the rolling element, and D is the bearing pitch diameter. It represents the contact angle.

[0109] For the optimized remaining signal rk op All N detected in impact The average of the fault widths corresponding to each impact is used to obtain the estimated value L of the rolling bearing fault size:

[0110] ;

[0111] In the formula: Represents the optimized residual signal rk op The number of valid impacts detected.

[0112] To verify the effectiveness of the method of the present invention in estimating the size of minor faults in rolling bearings under low-speed variable operating conditions, the instantaneous angular velocity signal from Example 1 was used for fault width estimation. The remaining signal rk after optimization was then analyzed. op Multiple fault impacts were detected, and the fault width corresponding to each impact was calculated. The average value is then used as the final fault size estimate.

[0113] Table 2 shows the estimated fault width, average value, and absolute deviation for each impact when the actual fault width is 0.3 mm. As can be seen from Table 2, the fault width estimation results obtained by the method of the present invention for different impacts have good consistency. The estimated values ​​for each impact are concentrated near the actual fault size, with an average estimated value of 0.352 mm and an absolute deviation of 0.052 mm. This indicates that the method of the present invention can effectively achieve adaptive estimation of the small fault size of rolling bearings under low-speed conditions. The results are shown in Table 2 below:

[0114] Table 2

[0115]

[0116] According to a second aspect of the present invention, an adaptive estimation system for the size of minor rolling bearing faults is provided, comprising: an acquisition module for acquiring an instantaneous angular velocity signal and filtered signals under different window widths; a first acquisition module for acquiring residual signals under different window widths based on the instantaneous angular velocity signal and the filtered signals under different window widths; a first calculation module for calculating the spectral concentration index and statistical kurtosis index of the residual signals under different window widths; a construction module for constructing a joint spectrum-statistical optimization index based on the spectral concentration index and the statistical kurtosis index; a selection module for selecting an optimized window width based on the joint spectrum-statistical optimization index; a second acquisition module for filtering the instantaneous angular velocity signal using a filter with an optimized window width to obtain an optimized residual signal; a first determination module for detecting impacts caused by rolling bearing faults in the optimized residual signal and determining the exit point of each impact; a second calculation module for calculating the MAD value of the optimized residual signal; a second determination module for determining the entry point of each impact based on the exit point and the MAD value; and a third acquisition module for obtaining an estimated size of the rolling bearing fault based on the exit point and the entry point of the impact. For any parts of the modules not described in detail above, please refer to the relevant descriptions in this embodiment.

[0117] According to a third aspect of the present invention, a processor is provided for performing operations including the step of performing the adaptive estimation method for minor fault size of rolling bearings as described in any one of the foregoing embodiments.

[0118] Example 2: Based on Example 1, in order to further verify the adaptability and robustness of the method of the present invention under different operating conditions, fault size estimation analysis was further performed on the instantaneous angular velocity signals collected under different speed ranges and different radial load conditions.

[0119] To further verify the effectiveness and robustness of the present invention, the method of the present invention was used to test different rotational speeds (0-10 rpm, 0-30 rpm, and 0-50 rpm) at a constant acceleration of 10 m / s². 2 Fault width was estimated using instantaneous angular velocity signals collected under various conditions (speed regulation within a corresponding range to achieve variable speed) and different radial loads (500N, 600N, 700N, 800N). The results are shown in Table 3 below. An error bar chart was plotted using the impact values ​​and their average values ​​for each data set. Figure 7 As shown, although changes in radial load and rotational speed will have some impact on the estimation results, they do not weaken the effectiveness of the method of this invention in adaptive estimation of fault size. This method exhibits good robustness and adaptability under different operating conditions, enabling reliable estimation of small fault sizes in rolling bearings under low and variable speed conditions, while providing strong support for the accurate identification of small-sized faults under high load conditions.

[0120] Table 3

[0121]

[0122] In summary, the method of this invention has been experimentally verified under different speed ranges and radial load conditions, demonstrating that it can achieve adaptive estimation of the size of minor faults in rolling bearings under low-speed and variable-speed operating conditions. Even under conditions of speed variation and load disturbance, the method of this invention can still effectively maintain the fault impact characteristics, improve the stability and accuracy of fault size estimation results, and demonstrate good robustness and adaptability to operating conditions.

[0123] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Those skilled in the art can make various modifications or equivalent substitutions without departing from the spirit and scope of the present invention, and all such modifications or equivalent substitutions should fall within the scope of protection of the present invention.

Claims

1. An adaptive estimation method for the size of minor faults in rolling bearings, characterized in that, include: Acquire instantaneous angular velocity signals and filtered signals under different window widths; based on the instantaneous angular velocity signals and filtered signals under different window widths, obtain residual signals under different window widths; calculate the spectral concentration index and statistical kurtosis index of the residual signals under different window widths; construct a joint spectrum-statistical optimization index based on the spectral concentration index and statistical kurtosis index; select an optimized window width based on the joint spectrum-statistical optimization index; filter the instantaneous angular velocity signals using a filter with an optimized window width to obtain the optimized residual signals; detect impacts caused by rolling bearing failures in the optimized residual signals and determine the exit point of each impact; calculate the MAD value of the optimized residual signals; determine the entry point of each impact based on the exit point and MAD value; obtain the estimated size of the rolling bearing failure based on the exit point and entry point of the impact.

2. The adaptive estimation method for minor fault dimensions of rolling bearings according to claim 1, characterized in that, The method specifically includes: S1. Obtain the instantaneous angular displacement and corresponding time, and calculate the instantaneous angular velocity signal using the forward difference method. ; S2, Use different window widths Fixed filter order The SG filter for instantaneous angular velocity signals Filtering is performed to obtain filtered signals with different window widths. Based on the instantaneous angular velocity signal and the filtered signal under different window widths, the residual signal under different window widths is obtained; S3, regarding the first The remaining signal corresponding to each window width The spectrum is obtained by performing a Fourier transform; then the normalized spectrum of the residual signal under different window widths is obtained using the following formula: ； in, Indicates the first The spectrum obtained by performing a Fourier transform on the residual signal within a window width. Indicates basis The obtained normalized spectrum; S4. Based on the barrier characteristic frequency and the set of candidate spectral lines near its harmonics constructed according to the frequency band range and normalized spectrum, calculate the spectral concentration index. ; S5, Calculate the first The remaining signal corresponding to each window width Statistical kurtosis index ; S6. Construct a joint spectrum-statistical optimization index based on the spectral concentration index and the statistical kurtosis index. ; ； In the formula: Indicates the first The spectral concentration index corresponding to each window width; Indicates the first The statistical kurtosis index corresponding to the window width; α represents the weighting coefficient; S7. Based on the joint spectrum-statistical optimization index, select the optimal window width. ; S8, featuring optimized window width The SG filter for instantaneous angular velocity signals Filtering is performed to obtain the optimized residual signal. ; S9. In the optimized residual signal The system detects impacts caused by rolling bearing failures and uses the x-coordinate corresponding to the minimum impact value as the exit point. ; S10. Calculate the remaining signal after optimization. MAD value; S11. For the remaining signal after optimization Each impact in, to the exit point Starting from the beginning, search backwards along the time axis to find the first intersection point between the signal amplitude and the MAD value when the signal amplitude changes from decreasing to increasing, and use the x-coordinate of this intersection point as the entry point. ; S12, based on the entry point corresponding to the j-th impact. and exit point Calculate the corresponding fault width ; All detected in the remaining signal after optimization The average of the fault widths corresponding to each impact is used to obtain the estimated value of the rolling bearing fault size. .

3. The adaptive estimation method for minor fault dimensions of rolling bearings according to claim 2, characterized in that, The spectrum concentration index The expression is: ； In the formula: The set of candidate spectral lines near the barrier characteristic frequency and its harmonics, constructed based on the normalized spectrum, is expressed as: ; This indicates the characteristic frequency of failure in the outer ring of a rolling bearing; This indicates the operation of taking the maximum value; h is the harmonic order of interest; Indicates frequency tolerance; Indicates the frequency band range.

4. The adaptive estimation method for minor fault dimensions of rolling bearings according to claim 2, characterized in that, The statistical kurtosis index The expression is: ； In the formula: This indicates the operation of calculating the mean. Indicates the remaining signal The mean; Indicates the first The remaining signal corresponding to each window width; This indicates the window width parameter number of the SG filter.

5. The adaptive estimation method for minor fault dimensions of rolling bearings according to claim 2, characterized in that, The weighting coefficient is expressed as follows: .

6. The adaptive estimation method for minor fault dimensions of rolling bearings according to claim 2, characterized in that, The optimal window width is selected based on the joint spectrum-statistical optimization index. The expression is: ； In the formula: This indicates the operation of calculating the mean. This indicates that the objective function Optimized window width corresponding to the minimum value ; This indicates the window width parameter number corresponding to the optimized window width.

7. The adaptive estimation method for minor fault dimensions of rolling bearings according to claim 2, characterized in that, The fault width expression is: ； In the formula, This represents the fault width of the j-th impact; Indicates the inner diameter of the bearing outer ring; This represents the correction factor.

8. An adaptive estimation system for minute fault dimensions of rolling bearings, characterized in that, include: The acquisition module is used to acquire instantaneous angular velocity signals and filtered signals under different window widths; The first acquisition module is used to obtain the remaining signal under different window widths based on the instantaneous angular velocity signal and the filtered signal under different window widths; The first calculation module is used to calculate the spectral concentration index and statistical kurtosis index of the remaining signal under different window widths; The module is used to construct a joint spectrum-statistical optimization index based on the spectrum concentration index and the statistical kurtosis index. The selection module is used to select the optimal window width based on the joint spectrum-statistical optimization index; The second acquisition module is used to filter the instantaneous angular velocity signal using a filter with an optimized window width to obtain the optimized residual signal; The first determining module is used to detect impacts caused by rolling bearing failure in the optimized remaining signal and determine the exit point of each impact. The second calculation module is used to calculate the MAD value of the optimized remaining signal; The second determining module is used to determine the entry point of each impact based on the exit point and the MAD value; The third acquisition module is used to obtain the estimated size of the rolling bearing failure based on the exit and entry points of the impact.

9. A processor, characterized in that, The processor is used to perform operations, the operations including the step of performing the adaptive estimation method for minor fault size of rolling bearings as described in any one of claims 1-7.

Images