A method for generating bearing degradation system-level data based on component-level testing

Abstract

The present application relates to a kind of bearing degradation system level data generation method based on component level test, it is related to bearing signal measurement technical field, solve the technical problem that must carry out system level test in order to obtain bearing system level degradation signal in prior art.The present application first uses dynamics simulation software or the assembly level simulation noise signal of bearing, then based on bearing component level test device, obtains the component level vibration signal of bearing in full life cycle degradation;Then by extracting high sampling rate, narrow bandwidth under the rotating frequency characteristic signal, obtain the filter signal of simulation signal and component level test signal;On this basis, by the correct peak value that can represent the motion characteristics of bearing obtained by the proposed screening method, finally with these peak values as reference datum, obtain bearing degradation system level data, can avoid the problem that traditional method needs to carry out system overall assembly level test, improve model training data generation efficiency and reduce economic cost simultaneously.

Description

Technical Field

[0001] This invention relates to the field of bearing signal measurement technology, and in particular to a method for generating bearing degradation system-level data based on component-level testing. Background Technology

[0002] Bearings are critical components of rotating systems. Bearing failure can easily lead to deterioration of gear meshing characteristics and system performance, and even extremely serious damage such as broken teeth or shafts. Furthermore, bearing degradation patterns differ under different system operating conditions, making system-level fault diagnosis or prediction necessary for bearing health status. Current fault diagnosis / prediction methods heavily rely on large amounts of data to train artificial intelligence models. Therefore, most current methods involve conducting multiple rounds of testing on different assemblies to obtain the corresponding training data, which is costly and inefficient. Therefore, this invention proposes a method for generating system-level bearing degradation data based on component-level testing. By jointly simulating noise signal data and component-level test signal data, large-scale, low-cost, and high-efficiency generation of different system-level test databases can be achieved. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method for generating bearing degradation system-level data based on component-level testing. The method combines numerical simulation and component-level testing, which can avoid the problem of traditional methods requiring overall system-level testing, while improving the efficiency of model training data generation and reducing economic costs.

[0004] This invention provides a method for generating system-level data on bearing degradation based on component-level testing, the specific steps of which are as follows:

[0005] Step S1: Obtain the parameters and operating condition information of the rotating machinery system under test; perform dynamic simulation on the rotating machinery system under test, and formulate the simulation and test load spectrum of the bearings in the rotating machinery system under test; generate the simulation noise signal of the bearing assembly system.

[0006] Step S2: Perform component-level degradation tests on the bearing and obtain the vibration signals throughout the entire life cycle during the degradation process as component-level test vibration signals;

[0007] Step S3: Filter the simulated noise signal and the component-level test vibration signal respectively, extract the frequency conversion characteristic signal, and obtain the filtered signal;

[0008] Step S4: In the filtered signal, the absolute maximum point is detected by sliding window and marked as a candidate peak. False peaks and misidentified adjacent peaks in the candidate peaks are removed to obtain the correct peak sequence of bearing motion characteristics.

[0009] Step S5: Based on the correct peak sequence of the bearing motion characteristics, match the rotational frequency peak phase of the simulated noise signal and the component-level test vibration signal, determine the reference anchor point position, and trim the signal at the anchor point position; perform cyclic expansion on the simulated noise signal to obtain mixed noise data; based on the fusion of mixed noise and component-level test vibration signal, form system-level data for bearing degradation.

[0010] Optionally, step S1 includes the following specific steps:

[0011] Step S11: Decompose the rotating mechanical system under test into a transmission chain, transmission shaft and gears, and construct a gear meshing force source;

[0012] Step S12: Establish a dynamic model of the transmission chain assembly based on the gear meshing force source;

[0013] Step S13: Based on the operating condition information of the transmission chain, formulate the simulation and test load spectrum of the bearing;

[0014] Step S14: Solve the dynamic model of the transmission chain assembly based on the nonlinear differential equation, and extract the vibration response of the bearing position as the simulation noise signal of the bearing assembly level.

[0015] Optionally, step S2 includes the following steps: selecting a healthy bearing in the transmission chain of the rotating mechanical system under test that is the same as the bearing of interest as the component-level test bearing, and conducting a component-level degradation test on it until the component-level test bearing fails; collecting the vibration signal of the component-level test bearing during the component-level degradation test within the sampling time; merging the vibration signal data of the component-level test bearing to obtain a vibration signal dataset for the entire life cycle as the component-level test vibration signal.

[0016] Optionally, based on the principle of mechanical vibration, the characteristic frequency of a single-point fault in the bearing under operating conditions is calculated, and a vibration sensor is selected based on the characteristic frequency of the single-point fault. The selected vibration sensor is then used to collect the vibration signal of the component-level test bearing during the component-level degradation test process within the sampling time.

[0017] Optionally, in step S3, a filter is designed based on the bearing rotation frequency, and the filter is used for filtering.

[0018] Optionally, in step S4, a dynamic threshold is set based on the median peak amplitude to remove false peaks whose amplitudes are lower than the dynamic threshold among the candidate peaks; and an interval constraint rule is applied to remove misidentified adjacent peaks.

[0019] Optionally, in step S5, the peak value of the first stable cycle is selected as the reference anchor point in the correct peak value sequence of the bearing motion characteristics, the reference phase angle of the component-level test vibration signal corresponding to the peak value is calculated, and the peak value with the smallest phase difference is searched in the simulated noise signal of the bearing assembly system.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects:

[0021] (1) The bearing degradation system-level data generation method of the present invention obtains the vibration signal of the bearing under different health conditions through component-level tests, generates system-level simulation noise signal through simulation model, and aligns the two signals based on time-frequency domain characteristics to generate the system-level vibration signal of the bearing in the assembly, which can be used to train the fault diagnosis or prediction model, thereby realizing the system-level fault diagnosis or prediction of bearing faults.

[0022] (2) The bearing degradation data generation method of the present invention generates bearing assembly-level vibration signal data by combining component-level tests with noise data generated by assembly dynamics simulation, which significantly improves the efficiency of obtaining training data required for bearing system-level fault diagnosis or prediction models.

[0023] (3) The bearing degradation data generation method of the present invention does not require conducting corresponding bearing system-level tests for different assemblies. It only requires combining component-level tests with corresponding assembly-level noise simulation signals to obtain the vibration signals of the bearing in the corresponding assembly. Since the cost of component-level tests is much lower than that of assembly-level tests (on the one hand, the test bench is universal and there is no need to develop corresponding test benches for different assemblies; on the other hand, the method of applying load and speed in component-level tests is simple), the cost of obtaining model training data is significantly reduced. Attached Figure Description

[0024] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0025] Figure 1 This is a flowchart of the method for generating system-level data on bearing degradation based on component-level testing according to the present invention. Detailed Implementation

[0026] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0027] A specific embodiment of the present invention, such as Figure 1 This paper provides a method for generating system-level data on bearing degradation based on component-level testing. The specific steps are as follows:

[0028] Step S1: Obtain the parameters and representative operating condition information of the rotating machinery system under test; perform dynamic simulation on the rotating machinery system under test, and formulate the simulation and test load spectrum of the bearings in the rotating machinery system under test; generate the simulation noise signal of the bearing assembly system. The specific steps are as follows:

[0029] Step S11: Decompose the rotating mechanical system under test into a transmission chain, transmission shaft and gears, and construct a gear meshing force source.

[0030] The lumped parameter method is used to integrate the mass of the transmission shaft in the transmission chain onto the gear, and the gear meshing force element, including gear meshing stiffness and meshing damping, is calculated based on parameters such as gear material and geometry.

[0031] Step S12, establish a dynamic model of the transmission chain assembly based on the gear meshing force source, the expression of which is:

[0032]

[0033] in, This represents the overall mass matrix of the transmission chain; Here is the damping matrix of the transmission chain; Here is the stiffness matrix of the transmission chain; Let be the displacement vector of the transmission chain; Represents the velocity vector of the transmission chain; Represents the acceleration vector of the transmission chain; This refers to the external force vector of the transmission chain, including the gear meshing force source, the transmission chain housing support force, and external loads.

[0034] Furthermore, the external load in the external force vector of the system is the input obtained based on the working conditions, the stiffness matrix and damping matrix of the system are obtained based on the mechanical structure and dynamics principles of the transmission chain system, and the displacement of the system is the result calculated based on the mechanical structure and dynamics principles of the transmission chain system.

[0035] Step S13: Based on the representative working condition information of the transmission chain, formulate the simulation and test load spectrum of the bearing.

[0036] Based on representative working conditions of the transmission chain, the working characteristics of the bearings in the rotating mechanical system under test, such as rotational speed and dynamic load, are calculated, thereby formulating the load spectrum for bearing simulation and testing.

[0037] Step S14: Solve the dynamic model of the transmission chain assembly based on the nonlinear differential equation, and extract the vibration response of the bearing position as the simulation noise signal of the bearing assembly level.

[0038] Step S2: Perform component-level degradation tests on the bearing and obtain the vibration signals throughout the entire life cycle during the degradation process as component-level test vibration signals;

[0039] Specifically, step S2 includes:

[0040] Step S21: Based on the principle of mechanical vibration, calculate the characteristic frequency of a single-point fault of the bearing under representative working conditions, and select a vibration sensor based on the characteristic frequency of the single-point fault.

[0041] Step S22: Select a healthy bearing in the transmission chain that is the same model as the bearing of interest as the component-level test bearing. Conduct component-level tests according to the test load spectrum in step S13 until the component-level test bearing fails. During the test, the vibration signal of the component-level test bearing is collected by the vibration sensor selected in step 21 according to a fixed single sampling duration and sampling interval. The data of the vibration signal are merged to obtain the full life cycle vibration signal set as the component-level test vibration signal.

[0042] Understandably, the bearings of concern are the type of bearings that are particularly prone to failure among the various bearings used in the drive train, and healthy bearings of the same model as that bearing type are selected for component-level testing.

[0043] Step S3: Design an FIR bandpass filter based on the bearing rotation frequency to filter the simulated noise signal and the component-level test vibration signal respectively, so as to extract the rotation frequency characteristic signal under high sampling rate and narrow bandwidth and obtain the filtered signal.

[0044] Step S3 specifically includes:

[0045] Step S31: Design an FIR bandpass filter based on bearing rotation frequency.

[0046] First, the bearing frequency is obtained from the bearing rotational speed, expressed as:

[0047]

[0048] in, For bearing rotation frequency, This represents the bearing speed.

[0049] Then, based on the bearing rotation frequency, the passband range is designed to be... The expression for the FIR bandpass filter is:

[0050]

[0051] in, Let the filter order be . Sampling rate, This represents the floor operation.

[0052] Step S32: The Hanning window function is used to suppress spectral leakage. An FIR bandpass filter is used to perform zero-phase filtering on the simulation noise signal and the component-level test signal respectively, retaining the dominant frequency conversion component to obtain the frequency conversion characteristic signal, and thus obtaining the filtered signal.

[0053] Step S4: In the filtered signal, the sliding window detects the absolute maximum point and marks it as a candidate peak. Then, a dynamic threshold is set based on the median amplitude of the candidate peaks to remove false peaks with amplitudes lower than the dynamic threshold. Finally, the interval constraint rule is applied to remove misidentified adjacent peaks and obtain the correct peak that can characterize the bearing motion characteristics.

[0054] Step S4 specifically includes:

[0055] Step S41: Identify the absolute maximum point of the filtered signal to obtain the candidate peak value of the filtered signal.

[0056] Specifically, in a sliding window on the filtered signal, the point with the maximum absolute value among the consecutive positive or negative peaks in each window is detected and marked as a candidate peak, ensuring that each frequency cycle corresponds to a single peak.

[0057] Furthermore, the width of the sliding window is expressed as:

[0058]

[0059] in, The width of the sliding window.

[0060] Step S42: Remove false peaks from the candidate peaks according to the dynamic threshold to obtain the remaining peak sequence.

[0061] Specifically, a dynamic threshold is calculated based on the median of the candidate peak amplitudes, and pseudo-peaks with amplitudes lower than the dynamic threshold τ are eliminated to avoid misidentification due to sensor noise or transient interference.

[0062] Furthermore, the expression for the dynamic threshold is:

[0063]

[0064] in, For dynamic thresholds; For adjustment coefficients, preferably, ; Candidate peak sequences; This represents the absolute value of each element in the peak sequence; This represents the average value.

[0065] Step S43: Based on the signal peak interval constraint, the remaining peak sequence is filtered to obtain the correct peak that can characterize the bearing motion characteristics.

[0066] Specifically, the number of vibration signal samples corresponding to the frequency rotation period is calculated. Set the minimum allowable interval Traverse the remaining peak sequence; if the interval between adjacent peaks is less than the minimum allowed interval... If the peak value is larger than the adjacent peak value, then the peak value distribution will be retained to ensure that the peak value distribution conforms to the frequency rotation cycle.

[0067] Furthermore, the number of samples corresponding to the frequency switching cycle is:

[0068]

[0069] in, This indicates the number of samples corresponding to the frequency rotation cycle.

[0070] Furthermore, the expression for the minimum allowable interval is:

[0071]

[0072] in, For the peak error removal correction constant, preferably, ∈[5,20].

[0073] Step S5: Based on the correct peak value that can characterize the bearing motion characteristics, match the rotational frequency peak phase of the simulated noise signal and the component-level test vibration signal, determine the reference anchor point position, and trim the signal at the reference anchor point position to ensure time domain alignment; perform cyclic expansion on the simulated noise signal to generate mixed noise data of the same length as the component-level test vibration signal; based on the mixed fused noise and the component-level test vibration signal, form system-level data for bearing degradation.

[0074] Step S5 specifically includes:

[0075] Step S51: Match the rotational frequency phase anchor points of the simulated noise signal and the component-level test vibration signal. Select the peak value of the first stable cycle from the correct peak sequence that characterizes the bearing motion as the reference anchor point. Calculate the reference phase angle of the component-level test vibration signal corresponding to this peak value. Search for the peak value in the simulated noise signal of the bearing assembly system that has the smallest phase difference between the simulated noise signal and the component-level test vibration signal. <10 ∘ ), as a reference anchor point.

[0076] Furthermore, the expression for the reference phase angle of the component-level test vibration signal is:

[0077]

[0078] in, The reference phase angle for the vibration signal in the component-level test. This refers to the time corresponding to the peak value of the first stable cycle (i.e., the reference anchor point); It represents the remainder after dividing two numbers.

[0079] Step S52: Based on the reference anchor point position, trim the component-level test vibration signal and simulated noise signal obtained in step S2, and expand the simulated noise signal to obtain the aligned simulated noise signal and component-level test vibration signal.

[0080] Specifically, starting from the reference anchor point, a signal data segment of the required duration T is extracted from the unfiltered component-level test vibration signal and simulated noise signal. If the simulation time of the simulated noise signal is insufficient, it is cyclically extended to duration T according to the frequency rotation cycle to ensure time domain continuity.

[0081] Step S53: Generate mixed noise data.

[0082] Specifically, the aligned simulated noise signal is superimposed with the component-level test vibration signal in a proportional manner to generate noisy mixed data. The expression is:

[0083]

[0084] in, (t) represents the vibration signal obtained from the test at time t. (t) represents the simulated noise signal at time t. This is the noise mixing ratio coefficient.

[0085] Furthermore, the time-domain alignment accuracy is verified by calculating the cross-correlation coefficient, ensuring that the mixed data conforms to the characteristics of actual working conditions.

[0086] Step S54: Based on the mixed noise data and component-level test vibration signals, system-level data on bearing degradation is generated.

[0087] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for generating bearing degradation system level data based on component level testing, comprising: The specific steps are as follows: Step S1, obtaining the parameters and working condition information of the rotating mechanical system to be tested; performing dynamic simulation on the rotating mechanical system to be tested, and formulating the simulation and test load spectrum of the bearing in the rotating mechanical system to be tested; The specific steps of generating the bearing assembly system simulation noise signal include: Step S11, decomposing the rotating mechanical system to be tested into a transmission chain, a transmission shaft and a gear, and constructing a gear meshing force source; Step S12, establishing a transmission chain assembly dynamics model based on the gear meshing force source, and the expression is: wherein, is the total mass matrix of the driveline; is the damping matrix of the driveline; is the stiffness matrix of the driveline; is the displacement vector of the driveline; denotes the velocity vector of the driveline; denotes the acceleration vector of the driveline; is the external force vector of the driveline; Step S13, formulating the simulation and test load spectrum of the bearing according to the working condition information of the transmission chain; Step S14, solving the transmission chain assembly dynamics model according to the nonlinear differential equation, and extracting the vibration response of the bearing position as the bearing assembly level simulation noise signal; Step S2, performing component level degradation test on the bearing, and obtaining the full life cycle vibration signal in the degradation process as the component level test vibration signal; Step S3, filtering the simulation noise signal and the component level test vibration signal respectively, extracting the rotation frequency characteristic signal, and obtaining the filtered signal; Step S4, detecting the absolute value maximum points in the filtered signal in a sliding window, marking them as candidate peak values, eliminating the pseudo-peak values and misidentified adjacent peak values in the candidate peak values, and obtaining the correct peak value sequence of the bearing motion characteristic; Step S5, matching the rotation frequency peak value phase of the simulation noise signal and the component level test vibration signal according to the correct peak value sequence of the bearing motion characteristic, determining the reference anchor point position and cutting the signal at the anchor point position; performing cyclic extension on the simulation noise signal to obtain mixed noise data; and based on the fusion of the mixed noise and the component level test vibration signal, forming the bearing degradation system level data.

2. The method of claim 1, wherein, The specific steps of step S2 are as follows: selecting a healthy bearing of the same type as the bearing of interest in the transmission chain of the rotating mechanical system to be tested as a component level test bearing, performing component level degradation test on the bearing until the component level test bearing fails; collecting the component level test bearing vibration signal in the component level degradation test process within the sampling time; and merging the data of the component level test bearing vibration signal to obtain a full life cycle vibration signal dataset as the component level test vibration signal.

3. The method of claim 1 or 2, wherein, According to the principle of mechanical vibration, the single-point fault characteristic frequency of the bearing under the working condition is calculated, the vibration sensor is selected according to the single-point fault characteristic frequency, and the selected vibration sensor is used to collect the component level test bearing vibration signal in the component level degradation test process within the sampling time.

4. The method of claim 1, wherein, In step S3, a filter is designed based on the bearing rotation frequency, and the filter is used for filtering.

5. The method of claim 1, wherein, In step S4, a dynamic threshold is set based on the median of the peak amplitude, and the pseudo-peak values with amplitudes lower than the dynamic threshold are removed; and interval constraint rules are applied to remove misidentified adjacent peak values.

6. The method of claim 1, wherein, In step S5, the first peak value of a stable period in the correct peak value sequence of the bearing motion characteristic is selected as a reference anchor point, the reference phase angle of the component level test vibration signal corresponding to the peak value is calculated, and the peak value with the smallest phase difference in the bearing assembly system simulation noise signal is searched.

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