Measurement method of oil film thickness and pressure distribution on bearing surface based on ultrasonic guided waves

Abstract

The present invention discloses a method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves. The specific steps are as follows: Step 1, embedding a piezoelectric array on the back of the bearing to be measured; Step 2, collecting a reference signal, changing the oil film pressure and film thickness, and collecting a guided wave signal; Step 3, obtaining a noise signal, and performing trend noise fitting on the noise signal; Step 4, after the trend noise fitting is completed, establishing a segmented RMS index to extract pressure and film thickness features; Step 5, importing the extracted guided wave data into a convolutional neural network in deep learning to decouple the coupling features, and using a RAPID imaging algorithm based on the principle of probabilistic damage imaging to image the bearing surface to complete the measurement. The present invention's method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves can accurately calculate the thickness of the oil film and infer the oil film pressure.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultrasonic guided waves, and in particular relates to a method for measuring the oil film thickness and pressure distribution on a bearing surface based on ultrasonic guided waves. Background Art

[0002] Sliding bearings are key components in many mechanical systems, and their performance directly impacts the efficiency and lifespan of the machinery. The oil film pressure of sliding bearings affects the bearing's load-bearing capacity and operational stability, while the oil film thickness determines the lubrication effect and frictional resistance. Therefore, the oil film pressure and thickness of sliding bearings are important indicators for measuring whether the sliding bearing is operating properly. Existing ultrasonic oil film detection technology calculates the distance between the upper and lower surfaces of the oil film by using the time difference in reflection of ultrasonic body waves. Its measurement accuracy places extremely high demands on the ultrasonic emission and sampling rate, resulting in high costs and accuracy that is significantly affected by the monitoring environment. Furthermore, its detection range is limited by the propagation distance of ultrasonic body waves, and each measuring point can only detect parameters such as local oil film thickness and pressure. Therefore, it is difficult to promote in practical applications.

[0003] Ultrasonic guided wave testing technology uses the propagation characteristics of Lamb waves, which have low attenuation and long propagation distances along the propagation path in plate-like media, to detect changes in waveguide properties along the propagation path. On the surface of a sliding bearing bush, the oil film thickness and pressure affect the dispersion characteristics and energy leakage of the propagating Lamb waves, thereby affecting their propagation speed and the energy attenuation characteristics of each frequency component. Therefore, the oil film thickness and pressure can be inferred by analyzing the propagation time and waveform characteristics of the Lamb waves propagating on the bush surface. Ultrasonic guided wave testing technology requires a much lower operating frequency for Lamb waves than ultrasonic body wave testing methods. Ultrasonic wave transmission and reception can be achieved through a low-cost piezoelectric chip embedded in the bottom of the bush contact surface. Furthermore, a piezoelectric array can be used to detect the oil film pressure and thickness of the entire bush surface. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves, which can perform real-time monitoring under the operating state of the bearing, obtain the pressure distribution and oil film thickness at any point on the bearing surface, and avoid affecting the propagation speed of the Lamb wave.

[0005] The technical solution adopted by the present invention is a method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves, which is specifically implemented according to the following steps:

[0006] Step 1: embedding and arranging a piezoelectric array on the back of the bearing to be tested;

[0007] Step 2: Collect the guided wave signal under the set working condition of the oil film as the reference signal. After the reference signal is collected, the pressure and film thickness of the oil film are changed to collect the guided wave signals under different pressures and film thicknesses.

[0008] Step 3, obtaining the noise signal in the guided wave signal under different pressures and different film thicknesses in step 2, and performing trend noise fitting on the noise signal;

[0009] Step 4: After trend noise fitting is completed, a segmented RMS index is established to extract the coupled RMS sensitive features of pressure and film thickness;

[0010] In step 5, the extracted guided wave data containing the coupled RMS sensitive features is imported into the convolutional neural network in deep learning to decouple the coupling features, and the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to image the bearing surface to complete the measurement.

[0011] The technical solution of the present invention is also characterized in that:

[0012] Step 1 is specifically as follows: a sensor array consisting of N pairs of piezoelectric sensors arranged symmetrically on the left and right sides of the bearing to be tested is embedded in the back of the bearing to be tested, and the excitation of the guided wave signal and data acquisition are controlled by the host computer.

[0013] Step 2 is as follows: Select the operating range of the oil film pressure of 1-20 MPa and the oil film thickness of 10-200 μm, use a pair of sensors with horizontal paths in the piezoelectric sensor array as the transmitting end and the receiving end, first, collect the guided wave signal within the operating range as the reference signal, and then change the oil film pressure and oil film thickness by 1 MPa each time, and collect the guided wave signals under different pressures and different film thicknesses.

[0014] Step 3 is specifically as follows: a polynomial fitting function based on the least squares method is used to fit the noise interference. The order of the polynomial fitting is 4 to 7. The difference between the original signal and the electromagnetic interference is the actual signal.

[0015] Step 4 is as follows: the waveguide signal is divided into multiple windows of fixed length in the time domain. The entire signal is divided into k segments for processing. The root mean square (RMS) value is calculated in each time window. The changes under different pressure and film thickness conditions are analyzed by comparing the RMS value of the entire signal with the local RMS value after segmentation. The RMS value in each time window is used as a feature to construct a feature matrix.

[0016] The formula for calculating the RMS value in each time window in step 4 is:

[0017] (1)

[0018] Where, k is the number of windows into which the waveguide signal is divided in the time domain.

[0019] Step 5 is specifically as follows: the feature matrix of the extracted coupling characteristic information about pressure and film thickness is used as the input feature of the neural network, and the convolutional neural network in deep learning is used for decoupling to obtain the relationship between the pressure distribution on the bearing surface and the change of the guided wave signal waveform, as well as the relationship between the oil film thickness on the bearing surface and the change of the guided wave signal waveform. Finally, the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to establish the oil film pressure distribution and oil film thickness distribution map on the bearing surface.

[0020] Step 5: The RAPID imaging algorithm based on the principle of probabilistic damage imaging is as follows:

[0021] S1, calculate the difference signal on the excitation-sensing path, and calculate the wavelet coefficient modulus curve of the difference signal to obtain the amplitude information time of the difference signal;

[0022] Difference signal The calculation method of the wavelet coefficient modulus curve is as follows:

[0023] (2)

[0024] (3)

[0025] Where, is the difference signal The modulus of the wavelet coefficients, a is the scale parameter of the wavelet, and its value is related to the center frequency of the Lamb wave. b is the translation parameter of the wavelet, is the mother wavelet function Conjugation after expansion and contraction;

[0026] The expression is as follows:

[0027] (4)

[0028] Where, a is the scale parameter of the wavelet, b is the translation parameter of the wavelet;

[0029] Mother wavelet function Morlet continuous complex wavelet is often selected as the wavelet function. Through Morlet continuous complex wavelet transform, the envelope of the difference signal can be obtained to obtain the coupling characteristic information of oil film pressure and oil film thickness at each time point;

[0030] S2, for the difference signal The wavelet coefficient modulus curve is normalized, and the specific formula is as follows:

[0031] (5)

[0032] Secondly, for any coordinate point in the bearing to be tested The surface pressure and film thickness from the actuator To the receiving exciter , the propagation time of Lamb wave in the path of excitation-any point on the bearing surface-receiving can be expressed as:

[0033] (6)

[0034] Where, t 0 is the time offset during Lamb wave excitation, v g is the group velocity of Lamb wave;

[0035] By using the sensor array and the probability superposition method, the bearing surface at any point can be calculated. Corresponding changes in pressure and oil film thickness , the specific calculation method is as follows:

[0036] (7)

[0037] Where N is the number of piezoelectric sensors. The pressure coordinates are determined by superimposing the pressure and oil film thickness probability maps established by multiple excitation sensing paths.

[0038] Finally, the calculated pressure and oil film thickness changes are As input parameters, they are input into the convolutional neural network in deep learning, and the coupling values ​​of the oil film pressure characteristics and the oil film thickness characteristics are decoupled. The relationship between the pressure field on the bearing surface to be measured and the change of the guided wave waveform, and the relationship between the film thickness distribution on the bearing surface and the guided wave waveform are obtained. The pressure distribution and oil film thickness at any point on the bearing surface are obtained to complete the measurement.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention provides a method for measuring the thickness and pressure distribution of the oil film on the bearing surface based on ultrasonic guided waves, which infers the thickness and pressure of the oil film by analyzing the propagation time and waveform characteristics of the ultrasonic wave. The measurement of the oil film thickness usually depends on the reflection time of the ultrasonic wave in the oil film. The ultrasonic wave is sent through the transmitter and the reflected wave is received. The measured time difference can accurately calculate the thickness of the oil film, and the pressure measurement is achieved by analyzing the speed change of the ultrasonic wave when it propagates in the oil film. The pressure change in the oil film will affect the propagation speed of the ultrasonic wave, so the oil film pressure is inferred by establishing a corresponding model. This method can not only perform real-time monitoring under the operating state of the bearing, obtain the pressure distribution and oil film thickness of any point on the bearing surface, avoid affecting the Lamb wave propagation speed and the energy attenuation characteristics of each frequency component and interference with the bearing system, but the application of ultrasonic guided wave technology can significantly improve the accuracy and reliability of the measurement, and contribute to the health monitoring and maintenance of sliding bearings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic flow chart of the measurement of oil film thickness and pressure distribution on the surface of a sliding bearing bush based on ultrasonic guided waves according to the present invention.

[0042] Figure 2 This is a schematic diagram of the installation of a piezoelectric sensor for the clearance between the sliding bearing bush and the bearing seat of the present invention;

[0043] Figure 3 This is a diagram of the installation layout of the piezoelectric sensor array of the present invention embedded in the back of the sliding bearing bushing;

[0044] Figure 4 It is a schematic diagram comparing the actual signal and the reference signal received after the ultrasonic guided wave excitation bearing in Example 1 of the present invention.

[0045] In the figure, 1. Bearing, 2. Piezoelectric sensor, 3. Bearing seat. DETAILED DESCRIPTION

[0046] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] The present invention provides a method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves, such as Figure 1-3 As shown, please follow the steps below:

[0048] Step 1: embedding and arranging a piezoelectric array on the back of the bearing to be tested;

[0049] Step 1 is specifically as follows: a sensor array consisting of N pairs of piezoelectric sensors 2 arranged symmetrically on the left and right sides of the bearing 1 to be tested is embedded, and the excitation and data acquisition of the waveguide signal are controlled by the upper computer. The terminal board converts the signal into an electrical signal. The piezoelectric sensor 2 is used to excite and receive the waveguide signal, while the signal amplifier and charge amplifier are used to amplify the waveguide signal.

[0050] Step 2: Collect the guided wave signal under the set working condition of the oil film as the reference signal. After the reference signal is collected, the pressure and film thickness of the oil film are changed to collect the guided wave signals under different pressures and film thicknesses.

[0051] Step 2 is specifically as follows: select the operating range of the oil film pressure of 1-20 MPa and the oil film thickness of 10-200 μm, use a pair of sensors with horizontal paths in the piezoelectric sensor 2 array as the transmitting end and the receiving end, first, collect the guided wave signal within the operating range as the reference signal, and then change the oil film pressure and oil film thickness at intervals of 1 MPa each time to collect the guided wave signals under different pressures and different film thicknesses.

[0052] Step 3, obtaining the noise signal in the guided wave signal under different pressures and different film thicknesses in step 2, and performing trend noise fitting on the noise signal;

[0053] Step 3 is specifically as follows: a polynomial fitting function based on the least squares method is used to fit the noise interference. The order of the polynomial fitting is 4 to 7. The difference between the original signal and the electromagnetic interference is the actual signal.

[0054] Step 4: After trend noise fitting is completed, a segmented RMS index is established to extract the coupled RMS sensitive features of pressure and film thickness;

[0055] Step 4 is as follows: the waveguide signal is divided into multiple windows of fixed length in the time domain. The entire signal is divided into k segments for processing. The RMS value is calculated in each time window. The changes under different pressure and film thickness conditions are analyzed by comparing the RMS value of the entire signal with the local RMS value after segmentation. The RMS value in each time window is used as a feature to construct a feature matrix.

[0056] The formula for calculating the RMS value in each time window in step 4 is:

[0057] (1)

[0058] Where k is the number of windows into which the waveguide signal is divided in the time domain.

[0059] In step 5, the extracted guided wave data containing the coupled RMS sensitive features is imported into the convolutional neural network in deep learning to decouple the coupling features, and the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to image the bearing surface to complete the measurement.

[0060] Step 5 is specifically as follows: the feature matrix of the extracted coupling characteristic information about pressure and film thickness is used as the input feature of the neural network, and the convolutional neural network in deep learning is used for decoupling to obtain the relationship between the pressure distribution on the bearing surface and the change of the guided wave signal waveform, as well as the relationship between the oil film thickness on the bearing surface and the change of the guided wave signal waveform. Finally, the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to establish the oil film pressure distribution and oil film thickness distribution map on the bearing surface.

[0061] Step 5: The RAPID imaging algorithm based on the principle of probabilistic damage imaging is as follows:

[0062] S1, calculate the difference signal on the excitation-sensing path, and calculate the wavelet coefficient modulus curve of the difference signal to obtain the amplitude information time of the difference signal;

[0063] Difference signal The calculation method of the wavelet coefficient modulus curve is as follows:

[0064] (2)

[0065] (3)

[0066] Where, is the difference signal The modulus of the wavelet coefficients, a is the scale parameter of the wavelet, and its value is related to the center frequency of the Lamb wave. b is the translation parameter of the wavelet, is the mother wavelet function Conjugation after expansion and contraction;

[0067] The expression is as follows:

[0068] (4)

[0069] Where, a is the scale parameter of the wavelet, b is the translation parameter of the wavelet;

[0070] Mother wavelet function Morlet continuous complex wavelet is often selected as the wavelet function. Through Morlet continuous complex wavelet transform, the envelope of the difference signal can be obtained to obtain the coupling characteristic information of oil film pressure and oil film thickness at each time point;

[0071] S2, for the difference signal The wavelet coefficient modulus curve is normalized, and the specific formula is as follows:

[0072] (5)

[0073] Secondly, for any coordinate point in the bearing to be tested The surface pressure and film thickness from the actuator To the receiving exciter , the propagation time of Lamb wave in the path of excitation-any point on the bearing surface-receiving can be expressed as:

[0074] (6)

[0075] Where, is the time offset during Lamb wave excitation, is the group velocity of Lamb wave;

[0076] By using the sensor array and the probability superposition method, the bearing surface at any point can be calculated. Corresponding changes in pressure and oil film thickness , the specific calculation method is as follows:

[0077] (7)

[0078] Where N is the number of piezoelectric sensors. The pressure coordinates are determined by superimposing the pressure and oil film thickness probability maps established by multiple excitation sensing paths.

[0079] Finally, the calculated pressure and oil film thickness changes are As input parameters, they are input into the convolutional neural network in deep learning, and the coupling values ​​of the oil film pressure characteristics and the oil film thickness characteristics are decoupled. The relationship between the pressure field on the bearing surface to be measured and the change of the guided wave waveform, and the relationship between the film thickness distribution on the bearing surface and the guided wave waveform are obtained. The pressure distribution and oil film thickness at any point on the bearing surface are obtained to complete the measurement.

[0080] Example 1

[0081] This embodiment provides a method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves. Figure 1-4 As shown, the specific implementation steps are as follows:

[0082] Step 1: embedding and arranging a piezoelectric array on the back of the bearing to be tested;

[0083] Step 1 is specifically as follows: a sensor array consisting of 6 pairs of piezoelectric sensors 2 arranged symmetrically on the left and right sides of the bearing 1 to be tested is embedded, and the excitation and data acquisition of the waveguide signal are controlled by the upper computer. The terminal board converts the signal into an electrical signal. The piezoelectric sensor 2 is used to excite and receive the waveguide signal, while the signal amplifier and charge amplifier are used to amplify the waveguide signal.

[0084] Step 2: Collect the guided wave signal under the set working condition of the oil film as the reference signal. After the reference signal is collected, the pressure and film thickness of the oil film are changed to collect the guided wave signals under different pressures and film thicknesses.

[0085] Step 2 is as follows: Select the operating range of the oil film pressure of 20 MPa and the oil film thickness of 100 μm, use a pair of sensors with horizontal paths in the piezoelectric sensor 2 array as the transmitting end and the receiving end, first, collect the guided wave signal within the operating range as the reference signal, and then change the oil film pressure and oil film thickness by 1 MPa each time, and collect the guided wave signals under different pressures and different film thicknesses.

[0086] Step 3, obtaining the noise signal in the guided wave signal under different pressures and different film thicknesses in step 2, and performing trend noise fitting on the noise signal;

[0087] Step 3 is specifically as follows: a polynomial fitting function based on the least squares method is used to fit the noise interference. The order of the polynomial fitting is 4 to 7. The difference between the original signal and the electromagnetic interference is the actual signal.

[0088] Step 4: After trend noise fitting is completed, a segmented RMS index is established to extract the coupled RMS sensitive features of pressure and film thickness;

[0089] Step 4 is as follows: the waveguide signal is divided into multiple windows of fixed length in the time domain. The entire signal is divided into k segments for processing. The RMS value is calculated in each time window. The changes under different pressure and film thickness conditions are analyzed by comparing the RMS value of the entire signal with the local RMS value after segmentation. The RMS value in each time window is used as a feature to construct a feature matrix.

[0090] The formula for calculating the RMS value in each time window in step 4 is:

[0091] (1)

[0092] Where k is the number of windows into which the waveguide signal is divided in the time domain.

[0093] In step 5, the extracted guided wave data containing the coupled RMS sensitive features is imported into the convolutional neural network in deep learning to decouple the coupling features, and the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to image the bearing surface to complete the measurement.

[0094] Step 5 is specifically as follows: the feature matrix of the extracted coupling characteristic information about pressure and film thickness is used as the input feature of the neural network, and the convolutional neural network in deep learning is used for decoupling to obtain the relationship between the pressure distribution on the bearing surface and the change of the guided wave signal waveform, as well as the relationship between the oil film thickness on the bearing surface and the change of the guided wave signal waveform. Finally, the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to establish the oil film pressure distribution and oil film thickness distribution map on the bearing surface.

[0095] Step 5: The RAPID imaging algorithm based on the principle of probabilistic damage imaging is as follows:

[0096] S1, calculate the difference signal on the excitation-sensing path, and calculate the wavelet coefficient modulus curve of the difference signal to obtain the amplitude information time of the difference signal;

[0097] Difference signal The calculation method of the wavelet coefficient modulus curve is as follows:

[0098] (2)

[0099] (3)

[0100] Where, is the difference signal The modulus of the wavelet coefficients, a is the scale parameter of the wavelet, and its value is related to the center frequency of the Lamb wave. b is the translation parameter of the wavelet, is the mother wavelet function Conjugation after expansion and contraction;

[0101] The expression is as follows:

[0102] (4)

[0103] Where, a is the scale parameter of the wavelet, b is the translation parameter of the wavelet;

[0104] Mother wavelet function Morlet continuous complex wavelet is often selected as the wavelet function. Through Morlet continuous complex wavelet transform, the envelope of the difference signal can be obtained to obtain the coupling characteristic information of oil film pressure and oil film thickness at each time point;

[0105] S2, for the difference signal The wavelet coefficient modulus curve is normalized, and the specific formula is as follows:

[0106] (5)

[0107] Secondly, for any coordinate point in the bearing to be tested The surface pressure and film thickness from the actuator To the receiving exciter , the propagation time of Lamb wave in the path of excitation-any point on the bearing surface-receiving can be expressed as:

[0108] (6)

[0109] Where, is the time offset during Lamb wave excitation, is the group velocity of Lamb wave;

[0110] By using the sensor array and the probability superposition method, the bearing surface at any point can be calculated. Corresponding changes in pressure and oil film thickness , the specific calculation method is as follows:

[0111] (7)

[0112] Where N is the number of piezoelectric sensors. The pressure coordinates are determined by superimposing the pressure and oil film thickness probability maps established by multiple excitation sensing paths.

[0113] Finally, the calculated pressure and oil film thickness changes are As input parameters, they are input into the convolutional neural network in deep learning, and the coupling values ​​of the oil film pressure characteristics and the oil film thickness characteristics are decoupled. The relationship between the pressure field on the bearing surface to be measured and the change of the guided wave waveform, and the relationship between the film thickness distribution on the bearing surface and the guided wave waveform are obtained. The pressure distribution and oil film thickness at any point on the bearing surface are obtained to complete the measurement.

[0114] Example 2

[0115] This embodiment provides a method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves. Figure 1-4 As shown, please follow the steps below:

[0116] Step 1: embedding and arranging a piezoelectric array on the back of the bearing to be tested;

[0117] Step 2: Collect the guided wave signal under the set working condition of the oil film as the reference signal. After the reference signal is collected, the pressure and film thickness of the oil film are changed to collect the guided wave signals under different pressures and film thicknesses.

[0118] Step 3, obtaining the noise signal in the guided wave signal under different pressures and different film thicknesses in step 2, and performing trend noise fitting on the noise signal;

[0119] Step 4: After trend noise fitting is completed, a segmented RMS index is established to extract the coupled RMS sensitive features of pressure and film thickness;

[0120] In step 5, the extracted guided wave data containing the coupled RMS sensitive features is imported into the convolutional neural network in deep learning to decouple the coupling features, and the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to image the bearing surface to complete the measurement.

[0121] Example 3

[0122] On the basis of Example 2, Figure 1-4 As shown, step 1 is specifically as follows: a sensor array consisting of N pairs of piezoelectric sensors arranged symmetrically on the left and right sides of the bearing to be tested is embedded in the back of the bearing to be tested, and the excitation of the guided wave signal and data acquisition are controlled by the host computer.

[0123] Step 2 is as follows: Select the operating range of the oil film pressure of 1-20 MPa and the oil film thickness of 10-200 μm, use a pair of sensors with horizontal paths in the piezoelectric sensor array as the transmitting end and the receiving end, first, collect the guided wave signal within the operating range as the reference signal, and then change the oil film pressure and oil film thickness by 1 MPa each time, and collect the guided wave signals under different pressures and different film thicknesses.

[0124] Step 3 is specifically as follows: a polynomial fitting function based on the least squares method is used to fit the noise interference. The order of the polynomial fitting is 4 to 7. The difference between the original signal and the electromagnetic interference is the actual signal.

[0125] Step 4 is as follows: the waveguide signal is divided into multiple windows of fixed length in the time domain. The entire signal is divided into k segments for processing. The RMS value is calculated in each time window. The changes under different pressure and film thickness conditions are analyzed by comparing the RMS value of the entire signal with the local RMS value after segmentation. The RMS value in each time window is used as a feature to construct a feature matrix.

[0126] The formula for calculating the RMS value in each time window in step 4 is:

[0127] (1);

[0128] Where k is the number of windows into which the waveguide signal is divided in the time domain.

[0129] Step 5 is specifically as follows: the feature matrix of the extracted coupling characteristic information about pressure and film thickness is used as the input feature of the neural network, and the convolutional neural network in deep learning is used for decoupling to obtain the relationship between the pressure distribution on the bearing surface and the change of the guided wave signal waveform, as well as the relationship between the oil film thickness on the bearing surface and the change of the guided wave signal waveform. Finally, the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to establish the oil film pressure distribution and oil film thickness distribution map on the bearing surface.

[0130] Step 5: The RAPID imaging algorithm based on the principle of probabilistic damage imaging is as follows:

[0131] S1, calculate the difference signal on the excitation-sensing path, and calculate the wavelet coefficient modulus curve of the difference signal to obtain the amplitude information time of the difference signal;

[0132] Difference signal The calculation method of the wavelet coefficient modulus curve is as follows:

[0133] (2)

[0134] (3)

[0135] Where, is the difference signal The modulus of the wavelet coefficients, a is the scale parameter of the wavelet, and its value is related to the center frequency of the Lamb wave. b is the translation parameter of the wavelet, is the mother wavelet function Conjugation after expansion and contraction;

[0136] The expression is as follows:

[0137] (4)

[0138] Where, a is the scale parameter of the wavelet, b is the translation parameter of the wavelet;

[0139] Mother wavelet function Morlet continuous complex wavelet is often selected as the wavelet function. Through Morlet continuous complex wavelet transform, the envelope of the difference signal can be obtained to obtain the coupling characteristic information of oil film pressure and oil film thickness at each time point;

[0140] S2, for the difference signal The wavelet coefficient modulus curve is normalized, and the specific formula is as follows:

[0141] (5)

[0142] Secondly, for any coordinate point in the bearing to be tested The surface pressure and film thickness from the actuator To the receiving exciter , the propagation time of Lamb wave in the path of excitation-any point on the bearing surface-receiving can be expressed as:

[0143] (6)

[0144] Where, is the time offset during Lamb wave excitation, is the group velocity of Lamb wave;

[0145] By using the sensor array and the probability superposition method, the bearing surface at any point can be calculated. Corresponding changes in pressure and oil film thickness , the specific calculation method is as follows:

[0146] (7)

[0147] Where N is the number of piezoelectric sensors. The pressure coordinates are determined by superimposing the pressure and oil film thickness probability maps established by multiple excitation sensing paths.

[0148] Finally, the calculated pressure and oil film thickness changes are As input parameters, they are input into the convolutional neural network in deep learning, and the coupling values ​​of the oil film pressure characteristics and the oil film thickness characteristics are decoupled. The relationship between the pressure field on the bearing surface to be measured and the change of the guided wave waveform, and the relationship between the film thickness distribution on the bearing surface and the guided wave waveform are obtained. The pressure distribution and oil film thickness at any point on the bearing surface are obtained to complete the measurement.

Claims

1. A method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves, characterized in that: Please follow the steps below to implement it: Step 1: embedding and arranging a piezoelectric array on the back of the bearing to be tested; Step 2: Collect the guided wave signal under the set working condition of the oil film as the reference signal. After the reference signal is collected, the pressure and film thickness of the oil film are changed to collect the guided wave signals under different pressures and film thicknesses. Step 3, obtaining the noise signal in the guided wave signal under different pressures and different film thicknesses in step 2, and performing trend noise fitting on the noise signal; Step 4: After trend noise fitting is completed, a segmented RMS index is established to extract the coupled RMS sensitive features of pressure and film thickness; In step 5, the extracted guided wave data containing the coupled RMS sensitive features is imported into the convolutional neural network in deep learning to decouple the coupling features, and the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to image the bearing surface to complete the measurement.

2. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 1, characterized in that: The step 1 specifically comprises: embedding a sensor array consisting of N pairs of piezoelectric sensors (2) arranged symmetrically on the left and right sides of the bearing bush (1) to be tested, and controlling the excitation of the guided wave signal and data acquisition through a host computer.

3. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 1, characterized in that: The step 2 is specifically as follows: selecting a working condition range in which the oil film pressure is 1-20 MPa and the oil film thickness is 10-200 μm, using a pair of sensors with horizontal paths in the piezoelectric sensor (2) array as the transmitting end and the receiving end, firstly collecting the guided wave signal within the working condition range as the reference signal, and secondly changing the oil film pressure and the oil film thickness by 1 MPa each time, collecting the guided wave signals under different pressures and different film thicknesses.

4. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 1, characterized in that: The step 3 is specifically as follows: using a polynomial fitting function based on the least squares method to fit the noise interference, the order of the polynomial fitting is 4 to 7, and the difference between the original signal and the electromagnetic interference is the actual signal.

5. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 1, characterized in that: Specifically, step 4 includes dividing the waveguide signal into multiple windows of fixed length in the time domain, dividing the entire signal into k segments for processing, calculating the RMS value in each time window, analyzing the changes under different pressure and film thickness conditions by comparing the RMS value of the entire signal with the local RMS value after segmentation, and using the RMS value in each time window as a feature to construct a feature matrix.

6. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 5, characterized in that: The formula for calculating the RMS value in each time window in step 4 is: （1）； Where k is the number of windows into which the waveguide signal is divided in the time domain.

7. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 1, characterized in that: The specific steps of step 5 are as follows: using the feature matrix of the coupling characteristic information about pressure and film thickness extracted as the input feature of the neural network, decoupling is performed using the convolutional neural network in deep learning, and the relationship between the pressure distribution on the bearing surface and the change of the guided wave signal waveform, as well as the relationship between the oil film thickness on the bearing surface and the change of the guided wave signal waveform are obtained. Finally, the RAPID imaging algorithm based on the probabilistic damage imaging principle is used to establish the oil film pressure distribution and oil film thickness distribution map on the bearing surface.

8. The method for measuring the oil film thickness and pressure distribution on the bearing surface based on ultrasonic guided waves according to claim 7, characterized in that: The RAPID imaging algorithm based on the probabilistic damage imaging principle described in step 5 is specifically as follows: S1, calculates the difference signal on the excitation-sensing path , and calculate the difference signal The wavelet coefficient modulus curve is used to obtain the amplitude information time of the difference signal; The difference signal The calculation method of the wavelet coefficient modulus curve is as follows: （2） （3） Where, is the difference signal The modulus of the wavelet coefficients, a is the scale parameter of the wavelet, and its value is related to the center frequency of the Lamb wave. b is the translation parameter of the wavelet, is the mother wavelet function Conjugation after expansion and contraction; described The expression is as follows: （4） Where, a is the scale parameter of the wavelet, b is the translation parameter of the wavelet; Mother wavelet function Morlet continuous complex wavelet is often selected as the wavelet function. Through Morlet continuous complex wavelet transform, the envelope of the difference signal can be obtained to obtain the coupling characteristic information of oil film pressure and oil film thickness at each time point; S2, the difference signal The wavelet coefficient modulus curve is normalized, and the specific formula is as follows: （5） Secondly, for any coordinate point in the bearing to be tested The surface pressure and film thickness from the actuator To the receiving exciter , the propagation time of Lamb wave in the path of excitation-any point on the bearing surface-receiving can be expressed as: （6） Where, is the time offset during Lamb wave excitation, is the group velocity of Lamb wave; By using the sensor array and the probability superposition method, the bearing surface at any point can be calculated. Corresponding changes in pressure and oil film thickness , the specific calculation method is as follows: （7） Where N is the number of piezoelectric sensors. The pressure coordinates are determined by superimposing the pressure and oil film thickness probability maps established by multiple excitation sensing paths. Finally, the calculated pressure and oil film thickness changes are As input parameters, they are input into the convolutional neural network in deep learning, and the coupling values ​​of the oil film pressure characteristics and the oil film thickness characteristics are decoupled. The relationship between the pressure field on the bearing surface to be measured and the change of the guided wave waveform, and the relationship between the film thickness distribution on the bearing surface and the guided wave waveform are obtained. The pressure distribution and oil film thickness at any point on the bearing surface are obtained to complete the measurement.

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