Transmission component vibration monitoring position optimization method and system and test bench

Abstract

The application discloses a transmission component vibration monitoring position optimization method and system and a test bench. The method comprises the following steps: firstly, a dynamic model under normal transmission system and manufacturing error conditions is established, and positions with greater acceleration response are obtained as preliminary vibration monitoring points in combination with different gears and main running speeds; vibration testing is performed on the preliminary monitoring points obtained through simulation, time domain index parameters under the main running speeds of each gear are extracted, and vibration sensitive positions are obtained after normalization; then, according to the characteristic frequencies of gears, bearings and other gears under the main running speeds, the energy in the 1 / 3 bandwidth of the characteristic frequency band is calculated, and the vibration monitoring points are optimized through frequency domain energy entropy; finally, the position optimization can be performed by taking the time domain sensitivity of each position as the horizontal coordinate and the frequency domain energy entropy as the vertical coordinate, so that the optimal vibration monitoring position can be positioned. The application further discloses a system and a vibration monitoring test bench, and lays a theoretical and practical foundation for abnormal sound identification.

Description

Technical Field

[0001] This invention belongs to the field of condition monitoring of transmission systems in engineering machinery, and specifically relates to a method, system and test bench for optimizing the position of vibration monitoring of transmission components. Background Technology

[0002] Transmission components are widely used in automobiles, construction machinery, and other fields. They typically consist of pumps, hydraulic torque converters, gears, bearings, shafts, etc. However, due to deficiencies in their design, manufacturing, and assembly, defective products often reach the customer, resulting in economic losses from rework, returns, and manual troubleshooting. Therefore, it is necessary to monitor transmission components before they reach the customer and screen out faulty samples through a running-in test bench.

[0003] Vibration testing and analysis is one of the most commonly used methods in mechanical fault diagnosis. Vibration signals can identify the characteristic frequencies of faulty components, thereby quickly locating the faulty component and the type of fault. To ensure the timeliness and accuracy of fault diagnosis, the selection of the location and number of vibration monitoring points is crucial. A small number facilitates data processing but may result in the loss of useful information, while a large number leads to a decrease in the timeliness of online real-time fault analysis, and a large amount of redundant information will also cause analysis chaos. Currently, the selection of vibration monitoring points on transmission component running-in test benches is still based on experience, mostly choosing locations near bearings with high stiffness and short transmission paths. However, in reality, for engineering machinery gearboxes, there are often more than twenty gear pairs and usually more than a dozen bearing locations. Arranging so many monitoring points is very detrimental to subsequent analysis work. Therefore, there is an urgent need to develop an optimized vibration monitoring scheme suitable for transmission component running-in test benches.

[0004] The disadvantages of existing technologies are as follows:

[0005] (1) At present, most vibration measurement points for transmission components of engineering machinery are selected based on experience at the bearing housing, resulting in a large number of redundant and complicated information, which is not conducive to the rapid and accurate location of faults.

[0006] (2) Some transmission component failure types based on virtual simulation analysis are mostly extremely harsh working conditions such as broken teeth. These working conditions will not appear on the offline running-in test bench. In fact, more than 80% of the failure types of newly assembled transmission components used for running-in tests are caused by gear manufacturing errors. These manufacturing errors cannot be realized in simulation. Therefore, the previous simulation measurement point optimization method based on broken teeth is not applicable to the actual offline testing bench.

[0007] (3) Based on the virtual simulation analysis, the reverse verification was not carried out by joint testing, and the simulation mostly focused only on the ranking of the importance of the time domain characteristic parameters. The optimal position of the measurement point was determined by analyzing only the simulation time domain. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, the present invention provides a method, system and test bench for optimizing the position of vibration monitoring of transmission components, in order to solve at least one of the above-mentioned technical problems.

[0009] According to one aspect of the present invention, a method for optimizing the vibration monitoring position of a transmission component is provided, comprising:

[0010] S1, Establish a dynamic simulation model of the transmission components;

[0011] S2, Based on the dynamic simulation model of the transmission component, the transmission component under normal and manufacturing error conditions is analyzed to obtain the vibration response results under the two conditions, and based on the vibration response results, multiple initial positions are determined as preliminary vibration monitoring points;

[0012] S3, Vibration test is performed based on multiple initially selected positions to obtain vibration acceleration signals at each initially selected position;

[0013] S4, perform time-domain sensitivity optimization based on the vibration acceleration signal to obtain the time-domain sensitivity of each initially selected position;

[0014] S5, Based on the vibration acceleration signal, perform frequency domain energy entropy optimization to obtain the frequency domain energy entropy of each initially selected position;

[0015] S6. Based on the time-domain sensitivity and frequency-domain energy entropy of each initially selected location, determine the optimal vibration monitoring location.

[0016] The above technical solution combines simulation analysis and vibration testing for offline detection. Based on the simulation analysis, the initial test location is obtained, and then the simulation results are verified by offline detection, thus realizing closed-loop control. In addition, the technical solution combines time-domain sensitivity optimization and frequency-domain energy entropy optimization to optimize the vibration monitoring location, ensuring the accuracy of vibration sensor location optimization.

[0017] Furthermore, the method provided by the above technical solution has the advantages of unified evaluation standards, high efficiency, and easy traceability of problems.

[0018] As a further technical solution, S2 also includes: obtaining the manufacturing error distribution of the transmission component; and selecting a target manufacturing error as the excitation source for the dynamic simulation model of the transmission component based on the manufacturing error distribution.

[0019] As a further technical solution, S4 also includes:

[0020] The vibration acceleration signals in three directions at each initially selected position are acquired and then denoised.

[0021] Extract the maximum, minimum, peak, peak-to-peak, mean, variance, absolute mean, root mean amplitude, kurtosis, skewness, waveform index, margin index, peak index, impulse index, kurtosis index, and root mean square value in the time domain at the main operating speed of the target gear to obtain 16 index parameters for each channel, and calculate the response results of the characteristic parameters.

[0022] Based on the response results of the feature parameters, the response matrix of the feature parameters is obtained, and the sensitivity matrix of each channel is obtained by normalization calculation.

[0023] Based on the sensitivity matrix, the average values ​​of 16 indicators for each channel are obtained and the preferred channel is determined. At the same time, the average sensitivity values ​​of the three directions of each initially selected position are classified according to X, Y and Z to obtain the preferred direction.

[0024] As a further technical solution, the determination of the target gear includes: obtaining the gear transmission route of each gear; analyzing the gear pairs included in each gear transmission route, and when two or more gear transmission routes cover all gear pairs of the transmission components, determining the gear corresponding to these gear transmission routes as the target gear.

[0025] As a further technical solution, each of the initially selected positions corresponds to a channel in one direction.

[0026] As a further technical solution, after determining the initial selection location, time-domain index parameters are extracted from samples of multiple transmission components.

[0027] As a further technical solution, S5 also includes:

[0028] The vibration acceleration signals in three directions at each initially selected position are acquired and then denoised.

[0029] Perform a Fourier transform on the noise-reduced vibration acceleration signal;

[0030] The characteristic frequency of the transmission component under study is calculated based on its structure. The upper and lower cutoff frequencies of 1 / 3 octave band are used as the bandwidth. The bandwidth containing the characteristic frequency is filtered out and segmented to obtain the energy frequency band matrix of each channel.

[0031] The probability density matrix is ​​calculated based on the energy frequency band matrix.

[0032] The energy information entropy of each channel is obtained based on the probability density matrix, and the energy information entropy matrix is ​​calculated using the energy information entropy.

[0033] The three-dimensional energy information entropy sums are sorted from low to high to obtain the preferred channels. At the same time, the three-dimensional energy information entropy sums of each initially selected position are classified according to X, Y and Z to obtain the preferred directions.

[0034] As a further technical solution, S6 also includes: using the time domain sensitivity of each initially selected location as the horizontal axis and the frequency domain energy entropy as the vertical axis to perform location optimization, thereby confirming the preferred location and preferred channel for vibration monitoring.

[0035] According to one aspect of the present invention, a vibration monitoring and position optimization system for a transmission component is provided, comprising:

[0036] The manufacturing error simulation analysis module is used to establish a dynamic simulation model of the transmission components to obtain the vibration response of the transmission components under two conditions: normal operation and manufacturing error failure. The initial position is determined based on the vibration response acceleration results.

[0037] The data acquisition module is used to place vibration acceleration sensors on the transmission component sample based on the simulation results and measure the vibration acceleration signals at each initially selected position of the transmission component.

[0038] The time-domain sensitivity optimization module is used to optimize the time-domain sensitivity based on the vibration acceleration signal to obtain the time-domain sensitivity of each initially selected position.

[0039] The frequency domain energy entropy optimization module is used to optimize the frequency domain energy entropy based on the vibration acceleration signal to obtain the frequency domain energy entropy at each initially selected position.

[0040] The vibration location optimization judgment module is used to confirm the final preferred location and direction for vibration monitoring based on the results of the time-domain sensitivity optimization module and the frequency-domain energy entropy optimization module.

[0041] According to one aspect of the present invention, a test bench for optimizing the vibration monitoring position of a transmission component is provided, for implementing the method for optimizing the vibration monitoring position of the transmission component. The test bench includes an input system, a base system, a bracket system, a transmission component, an output system, and a sensor support system. The input system provides input power to the transmission component. The base system provides an installation foundation and hydraulic clamping function for the input system, bracket system, support system, and output system. The bracket system transports the transmission component to the desired test position and lifts and transports the tested housing out of the test bench after the test is completed. The sensor support system enables automatic movement and precise positioning of the sensor.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] 1. This invention provides a method that combines simulation and vibration testing to form a closed-loop process, which makes up for the shortcomings of relying solely on simulation and resulting in incomplete results. It changes the past situation of mainly relying on experience to determine the vibration monitoring location and solves the problems of complex, redundant, inefficient and inaccurate measurement point information caused by too many or incomplete sensor deployments.

[0044] 2. The method of this invention combines time-domain sensitivity optimization and frequency-domain energy entropy optimization to optimize vibration monitoring location, which solves the problems of high-sensitivity location screening of time-domain feature parameters and accurate identification of faulty machine characteristic frequencies, thus ensuring the accuracy of vibration sensor location optimization.

[0045] 3. This invention determines the initial position based on multibody dynamics simulation, which solves the problem of difficult vibration sensor positioning and realizes the preliminary accurate identification of vibration measurement points.

[0046] 4. This invention provides a system that lays the foundation for subsequent intelligent diagnosis of abnormal noises.

[0047] 5. The present invention provides a test bench that facilitates rapid and accurate location of faults in transmission components. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the gearbox transmission structure according to an embodiment of the present invention.

[0049] Figure 2 This is a flowchart of a method according to an embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of the test bench structure according to an embodiment of the present invention.

[0051] Figures 4a-4b This is a schematic diagram of the time-domain sensitivity analysis results of the gearbox according to an embodiment of the present invention.

[0052] Figure 5 This is a schematic diagram of the energy probability density results of each frequency band of the gearbox according to an embodiment of the present invention.

[0053] Figures 6a-6b This is a schematic diagram of the frequency domain energy entropy result of the gearbox according to an embodiment of the present invention.

[0054] Figure 7 This is a schematic diagram illustrating the optimized sequence of vibration monitoring locations according to an embodiment of the present invention.

[0055] Figure 8 This is a schematic diagram showing the optimized position of vibration monitoring for transmission components according to an embodiment of the present invention.

[0056] Figure 9 This is a system flowchart according to an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of various embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0058] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a connection that allows for communication; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0060] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0061] This invention proposes a method for optimizing the vibration monitoring location of transmission components. The method first establishes a dynamic model of the transmission system under normal and manufacturing error conditions, and uses different gears and main operating speeds to identify locations with large acceleration responses as initial vibration monitoring points. Vibration tests are then conducted on the initial monitoring points obtained from the simulation, and time-domain index parameters at the main operating speeds of each gear are extracted and normalized to obtain the vibration-sensitive locations. Next, based on the characteristic frequencies of gears, bearings, etc., at the main operating speeds of each gear, the energy within one-third of the characteristic frequency band is calculated, and the vibration monitoring points are optimized using frequency domain energy entropy. Finally, by optimizing the location using the time-domain sensitivity of each position as the x-axis and the frequency domain energy entropy as the y-axis, the optimal vibration monitoring location can be determined.

[0062] This invention is based on Figure 1 Taking the gearbox shown as an example, the following technical solution is adopted to achieve this, such as... Figure 2 As shown:

[0063] S1: Establish a dynamic simulation model for the transmission components:

[0064] S11: Create a 3D model of the gearbox housing in CAD software and export it as a .xt file;

[0065] S12: Import the 3D gearbox housing into Ansys and mesh it, set the condensed nodes of the bearings, and condense the stiffness and mass matrices on these nodes. Finally, solve the model to obtain its cdb file.

[0066] S13: Establish a dynamic analysis model of the gearbox in Masta. Import the gearbox housing cdb file from step S12 into the dynamic model and couple it at the condensed nodes to generate the condensed mass matrix and stiffness matrix. Establish the final bearing-gear-carrier gearbox dynamic model.

[0067] S2: Statistically analyze the distribution of common manufacturing errors in the gearbox. Input the pitch error and circular runout error under normal operating conditions as excitation sources into the dynamic model and run advanced system deformation analysis; input the pitch error and circular runout error under fault conditions as excitation sources into the dynamic model and run advanced system deformation analysis. Obtain the vibration response of the gearbox housing under these two conditions. Based on the vibration response acceleration results, 11 measurement points with large responses and easy installation were initially selected.

[0068] After determining the initial vibration monitoring points, the method of this invention can utilize... Figure 3 The test bench shown is used for off-line testing. The test bench consists of 1 input system, 2 base system, 3 bracket system, 4 gearbox, 5 output system, and 6 sensor support system.

[0069] The input system mainly consists of a drive motor, a motor bracket, and a connecting flange, and its main functions are to provide input power to the gearbox and to clamp and move the motor.

[0070] The base system consists of a cross-shaped cast platform, vibration isolators, and a hydraulic system, providing a mounting foundation and hydraulic clamping function for the input, bracket, support, and output systems. The base is machined with cross-shaped grooves for easy movement and also includes power cylinders and hydraulic components for clamping the connected systems. Vibration isolators are installed at the bottom of the base to effectively isolate external interference.

[0071] The carriage system primarily transports the gearboxes from the production line to suitable testing positions, enabling rapid and accurate positioning of the tested item. Once the gearbox running and testing are completed, the carriage system can lift the tested gearbox to a suitable workstation for rapid transport out of the test bench.

[0072] The sensor support system consists of sensors, cylinders, etc. It uses a probe-type accelerometer with an embedded elastic element, which can realize the automatic movement and precise positioning of the sensor, eliminating the trouble of manual clamping multiple times.

[0073] The output system mainly includes the output motor and the load torque, etc.

[0074] S3: Optimize the temporal sensitivity of the selected 11 locations through testing.

[0075] S31: Initially, acceleration signals in three directions were collected from 11 initially selected locations. For each machine (referring to the gearbox sample, there are usually multiple ones), 16 time-domain indicators were extracted: maximum value, minimum value, peak value, peak-to-peak value, mean value, variance, absolute mean value, root mean square amplitude, kurtosis, skewness, waveform indicator, margin indicator, peak value indicator, impulse indicator, kurtosis indicator, and root mean square value, resulting in 16 indicator parameters for 33 channels.

[0076] The transmission routes of each gear in the gearbox are shown in Table 1. F1-F4 represent the four forward gears, and R1-R4 represent the four reverse gears. The symbols under the gear transmission routes all represent gear pairs. For example, Z4 / Z2*Z9 / Z4*Z10 / Z12 represents the transmission route from gear pair Z4 / Z2 to gear pair Z9 / Z4 and then to gear pair Z10 / Z12. * indicates the transmission direction of the gear pair.

[0077] Table 1. Transmission routes of each gear in the gearbox

[0078] F1 Z4 / Z2*Z9 / Z4*Z10 / Z12 F2 Z4 / Z2*Z8 / Z3*Z12 / Z10 F3 Z4 / Z2*Z6 / Z4*Z12 / Z10 F4 Z4 / Z2*Z5 / Z3*Z11 / Z7 R1 Z3 / Z1*Z9 / Z4*Z12 / Z10 R2 Z3 / Z1*Z8 / Z3*Z12 / Z10 R3 Z3 / Z1*Z6 / Z4*Z11 / Z7 R4 Z3 / Z1*Z5 / Z3*Z11 / Z7

[0079] Based on the transmission routes of each gear, it is concluded that only F1, F4, R2, and R3 need to be collected to include all gear information. Therefore, after calculating the parameters of each index under the main operating speed of the 33 channels and 4 gears, the response result is obtained through the following formula:

[0080] x im (F1)=F1(x im (v1)+x im (v2)+...+x im (v n ))

[0081] x im (F4)=F4(x im (v1)+x im (v2)+...+x im (v n ))

[0082] x im (R2)=R2(x im (v1)+x im (v2)+...+x im (vn))

[0083] x im (R3)=R3(x im (v1)+x im (v2)+...+x im (v n ))

[0084] x im =1 / 4((x) im (F1)+x im (F4)+x im (R2)+x im (R3))

[0085] Where i represents the channel, m represents the indicator, and Vn represents the main operating speed.

[0086] S32: Obtain the response matrix of characteristic parameters from the response results:

[0087]

[0088] If we define the maximum value of a matrix row as Xim(max) and form a diagonal matrix, then the inverse of the diagonal matrix is:

[0089]

[0090] S33: Multiply the inverse of the diagonal matrix from step S32 with the response matrix to obtain the sensitivity matrix for each channel:

[0091]

[0092] S34: Based on the definition of sensitivity, further calculate the average of 16 indicators for each channel:

[0093]

[0094] The results are as follows Figure 4a As shown, the maximum values ​​of the indicators at each position are sorted from highest to lowest, and the positions with the largest values ​​of 7, 8, 5 and 6 are initially identified as the preferred positions.

[0095] S35: Classify the mean sensitivity values ​​in three directions at the 11 locations according to X, Y, and Z.

[0096]

[0097]

[0098]

[0099] The results are as follows Figure 4b As shown, Y and Z with larger values ​​are selected as the preferred directions.

[0100] S4: Frequency Domain Energy Entropy Optimization

[0101] S41: Initially, acceleration signals in three directions were collected from 11 initially selected locations of the faulty machine. TSA noise reduction was performed on the vibration signals, and then Fourier transform was applied to the processed signals. The characteristic frequency was calculated based on the structure of the gearbox under study.

[0102] The gears are fixed-axis gears.

[0103] Formula for calculating the meshing frequency of fixed-axis gears:

[0104] GMF = Z × RPM (1)

[0105] Where: GMF is the meshing frequency of the fixed-axis gear, and the unit is Hertz (Hz);

[0106] Z is the number of teeth on the gear, which is dimensionless;

[0107] RPM is the rotational speed of the shaft containing the gear, measured in revolutions per minute (r / min).

[0108] A rolling bearing consists of four main components: an outer ring, an inner ring, rolling elements, and a cage. Each of the four components has its own unique frequency.

[0109] Formula for calculating the frequency of bearing rolling element failure:

[0110] BSF=1 / 2×RPM×Pd / Bd×(1 – (Bd / Pd×cos ψ)2) (2)

[0111] Where: BSF is the rolling element failure frequency, and the unit is Hertz (Hz);

[0112] RPM is the rotational speed of the shaft containing the bearing, measured in revolutions per minute (r / min).

[0113] N is the number of rolling elements in the bearing;

[0114] Pd is the bearing pitch diameter, in millimeters (mm);

[0115] Bd is the diameter of the rolling element of the bearing, in millimeters (mm);

[0116] ψ is the contact angle of the rolling element, in degrees (°).

[0117] Formula for calculating the frequency of damage to the bearing's inner raceway:

[0118] BPFI=1 / 2×RPM×N×(1–Bd / Pd×cosψ) (3)

[0119] Wherein: BPFI is the inner track damage frequency, in Hertz (Hz);

[0120] RPM is the rotational speed of the shaft containing the bearing, measured in revolutions per minute (r / min).

[0121] N is the number of rolling elements in the bearing;

[0122] Pd is the bearing pitch diameter, in millimeters (mm);

[0123] Bd is the diameter of the rolling element of the bearing, in millimeters (mm);

[0124] ψ is the contact angle of the rolling element, in degrees (°).

[0125] Formula for calculating the frequency of bearing outer track damage:

[0126] BPFO=1 / 2×RPM×N×(1+Bd / Pd×cos ψ) (4)

[0127] Where: BPFO is the outer orbit damage frequency, and the unit is Hertz (Hz);

[0128] RPM is the rotational speed of the shaft containing the bearing, measured in revolutions per minute (r / min).

[0129] N is the number of rolling elements in the bearing;

[0130] Pd is the bearing pitch diameter, in millimeters (mm);

[0131] Bd is the diameter of the rolling element of the bearing, in millimeters (mm);

[0132] ψ is the contact angle of the rolling element, in degrees (°).

[0133] Formula for calculating bearing cage failure frequency:

[0134] FTF=1 / 2×RPM×(1×Bd / Pd×cosψ) (5)

[0135] Where: FTF is the cage failure frequency, and the unit is Hertz (Hz);

[0136] RPM is the rotational speed of the shaft containing the bearing, measured in revolutions per minute (r / min).

[0137] N is the number of rolling elements in the bearing;

[0138] Pd is the bearing pitch diameter, in millimeters (mm);

[0139] Bd is the diameter of the rolling element of the bearing, in millimeters (mm);

[0140] ψ is the contact angle of the rolling element, in degrees (°).

[0141] Based on the calculated characteristic frequency, the bandwidth containing the characteristic frequency is selected and divided into j segments using the upper and lower cutoff frequencies of 1 / 3 octave as the bandwidth. The energy of each segment is represented by Qij.

[0142]

[0143] Where i represents the channel and j represents the frequency band.

[0144] S42: Sum the column energies of the matrix from step S41 to obtain the total energy of the channel. Then, the energy probability density of each channel at each frequency band:

[0145]

[0146] The probability density matrix is ​​obtained as follows:

[0147]

[0148] The results are as follows Figure 5 As shown.

[0149] S43: The energy information entropy of each channel is:

[0150]

[0151] The energy information entropy matrix is ​​obtained from the energy information entropy of each channel:

[0152] E = (E1 E2 … E i )

[0153] The results are as follows Figure 6a As shown.

[0154] S44: The sum of the three-dimensional energy information entropy of each channel is:

[0155] E i (sum) = E i (X)E i (Y)…E i (Z),

[0156] Three-dimensional energy information entropy and matrix for each channel:

[0157] E(sum)=(E1(sum) E2(sum) … E i (sum))

[0158] The three-dimensional energy information entropy sums of each channel are sorted from low to high, with the smaller values ​​of 7, 8, 5, and 6 being the preferred positions.

[0159] S45: The minimum value of the three-dimensional energy information entropy of channel i is:

[0160] E i (min)=Min(E i (X)E i (Y), E i (Z)),

[0161] Minimum value matrix of three-dimensional energy information entropy for each channel:

[0162] E(min)=(E1(min) E2(min) … E i (min))

[0163] The minimum values ​​of the three-dimensional energy information entropy of each channel are sorted from low to high. Combined with step S44, the smaller values ​​of 7, 8, 5, and 6 are selected as the preferred positions.

[0164] S46: The energy information entropy of the 11 positions in three directions is classified according to X, Y, and Z.

[0165]

[0166]

[0167]

[0168] The results are as follows Figure 6b As shown, radial Y and axial Z with smaller values ​​are selected as the preferred channels.

[0169] S5: Combining steps S3 and S4, perform position optimization with time-domain sensitivity as the x-axis and frequency-domain energy entropy as the y-axis (where higher time-domain sensitivity is better, and lower frequency-domain energy entropy is better). The results are as follows: Figure 7 As shown, the optimal vibration monitoring locations were ultimately determined to be 7, 5, 8, and 6, and the optimal vibration monitoring channels were Y and Z; the specific locations of the optimal channels are as follows. Figure 8 As shown.

[0170] This invention also provides a gearbox vibration position optimization system based on Masta simulation and frequency domain energy entropy, such as... Figure 9 As shown, it includes:

[0171] Manufacturing error simulation analysis module: A simulation model of manufacturing errors in the gearbox powertrain system is established using Pro / E, Ansys, and Masta to obtain the vibration response of the gearbox housing under both normal and manufacturing error fault conditions. Based on the vibration response acceleration results, 11 measurement points with relatively large responses and easy installation are initially selected.

[0172] Data acquisition module: Based on the simulation results, the above-mentioned vibration acceleration sensor is installed on the gearbox of a certain engineering machinery to measure the vibration acceleration signal of the gearbox;

[0173] The time-domain sensitivity optimization module performs noise reduction preprocessing on the measured transmission vibration acceleration signal, and then extracts the maximum, minimum, peak, peak-to-peak, mean, variance, absolute mean, root mean amplitude, kurtosis, skewness, waveform index, margin index, peak index, impulse index, kurtosis index, and root mean square value in the time domain at the main operating speeds of the four gears (F1, F4, R2, and R3), resulting in 16 index parameters for 33 channels. The response matrix of the feature parameters is obtained from the feature results, and normalized to obtain the sensitivity matrix for each channel. Based on the sensitivity definition, the mean values ​​of the 16 indicators for each channel are obtained, and the preferred channels are determined. Simultaneously, the mean sensitivity values ​​in the three directions of the 11 positions are classified according to X, Y, and Z to obtain the preferred directions.

[0174] Frequency domain energy entropy optimization module: Fourier transform the vibration signal after TSA noise reduction, calculate the characteristic frequencies of the bearings and 12 gears of the gearbox under study based on its structure, and use the upper and lower cutoff frequencies of 1 / 3 octave as the bandwidth. The bandwidth containing the characteristic frequencies is filtered out and divided into j segments, and the energy of each segment is represented by Qij, resulting in an energy frequency band matrix of 33 channels. The probability density matrix is ​​further calculated from the energy frequency band matrix. The energy information entropy matrix is ​​obtained by calculating the energy information entropy. The three-dimensional energy information entropy sums are sorted from low to high to obtain the preferred channels. At the same time, the energy information entropy sums of the three directions at 11 positions are classified according to X, Y, and Z to obtain the preferred directions.

[0175] Vibration location optimization judgment module: The result time domain sensitivity optimization module and frequency domain energy entropy optimization module arrange the mean time domain sensitivity from large to small and the mean frequency domain energy entropy from small to large to confirm the final optimal vibration monitoring location and direction.

[0176] This invention also provides a test bench for conducting offline tests on simulated initial selection locations. For example... Figure 3 As shown, the test bench consists of an input system, a base system, a bracket system, a gearbox, an output system, and a sensor support system.

[0177] The input system mainly consists of a drive motor, a motor bracket, and a connecting flange, and its main functions are to provide input power to the gearbox and to clamp and move the motor.

[0178] The base system consists of a cross-shaped cast platform, vibration isolators, and a hydraulic system, providing a mounting foundation and hydraulic clamping function for the input, bracket, support, and output systems. The base is machined with cross-shaped grooves for easy movement and also includes power cylinders and hydraulic components for clamping the connected systems. Vibration isolators are installed at the bottom of the base to effectively isolate external interference.

[0179] The carriage system primarily transports the gearboxes from the production line to suitable testing positions, enabling rapid and accurate positioning of the tested item. Once the gearbox running and testing are completed, the carriage system can lift the tested gearbox to a suitable workstation for rapid transport out of the test bench.

[0180] The sensor support system consists of sensors, cylinders, etc. It uses a probe-type accelerometer with an embedded elastic element, which can realize the automatic movement and precise positioning of the sensor, eliminating the trouble of manual clamping multiple times.

[0181] This invention presents a method for optimizing vibration location during offline detection by combining simulation and vibration testing. It simulates the acceleration response of a gearbox under normal conditions and manufacturing defects to determine the initial vibration monitoring point, and verifies the simulation results by combining testing, thereby achieving a closed loop.

[0182] This invention proposes a vibration monitoring and position optimization method for transmission components that combines time-domain sensitivity and frequency-domain energy entropy. In the time domain, the characteristic parameter response matrix at the main operating speed of each gear is obtained and normalized, and the vibration position is optimized through the sensitivity matrix. In the frequency domain, the characteristic frequency at the main operating speed of each gear is obtained, and the energy frequency band is divided according to the concept of 1 / 3 octave band to establish an energy probability density matrix, and the vibration position is optimized through the frequency-domain energy entropy.

[0183] The present invention also developed a corresponding test bench and control system to realize intelligent detection of transmission components after production, which helps to quickly and accurately locate faults in transmission components.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the position of vibration monitoring of transmission components, characterized in that, include: S1, Establish a dynamic simulation model of the transmission components; S2, Based on the dynamic simulation model of the transmission component, the transmission component under normal and manufacturing error conditions is analyzed to obtain the vibration response results under the two conditions, and based on the vibration response results, multiple initial positions are determined as preliminary vibration monitoring points; S3, Vibration test is performed based on multiple initially selected positions to obtain vibration acceleration signals at each initially selected position; S4, based on the vibration acceleration signal, perform time-domain sensitivity optimization to obtain the time-domain sensitivity of each initially selected position; further includes: acquiring vibration acceleration signals in three directions at each initially selected position and performing noise reduction processing; extracting the maximum, minimum, peak, peak-to-peak, mean, variance, absolute mean, root mean amplitude, kurtosis, skewness, waveform index, margin index, peak index, impulse index, kurtosis index, and root mean square value in the time domain at the main operating speed of the target gear, obtaining 16 index parameters for each channel; calculating the comprehensive response value of each channel based on the 16 index parameters at the target gear, using the comprehensive response value as the response result of the feature parameters; obtaining the response matrix of the feature parameters based on the response result of the feature parameters, and performing normalization calculation to obtain the sensitivity matrix of each channel; obtaining the mean of the 16 indexes of each channel based on the sensitivity matrix and determining the preferred channel, while classifying the mean sensitivity of the three directions at each initially selected position according to X, Y, and Z to obtain the preferred direction; S5, based on the vibration acceleration signal, frequency domain energy entropy optimization is performed to obtain the frequency domain energy entropy of each initially selected position; it also includes: acquiring vibration acceleration signals in three directions at each initially selected position and performing noise reduction processing; performing Fourier transform on the noise-reduced vibration acceleration signal; calculating the characteristic frequency according to the structure of the transmission component under study, using the upper and lower cutoff frequencies of 1 / 3 octave as the bandwidth, filtering out the bandwidth containing the characteristic frequency and segmenting it to obtain the energy frequency band matrix of each channel; calculating the probability density matrix based on the energy frequency band matrix; obtaining the energy information entropy of each channel based on the probability density matrix, and calculating the energy information entropy matrix through the energy information entropy; sorting the sum of the three-directional energy information entropy from low to high to obtain the preferred channel, and simultaneously classifying the sum of the three-directional energy information entropy of each initially selected position according to X, Y, and Z to obtain the preferred direction; S6. Based on the time-domain sensitivity and frequency-domain energy entropy of each initially selected location, determine the optimal vibration monitoring location.

2. The method for optimizing the position of vibration monitoring of transmission components according to claim 1, characterized in that, S2 further includes: obtaining the manufacturing error distribution of the transmission component; and selecting a target manufacturing error as the excitation source for the dynamic simulation model of the transmission component based on the manufacturing error distribution.

3. The method for optimizing the position of vibration monitoring of transmission components according to claim 1, characterized in that, The determination of the target gear includes: obtaining the gear transmission route of each gear; analyzing the gear pairs included in each gear transmission route; and determining the gear corresponding to these gear transmission routes as the target gear when two or more gear transmission routes cover all gear pairs of the transmission components.

4. The method for optimizing the position of vibration monitoring of transmission components according to claim 1, characterized in that, Each of the initial selected positions corresponds to a channel in one direction.

5. The method for optimizing the position of vibration monitoring of transmission components according to claim 1, characterized in that, After determining the initial location, time-domain index parameters are extracted from samples of multiple transmission components.

6. The method for optimizing the position of vibration monitoring of a transmission component according to claim 1, characterized in that, S6 also includes: using the time-domain sensitivity of each initially selected location as the horizontal axis and the frequency-domain energy entropy as the vertical axis to optimize the location and determine the preferred location and channel for vibration monitoring.

7. A vibration monitoring and position optimization system for transmission components, characterized in that, include: The manufacturing error simulation analysis module is used to establish a dynamic simulation model of the transmission components to obtain the vibration response of the transmission components under two conditions: normal operation and manufacturing error failure. The initial position is determined based on the vibration response acceleration results. The data acquisition module is used to place vibration acceleration sensors on the transmission component sample based on the simulation results and measure the vibration acceleration signals at each initially selected position of the transmission component. The time-domain sensitivity optimization module is used to optimize the time-domain sensitivity based on the vibration acceleration signal to obtain the time-domain sensitivity of each initially selected position. It also includes: acquiring vibration acceleration signals in three directions at each initially selected position and performing noise reduction processing; extracting the maximum, minimum, peak, peak-to-peak, mean, variance, absolute mean, root mean square amplitude, kurtosis, skewness, waveform index, margin index, peak index, impulse index, kurtosis index, and root mean square value in the time domain at the main operating speed of the target gear, obtaining 16 index parameters for each channel; calculating the comprehensive response value of each channel based on the 16 index parameters at the target gear, using the comprehensive response value as the response result of the feature parameters; obtaining the response matrix of the feature parameters based on the response result of the feature parameters, and performing normalization calculation to obtain the sensitivity matrix of each channel; obtaining the mean of the 16 indexes for each channel based on the sensitivity matrix and determining the preferred channel; and simultaneously classifying the mean sensitivity of the three directions at each initially selected position according to X, Y, and Z to obtain the preferred direction. The frequency domain energy entropy optimization module is used to optimize the frequency domain energy entropy based on the vibration acceleration signal to obtain the frequency domain energy entropy of each initially selected position. It also includes: acquiring vibration acceleration signals in three directions at each initially selected position and performing noise reduction processing; performing Fourier transform on the noise-reduced vibration acceleration signal; calculating the characteristic frequency of the transmission component under study based on its structure, using the upper and lower cutoff frequencies of 1 / 3 octave band as bandwidth, filtering out and segmenting the bandwidth containing the characteristic frequency to obtain the energy frequency band matrix of each channel; calculating the probability density matrix based on the energy frequency band matrix; obtaining the energy information entropy of each channel based on the probability density matrix, and calculating the energy information entropy matrix through the energy information entropy; sorting the sums of the three-directional energy information entropies from low to high to obtain the preferred channels, and simultaneously classifying the sums of the three-directional energy information entropies at each initially selected position according to X, Y, and Z to obtain the preferred directions. The vibration location optimization judgment module is used to confirm the final preferred location and direction for vibration monitoring based on the results of the time-domain sensitivity optimization module and the frequency-domain energy entropy optimization module.

8. A test bench for optimizing the vibration monitoring position of a transmission component, used to implement the method for optimizing the vibration monitoring position of a transmission component as described in any one of claims 1-6, characterized in that, The test bench includes an input system, a base system, a bracket system, a transmission component, an output system, and a sensor support system. The input system provides power to the transmission component. The base system provides the mounting foundation and hydraulic clamping function for the input system, bracket system, support system, and output system. The bracket system transports the transmission component to the desired test position and lifts the test chamber out of the test bench after the test is completed. The sensor support system enables automatic movement and precise positioning of the sensors.

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