Method, device and equipment for monitoring main bearing of heading machine and medium

Abstract

The application provides a kind of based on the monitoring processing method, device and equipment and medium of main bearing of heading machine, the method comprises: monitoring obtains the running data of main bearing in first preset time, and obtains first characteristic value according to the running data in first preset time, to construct the first time function corresponding to running data according to first characteristic value;Monitoring obtains the running data of main bearing at first time after constructing first time function, and obtains the first predicted value corresponding to first time according to first time function;When determining the difference between the running data of first time and first predicted value is greater than the first difference value of preset, determine that main bearing is in degradation stage, and execute the alarm processing of main bearing in degradation stage.Can timely alarm when main bearing enters degradation stage to adjust the load working condition and lubrication condition of main bearing, ensure project on time completion.

Description

Technical Field

[0001] This application relates to the field of rail transit technology, and in particular to a monitoring and processing method, device, equipment and medium based on the main bearing of a tunneling machine. Background Technology

[0002] Tunnel boring machines (TBMs) are critical and essential pieces of equipment used in tunnel engineering. The cutterhead of a TBM plays a vital role in tunnel construction. The main bearing, as the drive system of the cutterhead, bears all the thrust and torque required by the cutterhead, and its failure and repair are extremely difficult. Therefore, accurately determining the stage of operation of the main bearing is crucial to ensuring the smooth progress of tunnel engineering.

[0003] Current technologies typically estimate the service life of main bearings based on their structural parameters and load conditions. When the remaining service life is low, the load conditions are adjusted to extend the service life and ensure the timely completion of tunnel projects. However, during construction, main bearings are often subjected to complex stress conditions such as excessively heavy loads, large eccentric loads, and strong time-varying loads. The service life of main bearings estimated solely based on structural parameters and load conditions is usually inaccurate. Therefore, it is impossible to adjust the load conditions and lubrication of the main bearings in a timely manner to ensure the timely completion of the project. Summary of the Invention

[0004] This application provides a monitoring and processing method, device, equipment, and medium based on the main bearing of a tunneling machine, which solves the problem that the inaccurate estimation of the service life of the main bearing in the prior art leads to the inability to adjust the load condition and lubrication condition of the main bearing in a timely manner to ensure the timely completion of the project.

[0005] In a first aspect, this application provides a monitoring and processing method based on the main bearing of a tunneling machine, comprising: monitoring and acquiring the operating data of the main bearing within a first preset time period, and acquiring a first feature value based on the operating data within the first preset time period, so as to construct a first time function corresponding to the operating data based on the first feature value; monitoring and acquiring the operating data of the main bearing at a first moment after the construction of the first time function, and acquiring a first predicted value corresponding to the first moment based on the first time function; when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, determining that the main bearing is in a degradation stage, and performing alarm processing for the main bearing being in a degradation stage.

[0006] In one specific embodiment, the operating data includes the outer ring vibration data of the main bearing; then the step of monitoring and acquiring the operating data of the main bearing within a first preset time period, and acquiring a first feature value based on the operating data within the first preset time period, so as to construct a first time function corresponding to the operating data based on the first feature value, includes: monitoring and acquiring the outer ring vibration data of the main bearing at least two moments within the first preset time period using multiple vibration sensors disposed on the outer ring of the main bearing; acquiring first outer ring vibration feature values ​​of multiple outer ring vibration data corresponding to each moment, and constructing a first time function corresponding to the outer ring vibration data based on the first outer ring vibration feature values ​​corresponding to the at least two moments.

[0007] In one specific embodiment, the first outer ring vibration characteristic value of the plurality of outer ring vibration data includes one of the following: the maximum value of the plurality of outer ring vibration data, the minimum value of the plurality of outer ring vibration data, the mean value of the plurality of outer ring vibration data, and the kurtosis of the plurality of outer ring vibration data.

[0008] In one specific embodiment, the vibration data of the outer ring of the main bearing includes one or more of the following: axial vibration data of the first outer ring of the main bearing, radial vibration data of the first outer ring of the main bearing, and axial vibration data of the second outer ring of the main bearing.

[0009] In one specific implementation, the operating data includes oil outlet particle data of the main bearing; then the monitoring and acquisition of operating data of the main bearing within a first preset time period, and the acquisition of a first feature value based on the operating data within the first preset time period, to construct a first time function corresponding to the operating data based on the first feature value, includes:

[0010] Monitoring and acquiring oil outlet particle data of the main bearing within a first preset time period, selecting oil outlet particle data at least two moments within the first preset time period as first oil outlet particle feature values; constructing a first time function corresponding to the oil outlet particle data based on the first oil outlet particle feature values.

[0011] In one specific embodiment, the oil outlet particle data includes one or more of the following: size data of the oil outlet particles of the main bearing, concentration data of the oil outlet particles of the main bearing, material data of the oil outlet particles of the main bearing, and shape data of the oil outlet particles of the main bearing.

[0012] In one specific embodiment, the operating data includes the outer ring vibration data of the main bearing, and the method further includes: obtaining multiple characteristic frequencies of the main bearing based on the structural parameters of the main bearing; then, when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, determining that the main bearing is in a degradation stage and performing alarm processing for the main bearing being in a degradation stage includes: when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, or when the outer ring vibration data and multiple characteristic frequencies of the main bearing are not equal, determining that the main bearing is in a degradation stage and performing alarm processing for the main bearing being in a degradation stage.

[0013] In one specific embodiment, the multiple characteristic frequencies of the main bearing include a combination of the following: a first characteristic frequency of the main thrust roller of the main bearing, a second characteristic frequency of the auxiliary thrust roller of the main bearing, a third characteristic frequency of the radial roller of the main bearing, a fourth characteristic frequency of the raceway surface of the main thrust roller in contact with the first outer ring, a fifth characteristic frequency of the raceway surface of the main thrust roller in contact with the inner ring, a sixth characteristic frequency of the raceway surface of the auxiliary thrust roller in contact with the second outer ring, a seventh characteristic frequency of the raceway surface of the auxiliary thrust roller in contact with the inner ring, an eighth characteristic frequency of the raceway surface of the radial roller in contact with the first outer ring, a ninth characteristic frequency of the raceway surface of the radial roller in contact with the inner ring, a tenth characteristic frequency of the main thrust cage of the main bearing, an eleventh characteristic frequency of the auxiliary thrust cage of the main bearing, and a twelfth characteristic frequency of the radial cage of the main bearing.

[0014] In one specific embodiment, after determining that the main bearing is in a degradation stage, the method further includes: monitoring and acquiring the operating data of the main bearing within a second preset time period, and acquiring a second feature value based on the operating data within the second preset time period, so as to construct a second time function corresponding to the operating data based on the second feature value; monitoring and acquiring the operating data of the main bearing at a second moment after constructing the second time function, and acquiring a second predicted value corresponding to the second moment based on the second time function; when it is determined that the difference between the operating data at the second moment and the second predicted value is greater than a preset second difference, determining that the main bearing has failed, and performing alarm processing for the main bearing failure.

[0015] Secondly, this application provides a monitoring and processing device based on the main bearing of a tunneling machine, comprising: an acquisition module for monitoring and acquiring operating data of the main bearing within a first preset time period; a processing module for acquiring a first feature value based on the operating data within the first preset time period, and constructing a first time function based on the first feature value; the acquisition module is further configured to monitor and acquire operating data of the main bearing at a first moment after the first time function is constructed; the processing module is further configured to acquire a first predicted value corresponding to the first moment based on the first time function; when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, the main bearing is determined to be in a degradation stage, and an alarm processing for the main bearing being in a degradation stage is executed.

[0016] Thirdly, this application provides an electronic device, including: a processor, a memory, and a communication interface; the memory is used to store executable instructions of the processor; wherein the processor is configured to execute the monitoring and processing method based on the main bearing of a tunneling machine as described in the first aspect by executing the executable instructions.

[0017] Fourthly, this application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the monitoring and processing method based on the main bearing of a tunneling machine as described in the first aspect.

[0018] This application provides a monitoring and processing method, device, equipment, and medium based on the main bearing of a tunneling machine. The method includes: monitoring and acquiring the operating data of the main bearing within a first preset time period, and acquiring a first feature value based on the operating data within the first preset time period, so as to construct a first time function corresponding to the operating data based on the first feature value; monitoring and acquiring the operating data of the main bearing at a first moment after the construction of the first time function, and acquiring a first predicted value corresponding to the first moment based on the first time function; when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, determining that the main bearing is in a degradation stage, and performing alarm processing for the main bearing being in a degradation stage. Compared to existing technologies that estimate the lifespan of the main bearing based on structural parameters and load conditions, this application obtains characteristic values ​​from the main bearing's operating data within a preset time period, constructs a time function corresponding to the operating data, and predicts the operating data at the first moment based on this time function. When the actual operating data at the first moment differs significantly from the predicted value, it is determined that the tunneling machine's main bearing has entered the degradation stage and an alarm is triggered. This allows for accurate determination of the current usage stage based on the main bearing's operating data, and timely alarms can be issued when the main bearing enters the degradation stage to adjust the load conditions and lubrication of the main bearing, ensuring the project is completed on time. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the main bearing of a tunneling machine;

[0021] Figure 2 A schematic flowchart of an embodiment of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application;

[0022] Figure 3 A schematic flowchart of Embodiment 2 of the monitoring and processing method based on the main bearing of a tunneling machine provided in this application;

[0023] Figure 4 A flowchart illustrating Embodiment 3 of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application;

[0024] Figure 5 A schematic flowchart of Embodiment 4 of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application;

[0025] Figure 6 A schematic flowchart of Embodiment 5 of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application;

[0026] Figure 7 A schematic diagram of an embodiment of a monitoring and processing device based on the main bearing of a tunneling machine provided in this application;

[0027] Figure 8 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments made by those skilled in the art under the guidance of these embodiments are within the scope of protection of this application.

[0029] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Tunnel boring machines (TBMs) are critical and essential pieces of equipment used in tunnel engineering. The cutterhead of a TBM plays a vital role in tunnel construction. The main bearing, as the drive system of the cutterhead, bears all the thrust and torque required by the cutterhead, and its failure and repair are extremely difficult. Therefore, accurately determining the stage of operation of the main bearing is crucial to ensuring the smooth progress of tunnel engineering.

[0031] Figure 1 This is a structural schematic diagram of the main bearing of a tunneling machine. Figure 1 As shown, the main bearing of a tunneling machine mainly consists of raceways, rollers, and a cage. The raceways include an inner ring 11, a first outer ring 12, and a second outer ring 13. The rollers include main thrust rollers 14, auxiliary thrust rollers 15, and radial rollers 16. The cage includes a main thrust cage 17, an auxiliary thrust cage 18, and a radial cage 19. The rollers roll within the cage pockets, while the cage slides within the raceways. During normal operation of the tunneling machine's main bearing, there is contact between the cage and the rollers and raceways, as well as between the rollers and raceways. Many factors can lead to the degradation or even failure of the main bearing. For example, due to the large load at the contact point between the raceways and rollers, the rolling of the rollers will cause changes in the microstructure, leading to fatigue cracks and raceway spalling. The cage guides the rollers to rotate, and there are impacts between the rollers and the cage, as well as between the cages themselves, all of which can easily cause cage breakage. When the seals of the tunneling machine's main bearing are damaged and impurities enter, wear will be accelerated, leading to lubrication failure.

[0032] Current technologies typically estimate the service life of main bearings based on their structural parameters and load conditions. When the remaining service life is low, the load conditions are adjusted to extend the service life and ensure the timely completion of tunnel projects. However, during construction, main bearings are often subjected to complex stress conditions such as excessively heavy loads, large eccentric loads, and strong time-varying loads. The service life of main bearings estimated solely based on structural parameters and load conditions is usually inaccurate. Therefore, it is impossible to adjust the load conditions and lubrication of the main bearings in a timely manner to ensure the timely completion of the project.

[0033] Based on the above-mentioned technical problems, the technical conception process of this application is as follows: how to accurately determine the stage of use of the main bearing, and adjust the load conditions and lubrication conditions of the main bearing in a timely manner to ensure that the project is completed on time.

[0034] The technical solution of this application will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0035] Figure 2 This is a flowchart illustrating an embodiment of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application. See also... Figure 2 The monitoring and processing method based on the main bearing of the tunneling machine specifically includes the following steps:

[0036] Step S201: Monitor and acquire the operating data of the main bearing within a first preset time period, and obtain a first feature value based on the operating data within the first preset time period, so as to construct a first time function corresponding to the operating data based on the first feature value.

[0037] In this embodiment, the operating data of the main bearing can be monitored and acquired within a first preset time period. This operating data may be the vibration data of the outer ring of the main bearing, for example... Figure 1 Axial vibration data of the first outer ring 12. For example, the axial vibration data of the first outer ring of the main bearing at two moments within a first preset time period can be monitored and acquired by multiple vibration sensors disposed along the axial direction of the first outer ring.

[0038] Specifically, multiple axial vibration data of the first outer ring are acquired at each time step. Based on these data, a first feature value is obtained for each of the multiple axial vibration data of the first outer ring at each time step. For example, the first feature value can be the maximum value among the multiple axial vibration data of the first outer ring at each time step. Based on the first feature values ​​at these two time steps, a first time function corresponding to the axial vibration data of the first outer ring is constructed.

[0039] For example, the first preset time can be from time t1 to time t3, and the first feature values ​​corresponding to two times within this first preset time are obtained as A. α1 and A α2 Based on these two first eigenvalues, the first time function corresponding to the axial vibration data of the first outer ring is constructed as follows:

[0040]

[0041] Step S202: Monitor and acquire the operating data of the main bearing at the first moment after the first time function is constructed, and obtain the first predicted value corresponding to the first moment based on the first time function.

[0042] Step S203: When it is determined that the difference between the operating data at the first moment and the first predicted value is greater than the preset first difference, it is determined that the main bearing is in the degradation stage, and an alarm process for the main bearing being in the degradation stage is executed.

[0043] In this embodiment, the operating data of the main bearing at a first moment, i.e., the first predicted value, can be predicted based on the constructed first time function. For example, the first predicted value A corresponding to the axial vibration data of the first outer ring constructed in the previous example can be obtained based on the first time function. α (T).

[0044] Obtain the actual operating data of the main bearing at the first moment, such as the axial vibration data A′ of the first outer ring of the main bearing at the first moment T. α .

[0045] Obtain the axial vibration data A′ of the first outer ring at the first moment. α Compared with the first predicted value A α If the difference between (T) and the main bearing is determined to be in the degradation stage when the difference is greater than the preset first difference, an alarm process for the main bearing to be in the degradation stage is executed.

[0046] In one example, after determining that the main bearing is not in the degradation stage, the operating data of the main bearing can be re-monitored and acquired at preset intervals for a first preset time period, and a first feature value can be obtained based on the operating data within the first preset time period, so as to reconstruct the first time function corresponding to the operating data based on the first feature value.

[0047] In one example, the operating data of the main bearing at the first moment can be a random variable, satisfying Chebyshev's inequality:

[0048]

[0049] Where P represents probability, X represents the operating data of the main bearing, K is any positive real number, μ is the first predicted value, and σ is the standard deviation. When K is 3, the probability that any sample falls outside the 3σ interval, i.e., [μ-3σ, μ+3σ], is less than 1 / 9. Therefore, when the operating data at the first moment exceeds the warning interval of 3σ, it is considered that the main bearing of the tunneling machine has failed and has begun to degrade, and the 3σ position is used as the degradation point to divide the degradation stage.

[0050] In this embodiment, the operating data of the main bearing within a first preset time period is monitored and acquired, and a first feature value is obtained based on the operating data within the first preset time period, so as to construct a first time function corresponding to the operating data based on the first feature value; the operating data of the main bearing at a first moment after the construction of the first time function is monitored and acquired, and a first predicted value corresponding to the first moment is obtained based on the first time function; when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, it is determined that the main bearing is in the degradation stage, and an alarm processing for the main bearing being in the degradation stage is executed. Compared to existing technologies that estimate the lifespan of the main bearing based on structural parameters and load conditions, this application obtains characteristic values ​​from the main bearing's operating data within a preset time period, constructs a time function corresponding to the operating data, and predicts the operating data at the first moment based on this time function. When the actual operating data at the first moment differs significantly from the predicted value, it is determined that the tunneling machine's main bearing has entered the degradation stage and an alarm is triggered. This allows for accurate determination of the current usage stage based on the main bearing's operating data, and timely alarms can be issued when the main bearing enters the degradation stage to adjust the load conditions and lubrication of the main bearing, ensuring the project is completed on time.

[0051] Figure 3 This is a flowchart illustrating a second embodiment of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application. Figure 2 Based on the illustrated embodiment, see also Figure 3 The monitoring and processing method based on the main bearing of the tunneling machine specifically includes the following steps:

[0052] Step S301: By using multiple vibration sensors installed on the outer ring of the main bearing, monitor and acquire the vibration data of the outer ring of the main bearing at at least two moments within a first preset time period.

[0053] Step S302: Obtain the first outer ring vibration feature value of the multiple outer ring vibration data corresponding to each time moment, and construct the first time function corresponding to the outer ring vibration data based on the first outer ring vibration feature value corresponding to at least two time moments.

[0054] In this embodiment, the operating data may include the vibration data of the outer ring of the main bearing. For example, the outer ring vibration data may be the axial vibration data of the first outer ring of the main bearing, the radial vibration data of the first outer ring of the main bearing, or the axial vibration data of the second outer ring of the main bearing.

[0055] Vibration data of the outer ring of the main bearing can be monitored and acquired at least two moments within a first preset time period by using multiple vibration sensors installed on the outer ring. The outer ring vibration data can be the amplitude of the vibration signal after noise reduction processing. For example, axial vibration data A of the first outer ring of the main bearing at two moments within the first preset time period can be monitored and acquired by using L vibration sensors installed along the axial direction of the first outer ring. l =a l (t); The radial vibration data A of the first outer ring of the main bearing at two moments within a first preset time period can be monitored and acquired by setting M vibration sensors in the radial direction of the first outer ring. m =a m (t); The axial vibration data A of the second outer ring of the main bearing at two moments within a first preset time period can be monitored and obtained by setting N vibration sensors along the axial direction of the second outer ring. n =a n (t).

[0056] In this embodiment, the first outer-circle vibration characteristic value of multiple outer-circle vibration data corresponding to each time moment is obtained. The first outer-circle vibration characteristic value of the multiple outer-circle vibration data can be the maximum value among the multiple outer-circle vibration data; or, the first outer-circle vibration characteristic value of the multiple outer-circle vibration data can be the minimum value among the multiple outer-circle vibration data; or, the first outer-circle vibration characteristic value of the multiple outer-circle vibration data can be the mean value among the multiple outer-circle vibration data; or, the first outer-circle vibration characteristic value of the multiple outer-circle vibration data can be the kurtosis of the multiple outer-circle vibration data.

[0057] For example, the outer ring vibration data is the axial vibration data A of the first outer ring of the main bearing. l =a l (t), then for each moment within the first preset time period, the first outer ring vibration characteristic value of the multiple outer ring vibration data can be the maximum value among the L first outer ring axial vibration data: A1 = max(A l )=max[a l (t)];

[0058] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the minimum value among L axial vibration data of the first outer ring: A2 = min(A l )=min[a l (t)];

[0059] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the average of L first outer ring axial vibration data:

[0060]

[0061] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the kurtosis of L first outer ring axial vibration data:

[0062]

[0063] For example, the outer ring vibration data is the radial vibration data A of the first outer ring of the main bearing. m =a m (t), then for each moment within the first preset time period, the first outer ring vibration characteristic value of the multiple outer ring vibration data can be the maximum value among the M radial vibration data of the first outer ring: A5 = max(A m )=max[a m (t)];

[0064] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the minimum value among M radial vibration data of the first outer ring: A6 = min(A m )=min[a m (t)];

[0065] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the mean of M radial vibration data of the first outer ring:

[0066]

[0067] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the kurtosis of M first outer ring radial vibration data:

[0068]

[0069] For example, the outer ring vibration data is the axial vibration data A of the second outer ring of the main bearing. n =a n (t), then for each moment within the first preset time period, the first outer ring vibration characteristic value of the multiple outer ring vibration data can be the maximum value among the N second outer ring axial vibration data: A9 = max(A n )=max[a n (t)];

[0070] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the minimum value among N second outer ring axial vibration data: A 10 =min(A n )=min[an (t)];

[0071] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the average of N second outer ring axial vibration data:

[0072]

[0073] Alternatively, the first outer ring vibration characteristic value of multiple outer ring vibration data can be the kurtosis of N second outer ring axial vibration data:

[0074]

[0075] Based on the vibration characteristic values ​​of the first outer ring corresponding to at least two time points, a first time function corresponding to the outer ring vibration data is constructed. For example, the first preset time can be from time t1 to time t3, and the vibration characteristic values ​​of the first outer ring corresponding to the axial vibration data of the first outer ring at two time points within this first preset time are A... α1 and A a2 Based on these two vibration characteristic values ​​of the first outer ring, the first time function corresponding to the axial vibration data of the first outer ring is constructed as follows:

[0076]

[0077] Step S303: Monitor and acquire the outer ring vibration data of the main bearing at the first moment after constructing the first time function corresponding to the outer ring vibration data, and obtain the first outer ring vibration prediction value corresponding to the first moment according to the first time function corresponding to the outer ring vibration data.

[0078] Step S304: When it is determined that the difference between the outer ring vibration data at the first moment and the predicted value of the first outer ring vibration is greater than the preset first outer ring vibration difference, the main bearing is determined to be in the degradation stage, and an alarm process for the main bearing to be in the degradation stage is executed.

[0079] In this embodiment, the vibration data of the main bearing at a first moment can be predicted based on the first time function corresponding to the constructed outer ring vibration data, i.e., the predicted value of the first outer ring vibration. For example, the predicted value A of the first outer ring axial vibration at the first moment T can be obtained based on the first time function corresponding to the axial vibration data of the first outer ring constructed in the aforementioned example. α (T).

[0080] Obtain the actual outer ring vibration data of the main bearing at the first moment, for example, the axial vibration data A′ of the first outer ring of the main bearing at the first moment T. α .

[0081] Obtain the axial vibration data A′ of the first outer ring at the first moment. αThe predicted axial vibration value A of the first outer ring α If the difference (T) is greater than the preset first outer ring vibration difference, the main bearing is determined to be in the degradation stage, and an alarm process for the main bearing to be in the degradation stage is executed.

[0082] In this embodiment, the vibration data of the outer ring of the main bearing is monitored and acquired. Feature values ​​of the outer ring vibration data are extracted, and a time function corresponding to the outer ring vibration data is constructed based on these feature values. The outer ring vibration data at a first moment is predicted based on this time function. When the difference between the actual outer ring vibration data at the first moment and the predicted outer ring vibration value is greater than a preset outer ring vibration difference value, the main bearing is determined to be in a degradation stage, and an alarm is triggered. This method can more accurately determine the service stage of the main bearing based on its outer ring vibration data, further solving the problem of inaccurate estimation of the main bearing's service life in existing technologies, which leads to the inability to adjust the load conditions and lubrication of the main bearing in a timely manner to ensure timely project completion.

[0083] Figure 4 This is a flowchart illustrating Embodiment 3 of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application. Figure 2 or Figure 3 Based on the illustrated embodiment, see also Figure 4 The monitoring and processing method based on the main bearing of the tunneling machine specifically includes the following steps:

[0084] Step S401: Monitor and acquire the oil outlet particle data of the main bearing within a first preset time period, and select the oil outlet particle data at at least two moments within the first preset time period as the first oil outlet particle feature value.

[0085] In this embodiment, the operational data may include oil outlet particle data of the main bearing. The oil outlet particle data can characterize the wear condition of components such as the bearing races, rollers, and cages of the tunneling machine's main bearing. For example, the oil outlet particle data may be the size data of the oil outlet particles, the concentration data of the oil outlet particles, the material data of the oil outlet particles, or the shape data of the oil outlet particles.

[0086] The system monitors and acquires oil outlet particle data of the main bearing within a first preset time period. For example, the system can monitor and acquire the size data of the oil outlet particles within the first preset time period. The size data of the oil outlet particles may include the length-to-width ratio (HWR) of the oil outlet particles and the equivalent diameter D of the oil outlet particles. eqThe roundness (CD) of the oil outlet particles, the height-thickness ratio (HTR) of the oil outlet particles, the width-thickness ratio (WTR) of the oil outlet particles, and the equivalent spherical diameter (D) of the oil outlet particles. eqS , such as the spherical degree (SD) of the particles at the oil outlet.

[0087] For example, the concentration data of particles at the oil outlet of the main bearing can be monitored and acquired within a first preset time period. The concentration data of particles at the oil outlet can be the particle concentration corresponding to particles of different sizes at the oil outlet. For example, the particle concentration corresponding to particles with an outer diameter of 4μm, 6μm, or 14μm.

[0088] For example, material data of the oil outlet particles in the main bearing can be monitored and acquired within a first preset time period. Figure 1 As shown, the inner ring 11, the first outer ring 12, and the second outer ring 13 are made of type I material; the main thrust roller 14, the auxiliary thrust roller 15, and the radial roller 16 are made of type II material; and the main thrust cage 17, the auxiliary thrust cage 18, and the radial cage 19 are made of type III material. The proportions of type I, type II, and type III materials can be obtained as material data for the oil outlet particles.

[0089] For example, the shape data of the particles at the oil outlet of the main bearing can be monitored and acquired within a first preset time period. Under normal wear conditions, the friction pairs inside the main bearing of the tunneling machine are well lubricated. At this time, only a small number of fine, thin flake particles with a size of 1-15 μm are generated in the oil. When lubrication is insufficient, local high temperature will cause the micro-protrusions of the friction pair to "weld" together, and tear during relative movement, eventually detaching from the parent body to form blocky, adherent particles. In addition, there are sliding particles, cutting particles, fatigue flake particles, spherical particles, oxide particles, and corrosion particles. Different particle shapes at the oil outlet can characterize different wear states. Sliding particles and cutting particles are both rapidly deteriorating particles; if left to develop, the tunneling machine will experience accidents in a short period of time. Fatigue flake particles and spherical particles are fatigue wear products, and their quantity and size indicate the degree of this wear. Oxidation particles and corrosion particles are both chemical corrosion, mostly due to the presence of water in the lubrication system or changes in the physicochemical properties of the lubricating medium.

[0090] In this embodiment, oil outlet particle data at at least two moments within the first preset time period can be selected as the first oil outlet particle feature value.

[0091] Step S402: Based on the first oil outlet particle characteristic value, construct the first time function corresponding to the oil outlet particle data.

[0092] For example, the concentration data of oil outlet particles at times t4 and t5 within a first preset time period can be selected. For instance, if the oil outlet particle concentration at time t4 is ρ1 and the oil outlet particle concentration at time t5 is ρ2, then the first time function corresponding to this oil outlet particle concentration data is:

[0093]

[0094] For example, material data of the oil outlet particles at times t5 and t6 within a first preset time period can be selected. Let the proportions of type I, type II, and type III materials in the oil outlet of the tunneling machine main bearing be x, y, and z, respectively. When key components come into contact, the proportions of each material will vary depending on the friction pair. For instance, at time t5, the proportions of type I, type II, and type III materials might be x1, y1, and z1, respectively, and at time t6, they might be x2, y2, and z2, respectively. Then, the first time function corresponding to this oil outlet particle material data is:

[0095]

[0096]

[0097]

[0098] For example, the size data of the oil outlet particles at times t7 and t8 within a first preset time period can be selected. The size data may include the aspect ratio HWR and the equivalent diameter D. eq Roundness CD, Length-to-thickness ratio HTR, Width-to-thickness ratio WTR, Equivalent sphere diameter D eqS sphericity SD, etc. For example, the first time function corresponding to the aspect ratio data of the oil outlet particles is:

[0099]

[0100] The first-time function corresponding to the equivalent diameter data of the oil outlet particles is:

[0101]

[0102] The first time function corresponding to the oil outlet particle roundness data is:

[0103]

[0104] The first time function corresponding to the length-to-thickness ratio data of the oil outlet particles is:

[0105]

[0106] The first time function corresponding to the particle width-to-thickness ratio data at the oil outlet is:

[0107]

[0108] The first time function corresponding to the equivalent spherical diameter data of the oil outlet particles is:

[0109]

[0110] The first time function corresponding to the sphericity data of the oil outlet particles is:

[0111]

[0112] For example, time t9 and t within a first preset time period can be selected. 10 The shape data of particles at the oil outlet at any given time. In the healthy phase before entering the degradation stage, the concentrations of blocky adhesive particles, sliding particles, cutting particles, oxide particles, and corrosion particles at the main bearing oil outlet are all 0. The fatigue lamellar particles can be selected at t9 and t... (The sentence is incomplete and requires further context to be translated accurately.) 10 The concentrations at times t3 and t4 are ρ3 and ρ4, respectively, where 0 ≤ t9 ≤ t4. 10 Then, the first time function corresponding to the fatigue flaky particle shape data at the oil outlet is:

[0113]

[0114] The t9 and t of the spherical particles in the first preset time period can be selected. 10 The concentrations at times t5 and t6 are ρ5 and ρ6, respectively, where 0 ≤ t9 ≤ t6. 10 The first time function corresponding to the shape data of the spherical particles at the oil outlet is:

[0115]

[0116] Step S403: Monitor and acquire the oil outlet particle data of the main bearing at the first moment after constructing the first time function corresponding to the oil outlet particle data, and obtain the first predicted value of the oil outlet particle corresponding to the first moment based on the first time function corresponding to the oil outlet particle data.

[0117] Step S404: When it is determined that the difference between the oil outlet particle data at the first moment and the predicted value of the first oil outlet particle is greater than the preset first oil outlet particle difference, the main bearing is determined to be in the degradation stage, and an alarm processing for the main bearing being in the degradation stage is executed.

[0118] In this embodiment, the oil outlet particle data of the main bearing at the first moment can be predicted based on the first time function corresponding to the constructed oil outlet particle data, i.e., the first predicted oil outlet particle value. For example, the predicted oil outlet particle concentration value ρ(T) corresponding to the first moment T can be obtained based on the first time function corresponding to the oil outlet particle concentration data constructed in the aforementioned example.

[0119] Obtain the actual oil outlet particle data of the main bearing at the first moment, such as the oil outlet particle concentration data ρ′ of the main bearing at the first moment T.

[0120] The difference between the oil outlet particle concentration data ρ′ at the first moment and the predicted value of oil outlet particle concentration ρ(T) is obtained. When it is determined that the difference is greater than the preset first oil outlet particle difference value, the main bearing is determined to be in the degradation stage, and the alarm processing of the main bearing being in the degradation stage is executed.

[0121] In one example, when the concentration of blocky adhesive particles, sliding particles, cutting particles, oxidized particles, and corrosive particles is greater than 0, the main bearing is determined to be in a degradation stage, and an alarm is triggered to indicate that the main bearing is in a degradation stage.

[0122] In this embodiment, oil outlet particle data of the main bearing is monitored and acquired. Feature values ​​of the oil outlet particle data are selected, and a time function of the oil outlet particle data is constructed based on the feature values. Based on this time function, the oil outlet particle data at a first moment is predicted. When the difference between the actual oil outlet particle data at the first moment and the predicted oil outlet particle data is greater than a preset oil outlet particle difference value, it is determined that the main bearing is in a degradation stage and an alarm is triggered. This method can more accurately determine the service stage of the main bearing based on the oil outlet particle data, further solving the problem that the inaccurate estimation of the service life of the main bearing in the prior art leads to the inability to adjust the load conditions and lubrication of the main bearing in a timely manner to ensure the timely completion of the project.

[0123] Figure 5 This is a flowchart illustrating Embodiment 4 of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application. Figures 2 to 4 Based on the illustrated embodiment, see also Figure 5 The monitoring and processing method based on the main bearing of the tunneling machine specifically includes the following steps:

[0124] Step S501: Monitor and acquire the operating data of the main bearing within a first preset time period, and obtain a first feature value based on the operating data within the first preset time period, so as to construct a first time function corresponding to the operating data based on the first feature value.

[0125] The operational data includes the vibration data of the outer ring of the main bearing. In this embodiment, the vibration data of the outer ring of the main bearing can be obtained by a vibration sensor installed on the outer ring of the main bearing. The outer ring vibration data can be the axial vibration data of the first outer ring of the main bearing, the radial vibration data of the first outer ring of the main bearing, or the axial vibration data of the second outer ring of the main bearing.

[0126] Step S502: Monitor and acquire the operating data of the main bearing at the first moment after the first time function is constructed, and obtain the first predicted value corresponding to the first moment based on the first time function.

[0127] Step S503: Obtain multiple characteristic frequencies of the main bearing based on its structural parameters.

[0128] In this embodiment, multiple characteristic frequencies of the main bearing can be obtained based on the structural parameters of the main bearing. These multiple characteristic frequencies may include: a first characteristic frequency of the main thrust roller, a second characteristic frequency of the auxiliary thrust roller, a third characteristic frequency of the radial roller, a fourth characteristic frequency of the raceway surface where the main thrust roller contacts the first outer ring, a fifth characteristic frequency of the raceway surface where the main thrust roller contacts the inner ring, a sixth characteristic frequency of the raceway surface where the auxiliary thrust roller contacts the second outer ring, a seventh characteristic frequency of the raceway surface where the auxiliary thrust roller contacts the inner ring, an eighth characteristic frequency of the raceway surface where the radial roller contacts the first outer ring, a ninth characteristic frequency of the raceway surface where the radial roller contacts the inner ring, a tenth characteristic frequency of the main thrust cage, an eleventh characteristic frequency of the auxiliary thrust cage, and a twelfth characteristic frequency of the radial cage.

[0129] For example, the main bearing rotates at a speed of n, and the inner ring rotates at a frequency of:

[0130]

[0131] The number of main rollers is q 1a The diameter is d 1a The diameter of the distribution circle is D 1a Then the first characteristic frequency of the main roller is:

[0132]

[0133] The fourth characteristic frequency of the raceway surface where the main pushing roller contacts the first outer ring is:

[0134]

[0135] The fifth characteristic frequency of the raceway surface where the main pushing roller contacts the inner ring is:

[0136]

[0137] The tenth characteristic frequency of the main cage is:

[0138]

[0139] Similarly, the second characteristic frequency f of the auxiliary thrust roller of the main bearing can also be obtained. g2a The third characteristic frequency f of the radial rollers of the main bearing g3a The sixth characteristic frequency f of the raceway surface where the auxiliary push roller contacts the second outer ring. o2a The seventh characteristic frequency f of the raceway surface where the auxiliary push roller contacts the inner ring. i2a The eighth characteristic frequency f of the raceway surface where the radial roller contacts the first outer ring. o3a The ninth characteristic frequency f of the raceway surface where the radial roller contacts the inner ring. i3a The eleventh characteristic frequency f of the auxiliary thrust cage of the main bearing b2a and the twelfth characteristic frequency f of the radial cage of the main bearing. b3a .

[0140] Step S504: When it is determined that the difference between the operating data at the first moment and the first predicted value is greater than the preset first difference, or when the outer ring vibration data is not equal to multiple characteristic frequencies of the main bearing, the main bearing is determined to be in the degradation stage, and an alarm process for the main bearing to be in the degradation stage is executed.

[0141] In this embodiment, when it is determined that the vibration data of the outer ring is not equal to multiple characteristic frequencies of the main bearing, the main bearing is determined to be in a degradation stage, and an alarm process is performed to indicate that the main bearing is in a degradation stage.

[0142] In one example, when the acquired outer ring vibration data of the main bearing shows f o =0.4×q×f n f i =0.6×q×f n f g =0.18×q×f n When the failure frequency reaches a certain level, the main bearing is determined to be in a degradation stage, and an alarm is triggered to indicate that the main bearing is in a degradation stage. Here, q represents the number of main thrust rollers, auxiliary thrust rollers, or radial rollers.

[0143] In this embodiment, the lubricating oil temperature T at the oil inlet of the main bearing can also be obtained. in (t), the temperature of the lubricating oil at the oil outlet, T out (t), obtain the amplitude of the lubricating oil temperature rise ΔT(t)=T out (t)-T in(t). The main bearing of the tunneling machine generates different amounts of frictional heat under different operating conditions, which causes different temperature rises in the lubricating oil. Let the operating temperature range of the lubricating oil be [T]. min ,T max ], then the healthy phase before entering the degradation phase must meet T min ≤T in (t)≤T out (t)≤T max Meanwhile, the amplitude of the lubricating oil temperature rise ΔT(t) = T out (t)-T in (t) must be within the specified range, i.e., ΔT(t) = T out (t)-T in (t)≤ΔT max ΔT max This represents the maximum temperature rise. The lubricating oil temperature T at the oil outlet is then determined. out (t) Exceeds the operating temperature range of the lubricating oil [T] min ,T max When, or when the amplitude of the lubricating oil temperature rise change ΔT(t) exceeds the maximum temperature rise ΔT max If the main bearing is in a degradation stage, an alarm will be triggered to indicate that the main bearing is in a degradation stage.

[0144] In this embodiment, vibration data of the outer ring of the main bearing is acquired, and multiple characteristic frequencies of the main bearing are obtained based on its structural parameters. When the vibration data of the outer ring is not equal to the multiple characteristic frequencies of the main bearing, the main bearing is determined to be in a degradation stage, and an alarm is triggered to indicate that the main bearing is in a degradation stage. This method can more accurately determine the service stage of the main bearing based on its structural parameters, further solving the problem of inaccurate estimation of the service life of the main bearing in the prior art, which leads to the inability to adjust the load conditions and lubrication of the main bearing in a timely manner to ensure the timely completion of the project.

[0145] Figure 6 This is a flowchart illustrating Embodiment 5 of a monitoring and processing method based on the main bearing of a tunneling machine provided in this application. Figures 2 to 5 Based on the illustrated embodiment, see also Figure 6 Following step S203 above, the monitoring and processing method based on the main bearing of the tunneling machine further includes the following steps:

[0146] Step S601: Monitor and acquire the operating data of the main bearing within a second preset time period, and obtain a second feature value based on the operating data within the second preset time period, so as to construct a second time function corresponding to the operating data based on the second feature value.

[0147] In this embodiment, after determining that the main bearing is in a degradation stage, operating data can be reacquired, and the operating data of the main bearing can be monitored and acquired within a second preset time period. This operating data can be the vibration data of the outer ring of the main bearing, for example... Figure 1 The axial vibration data of the first outer ring 12 of the main bearing can be monitored and acquired by multiple vibration sensors disposed along the axial direction of the first outer ring at two moments within a second preset time period.

[0148] Specifically, multiple axial vibration data of the first outer ring are acquired at each time step. Based on these data, second characteristic values ​​of the multiple axial vibration data of the first outer ring at each time step are obtained. For example, the second characteristic value can be the maximum value among the multiple axial vibration data of the first outer ring at each time step. Based on the second characteristic values ​​at these two time steps, a second time function corresponding to the axial vibration data of the first outer ring is constructed.

[0149] For example, the second preset time can be t σ1 Time to t σ3 At time, the second feature values ​​corresponding to two times within the second preset time period are obtained as A. σα1 and A σα2 Based on these two second eigenvalues, the second time function corresponding to the axial vibration data of the first outer ring is constructed as follows:

[0150]

[0151] Step S602: Monitor and acquire the operating data of the main bearing at the second moment after the second time function is constructed, and obtain the second predicted value corresponding to the second moment according to the second time function.

[0152] Step S603: When it is determined that the difference between the running data at the second moment and the second predicted value is greater than the preset second difference, the main bearing is determined to have failed, and alarm processing for the main bearing failure is executed.

[0153] In this embodiment, the operating data of the main bearing at a second moment, i.e., the second predicted value, can be predicted based on the constructed second time function. For example, the second predicted value A corresponding to the axial vibration data of the first outer ring constructed in the previous example can be obtained based on the second time function. α (T1).

[0154] Obtain the actual operating data of the main bearing at the second moment, such as the axial vibration data A′ of the first outer ring of the main bearing at the second moment T1. α1 .

[0155] Obtain the axial vibration data A′ of the first outer ring at the second moment. α1Compared with the second predicted value A α If the difference between (T1) and the preset second difference is determined, the main bearing is determined to be faulty, and an alarm is triggered for the failure of the main bearing.

[0156] In one example, after determining that the main bearing has not failed, the operating data of the main bearing can be re-monitored and acquired at preset intervals for a second preset time period, and a second feature value can be obtained based on the operating data within the second preset time period, so as to reconstruct the second time function corresponding to the operating data based on the second feature value.

[0157] In one example, the operating data of the main bearing at the second time step can be a random variable, satisfying Chebyshev's inequality:

[0158]

[0159] Where P represents probability, X represents the operating data of the main bearing, K is any positive real number, μ is the second predicted value, and σ is the standard deviation. When K is 5, the probability that any sample falls outside the 5σ interval (μ-5σ, μ+5σ) is less than 1 / 25. Therefore, when the operating data at the second moment exceeds the 5σ warning interval, it is considered that a fault has occurred and has seriously affected the normal operation of the tunneling machine's main bearing. The 5σ position is used as the failure threshold point to divide the failure stage.

[0160] In this embodiment, after determining that the main bearing is in the degradation stage, a second feature value is obtained based on the operating data of the main bearing within a second preset time period. A second time function corresponding to the operating data is constructed, and the operating data at the second moment is predicted based on the second time function. When the actual operating data at the second moment differs significantly from the predicted value, the main bearing of the tunneling machine is determined to have failed and an alarm is triggered. This allows for accurate determination of the usage stage based on the operating data of the main bearing, and timely alarms can be triggered when the main bearing fails to adjust the load conditions and lubrication of the main bearing, ensuring that the project is completed on time.

[0161] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0162] Figure 7 This application provides a schematic diagram of the structure of an embodiment of a monitoring and processing device based on the main bearing of a tunneling machine; as shown. Figure 7As shown, the monitoring and processing device 70 based on the main bearing of a tunneling machine includes an acquisition module 71 and a processing module 72. The acquisition module 71 is used to monitor and acquire the operating data of the main bearing within a first preset time period. The processing module 72 is used to acquire a first feature value based on the operating data within the first preset time period, and to construct a first time function based on the first feature value. The acquisition module 71 is also used to acquire the operating data of the main bearing at a first moment after the construction of the first time function. The processing module 72 is also used to acquire a first predicted value corresponding to the first moment based on the first time function. When it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, it is determined that the main bearing is in a degradation stage, and an alarm processing for the main bearing being in a degradation stage is executed.

[0163] The monitoring and processing device based on the main bearing of a tunneling machine provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0164] In one possible implementation, the operating data includes the outer ring vibration data of the main bearing; the acquisition module 71 is specifically used to monitor and acquire the outer ring vibration data of the main bearing at least two moments within a first preset time period by using multiple vibration sensors disposed on the outer ring of the main bearing; the processing module 72 is specifically used to acquire the first outer ring vibration characteristic value of the multiple outer ring vibration data corresponding to each moment, and construct the first time function corresponding to the outer ring vibration data based on the first outer ring vibration characteristic value corresponding to the at least two moments.

[0165] In one possible implementation, the first outer-circle vibration characteristic value of the multiple outer-circle vibration data includes one of the following: the maximum value of the multiple outer-circle vibration data, the minimum value of the multiple outer-circle vibration data, the mean value of the multiple outer-circle vibration data, and the kurtosis of the multiple outer-circle vibration data.

[0166] In one possible implementation, the vibration data of the outer ring of the main bearing includes one or more of the following: axial vibration data of the first outer ring of the main bearing, radial vibration data of the first outer ring of the main bearing, and axial vibration data of the second outer ring of the main bearing.

[0167] The monitoring and processing device based on the main bearing of a tunneling machine provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0168] In one possible implementation, the operating data includes the oil outlet particle data of the main bearing; the acquisition module 71 is specifically used to monitor and acquire the oil outlet particle data of the main bearing within a first preset time period; the processing module 72 is specifically used to select the oil outlet particle data at at least two moments within the first preset time period as the first oil outlet particle feature value, and construct the first time function corresponding to the oil outlet particle data based on the first oil outlet particle feature value.

[0169] In one possible implementation, the oil outlet particle data includes one or more of the following: size data of the oil outlet particles of the main bearing, concentration data of the oil outlet particles of the main bearing, material data of the oil outlet particles of the main bearing, and shape data of the oil outlet particles of the main bearing.

[0170] The monitoring and processing device based on the main bearing of a tunneling machine provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0171] In one possible implementation, the operating data includes the outer ring vibration data of the main bearing; the acquisition module 71 is further configured to acquire multiple characteristic frequencies of the main bearing based on the structural parameters of the main bearing; the processing module 72 is specifically configured to determine that the main bearing is in a degradation stage when the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, or when the outer ring vibration data is not equal to the multiple characteristic frequencies of the main bearing, and to perform alarm processing for the main bearing being in a degradation stage.

[0172] In one possible implementation, the main bearing's multiple characteristic frequencies include a combination of the following: a first characteristic frequency of the main thrust roller of the main bearing, a second characteristic frequency of the auxiliary thrust roller of the main bearing, a third characteristic frequency of the radial roller of the main bearing, a fourth characteristic frequency of the raceway surface of the main thrust roller in contact with the first outer ring, a fifth characteristic frequency of the raceway surface of the main thrust roller in contact with the inner ring, a sixth characteristic frequency of the raceway surface of the auxiliary thrust roller in contact with the second outer ring, a seventh characteristic frequency of the raceway surface of the auxiliary thrust roller in contact with the inner ring, an eighth characteristic frequency of the raceway surface of the radial roller in contact with the first outer ring, a ninth characteristic frequency of the raceway surface of the radial roller in contact with the inner ring, a tenth characteristic frequency of the main thrust cage of the main bearing, an eleventh characteristic frequency of the auxiliary thrust cage of the main bearing, and a twelfth characteristic frequency of the radial cage of the main bearing.

[0173] The monitoring and processing device based on the main bearing of a tunneling machine provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0174] In one possible implementation, the acquisition module 71 is further configured to monitor and acquire the operating data of the main bearing within a second preset time period after determining that the main bearing is in a degradation stage; the processing module 72 is configured to acquire a second feature value based on the operating data within the second preset time period, and construct a second time function corresponding to the operating data based on the second feature value; the acquisition module 71 is further configured to monitor and acquire the operating data of the main bearing at a second moment after constructing the second time function; the processing module 72 is further configured to acquire a second predicted value corresponding to the second moment based on the second time function; when it is determined that the difference between the operating data at the second moment and the second predicted value is greater than a preset second difference, the main bearing is determined to have failed, and an alarm processing for the main bearing failure is executed.

[0175] The monitoring and processing device based on the main bearing of a tunneling machine provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0176] Figure 8 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 8 As shown, the electronic device 80 includes: a processor 81, a memory 82, and a communication interface 83; wherein, the memory 82 is used to store executable instructions of the processor 81; the processor 81 is configured to execute the technical solutions in any of the foregoing method embodiments by executing the executable instructions.

[0177] Optionally, the memory 82 can be either standalone or integrated with the processor 81.

[0178] Optionally, when the memory 82 is a device independent of the processor 81, the electronic device 80 may further include a bus 84 for connecting the aforementioned devices.

[0179] The server is used to execute the technical solutions in any of the aforementioned method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0180] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing embodiments.

[0181] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A monitoring and processing method based on the main bearing of a tunneling machine, characterized in that, include: The system monitors and acquires the operating data of the main bearing within a first preset time period. The operating data includes oil outlet particle data of the main bearing. The oil outlet particle data includes one or more of the following combinations: size data, concentration data, material data, and shape data of the oil outlet particles of the main bearing. A first feature value is obtained based on the operating data within the first preset time period, and a first time function corresponding to the operating data is constructed based on the first feature value. This includes: monitoring and acquiring the oil outlet particle data of the main bearing within a first preset time period; selecting oil outlet particle data at least two times within the first preset time period as the first oil outlet particle feature value; and constructing a first time function corresponding to the oil outlet particle data based on the first oil outlet particle feature value. The first time function is a function of the oil outlet particle data and time, and the first time function can be obtained based on the oil outlet particle data at at least two times and the at least two times. The system monitors and acquires the operating data of the main bearing at the first moment after the first time function is constructed, and obtains the first predicted value corresponding to the first moment based on the first time function. When the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, the main bearing is determined to be in the degradation stage, and an alarm process for the main bearing to be in the degradation stage is executed.

2. The monitoring and processing method based on the main bearing of a tunneling machine according to claim 1, characterized in that, The operating data includes the vibration data of the outer ring of the main bearing; then the monitoring acquires the operating data of the main bearing within a first preset time period, and obtains a first feature value based on the operating data within the first preset time period, and constructs a first time function corresponding to the operating data based on the first feature value, including: By using multiple vibration sensors installed on the outer ring of the main bearing, vibration data of the outer ring of the main bearing at at least two moments within a first preset time period are monitored and acquired. The first outer ring vibration feature value of the multiple outer ring vibration data corresponding to each time moment is obtained respectively, and the first time function corresponding to the outer ring vibration data is constructed based on the first outer ring vibration feature value corresponding to the at least two time moments.

3. The monitoring and processing method based on the main bearing of a tunneling machine according to claim 2, characterized in that, The first outer ring vibration characteristic value of the plurality of outer ring vibration data includes one of the following: The maximum value, minimum value, mean, and kurtosis of the plurality of outer ring vibration data.

4. The monitoring and processing method based on the main bearing of a tunneling machine according to claim 2, characterized in that, The vibration data of the outer ring of the main bearing includes one or more of the following: The axial vibration data of the first outer ring of the main bearing, the radial vibration data of the first outer ring of the main bearing, and the axial vibration data of the second outer ring of the main bearing.

5. The monitoring and processing method based on the main bearing of a tunneling machine according to claim 1, characterized in that, The operating data includes the vibration data of the outer ring of the main bearing, and the method further includes: Based on the structural parameters of the main bearing, multiple characteristic frequencies of the main bearing are obtained; Then, when it is determined that the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, the main bearing is determined to be in a degradation stage, and an alarm processing for the main bearing being in a degradation stage is executed, including: When the difference between the operating data at the first moment and the first predicted value is greater than a preset first difference, or when the outer ring vibration data is not equal to multiple characteristic frequencies of the main bearing, the main bearing is determined to be in a degradation stage, and an alarm process for the main bearing to be in a degradation stage is executed.

6. The monitoring and processing method based on the main bearing of a tunneling machine according to claim 5, characterized in that, The main bearing's multiple characteristic frequencies include combinations of the following: The first characteristic frequency of the main thrust roller of the main bearing, the second characteristic frequency of the auxiliary thrust roller of the main bearing, the third characteristic frequency of the radial roller of the main bearing, the fourth characteristic frequency of the raceway surface of the main thrust roller in contact with the first outer ring, the fifth characteristic frequency of the raceway surface of the main thrust roller in contact with the inner ring, the sixth characteristic frequency of the raceway surface of the auxiliary thrust roller in contact with the second outer ring, the seventh characteristic frequency of the raceway surface of the auxiliary thrust roller in contact with the inner ring, the eighth characteristic frequency of the raceway surface of the radial roller in contact with the first outer ring, the ninth characteristic frequency of the raceway surface of the radial roller in contact with the inner ring, the tenth characteristic frequency of the main thrust cage of the main bearing, the eleventh characteristic frequency of the auxiliary thrust cage of the main bearing, and the twelfth characteristic frequency of the radial cage of the main bearing.

7. The monitoring and processing method based on the main bearing of a tunneling machine according to claim 1, characterized in that, After determining that the main bearing is in a degradation stage, the method further includes: The system monitors and acquires the operating data of the main bearing within a second preset time period, and obtains a second feature value based on the operating data within the second preset time period, so as to construct a second time function corresponding to the operating data based on the second feature value. The system monitors and acquires the operating data of the main bearing at a second moment after the second time function is constructed, and obtains the second predicted value corresponding to the second moment based on the second time function. When the difference between the operating data at the second moment and the second predicted value is greater than a preset second difference, the main bearing is determined to have failed, and an alarm process for the main bearing failure is executed.

8. A monitoring and processing device based on the main bearing of a tunneling machine, characterized in that, include: The acquisition module is used to monitor and acquire the operating data of the main bearing within a first preset time period. The operating data includes the oil outlet particle data of the main bearing. The oil outlet particle data includes one or more of the following: size data, concentration data, material data, and shape data of the oil outlet particles of the main bearing. The processing module is used to obtain a first feature value based on the operating data within the first preset time period, and to construct a first time function based on the first feature value. The processing module includes: monitoring and obtaining oil outlet particle data of the main bearing within the first preset time period; selecting oil outlet particle data at least two times within the first preset time period as the first oil outlet particle feature value; and constructing a first time function corresponding to the oil outlet particle data based on the first oil outlet particle feature value. The first time function is a function of oil outlet particle data and time. The first time function can be obtained based on the oil outlet particle data at at least two times and the at least two times. The acquisition module is also used to monitor and acquire the operating data of the main bearing at the first moment after the first time function is constructed; The processing module is further configured to obtain a first predicted value corresponding to the first moment according to the first time function; when it is determined that the difference between the running data at the first moment and the first predicted value is greater than a preset first difference, it determines that the main bearing is in the degradation stage and performs an alarm processing for the main bearing being in the degradation stage.

9. An electronic device, characterized in that, include: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the monitoring and processing method based on the main bearing of a tunneling machine as described in any one of claims 1 to 7 by executing the executable instructions.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the monitoring and processing method based on the main bearing of a tunneling machine as described in any one of claims 1 to 7.

Images