A method and device for predicting the service life of a main bearing of a heading machine and a medium

Abstract

This invention proposes a method, device, and medium for predicting the lifespan of a tunneling machine main bearing. The method collects motion state data of the tunneling machine main bearing, then constructs a health status index based on this data. According to the health status index, the operating process of the tunneling machine main bearing is divided into a degradation stage and a critical stage. Further, in the degradation stage, the total lifespan of the tunneling machine main bearing is calculated based on the motion state data and the bearing's rotational speed. In the critical stage, the remaining service life of the tunneling machine main bearing is calculated based on its failure modes. The advantages of this invention are high accuracy in predicting the lifespan of the tunneling machine main bearing, comprehensively considering parameters such as vibration signals, load, lubricating oil temperature, and particle count, achieving a comprehensive and accurate prediction of the tunneling machine main bearing's lifespan.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of main bearing test of heading machine, and particularly relates to a main bearing life prediction method, device and medium of heading machine. BACKGROUND

[0002] The heading machine is a tunnel construction equipment which is integrated with machinery, electricity, hydraulic pressure, pneumatics, guidance, sensing and information technology, can continuously work and complete construction processes such as heading, supporting and slag removal, and is praised as the "King of Construction Machinery", and can adapt to harsh working environment and has high automation and intelligence. With the rapid development of national economy, the shield construction of railway tunnel, urban subway, water conservancy project, highway tunnel, urban municipal gas pipeline project and sewage pipeline project is fully expanded, the total demand of the heading machine market shows a rapid growth trend, and the main bearing of the heading machine brings a broad market.

[0003] As a main key component of the heading machine, the cutter head plays a very important role in the tunnel construction process. The main bearing in the cutter head system is a key part for transmitting heading power and movement, and bears huge axial force, overturning moment and certain radial force in work, and the performance, service life and reliability of the main bearing directly affect the construction progress, safety and heading mileage of the heading machine. The service life of the main bearing of the heading machine is closely related to the structure design, and is closely related to the load working condition and lubrication sealing, in order to improve the reliability of the main bearing of the heading machine in the running process, the running condition of the main bearing of the heading machine should be monitored in real time, the service life of the main bearing of the heading machine should be accurately predicted, and the lubrication condition of the main bearing of the heading machine should be adjusted in time, so as to prolong the service life of the main bearing of the heading machine.

[0004] At present, the prediction of the service life of the main bearing of the heading machine is mainly based on ISO 281, and a conservative estimate of the service life of the main bearing of the heading machine can be made based on the load working condition after the main bearing of the heading machine is designed, however, this method does not consider the influence of the actual working condition on the service life of the main bearing of the heading machine. In order to further realize the prediction of the service life of the main bearing of the heading machine, the loading condition of the main bearing of the heading machine can be monitored in real time, a life prediction model can be established, the equivalent total consumed life can be calculated, and the remaining service life of the main bearing of the heading machine can be obtained, but the loading condition in the prior art does not estimate the lubrication, and then the service life of the main bearing is calculated.

[0005] In summary, there is an urgent need for a main bearing life prediction method, device and medium of heading machine to solve the problems in the prior art. SUMMARY

[0006] The present application aims to provide a main bearing life prediction method, device and medium of heading machine, and the specific technical solutions are as follows:

[0007] A kind of heading machine main bearing life prediction method, comprising:

[0008] Step S1: the motion state data of heading machine main bearing and the rotational speed of heading machine main bearing are collected;

[0009] Step S2: the health state index of heading machine main bearing is constructed based on the motion state data in step S1;

[0010] Step S3: according to the health state index in step S2, the running process of heading machine main bearing is divided into degradation stage and critical stage, the degradation stage indicates that the life of heading machine main bearing is in the stage of stable performance attenuation, and the critical stage indicates that heading machine main bearing appears failure, and the life of heading machine main bearing is in the stage of sharp performance decline;

[0011] Step S4, the life of heading machine main bearing is calculated, specifically comprising:

[0012] For degradation stage: the total life of heading machine main bearing is calculated according to the motion state data in step S1 and the rotational speed of heading machine main bearing;

[0013] For critical stage: the remaining service life of heading machine main bearing is calculated.

[0014] Preferably, in step S1, the motion state data includes vibration signal set, load set, lubricating oil temperature set and particle number set, the vibration signal set includes axial vibration signal and radial vibration signal, and the load set includes main thrust load, auxiliary thrust load and radial load.

[0015] Preferably, in step S2, the health state index is constructed by vibration signal set and load set, when the motion state data of heading machine main bearing meets the health state index, the heading machine main bearing is in healthy state, and the health state index is as follows:

[0016] When heading machine main bearing is in healthy state, T A1 ∈[a1,a2]、T A2 ∈[a3,a4]、T A3 ∈[a5,a6]、T A4 ∈[a7,a8]、T R1 ∈[r1,r2]、T R2 ∈[r3,r4]、T R3 ∈[r5,r6]、T R4 ∈[r7,r8] and σ 1amax 、σ 1bmax 、σ 1cmax Less than allowable contact stress [σ];

[0017] Wherein, T A1represents the maximum value of the axial vibration signal of the main bearing of the heading machine at time t, a1 and a2 are upper and lower limits determined according to T A1(t+Δt) A1(t+Δt) represents the maximum value prediction of the axial vibration signal of the main bearing of the heading machine at time t+Δt;

[0018] T A2 represents the minimum value of the axial vibration signal of the main bearing of the heading machine at time t, a3 and a4 are upper and lower limits determined according to T A2(t+Δt) A2(t+Δt) represents the minimum value prediction of the axial vibration signal of the main bearing of the heading machine at time t+Δt;

[0019] T A3 represents the mean value of the axial vibration signal of the main bearing of the heading machine at time t, a5 and a6 are upper and lower limits determined according to T A3(t+Δt) A3(t+Δt) represents the mean value prediction of the axial vibration signal of the main bearing of the heading machine at time t+Δt;

[0020] T A4 represents the root mean square value of the axial vibration signal of the main bearing of the heading machine at time t, a7 and a8 are upper and lower limits determined according to T A4(t+Δt) A4(t+Δt) represents the root mean square value prediction of the axial vibration signal of the main bearing of the heading machine at time t+Δt;

[0021] T R1 represents the maximum value of the radial vibration signal of the main bearing of the heading machine at time t, r1 and r2 are upper and lower limits determined according to T R1(t+Δt) R1(t+Δt) represents the maximum value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;

[0022] T R2 represents the minimum value of the radial vibration signal of the main bearing of the heading machine at time t, r3 and r4 are upper and lower limits determined according to T R2(t+Δt) R2(t+Δt) represents the minimum value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;

[0023] T R3 represents the mean value of the radial vibration signal of the main bearing of the heading machine at time t, r5 and r6 are upper and lower limits determined according to T R3(t+Δt) R3(t+Δt) represents the mean value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;

[0024] T R4 represents the root mean square value of the radial vibration signal of the main bearing of the heading machine at time t, r7 and r8 are upper and lower limits determined according to T R4(t+Δt) R4(t+Δt) ​​​​​​​​A root mean square value prediction of a radial vibration signal of the main bearing of the roadheader at a time t+Δt;

[0025] σ 1amax 、σ 1bmax 、σ 1cmax respectively represent the main thrust raceway surface contact stress, the radial raceway surface contact stress, and the auxiliary thrust raceway surface contact stress.

[0026] Preferably, in step S3, the specific process of dividing the running process of the main bearing of the roadheader into the degradation stage and the critical stage is as follows:

[0027] The main bearing of the roadheader in the degradation stage satisfies T' A1 ∈[a1,a′1]、T′ A2 ∈[a3,a′3]、T′ A3 ∈[a5,a′5]、T′ A4 ∈[a7,a′7]、T′ R1 ∈[r1,r′2]、T′ R2 ∈[r3,r′4]、T′ R3 ∈[r5,r′5]、T′ R4 ∈[r7,r′7]、W′∈[W min ,W mid ]、p′∈[p min ,p mid ];

[0028] The main bearing of the roadheader in the critical stage satisfies T'' A1 ∈[a′1,a2]、T″ A2 ∈[a′3,a4]、T″ A3 ∈[a′5,a6]、T″ A4 ∈[a′7,a8]、T″ R1 ∈[a′1,a2]、T″ R2 ∈[a′3,a4]、T″ R3 ∈[a′5,a6]、T″ R4 ∈[a′7,a8]、W″∈[W mid ,W max ]、p″∈[p mid ,p max ];

[0029] Wherein, a2-a′1≥a′1-a1、a4-a′3≥a′3-a3、a6-a′5≥a′5-a5、a8-a′7≥a′7-a7、W max -W mid ≥W mid -W min 、p max -p mid ≤pmid -p min , T' A1 , T' A2 , T' A3 , T' A4 respectively represent the maximum value, the minimum value, the mean value and the root mean square value of the axial vibration signal in the degradation stage, T' R1 , T' R2 , T' R3 , T' R4 respectively represent the maximum value, the minimum value, the mean value and the root mean square value of the radial vibration signal in the degradation stage, T" A1 , T" A2 , T" A3 , T" A4 respectively represent the maximum value, the minimum value, the mean value and the root mean square value of the axial vibration signal in the critical stage, T" R1 , T" R2 , T" R3 , T" R4 respectively represent the maximum value, the minimum value, the mean value and the root mean square value of the radial vibration signal in the critical stage, W' represents the temperature of the lubricating oil discharged by the main bearing of the tunneling machine in the degradation stage, and p' represents the granularity of the lubricating oil discharged by the main bearing of the tunneling machine in the degradation stage, the granularity being the number of particles per liter of lubricating oil.

[0030] Preferably, in the degradation stage in step S4, the process of calculating the total service life of the main bearing of the tunneling machine is as follows:

[0031] obtaining the basic rated dynamic load of the main push roller group, the auxiliary push roller group and the radial roller group of the main bearing of the tunneling machine, then bringing the data in the load set into the load distribution relationship to calculate the equivalent dynamic load of the main push roller group, the auxiliary push roller group and the radial roller group respectively, and calculating the basic rated life of the main bearing of the tunneling machine based on the basic rated dynamic load and the equivalent dynamic load, and further calculating the total service life of the main bearing of the tunneling machine based on the probability product formula.

[0032] Preferably, in the critical stage in step S4, the process of calculating the remaining service life of the main bearing of the tunneling machine is as follows:

[0033] determining the failure mode of the main bearing of the tunneling machine, the defect size corresponding to the failure mode and the defect frequency, establishing a relationship between the defect size and the defect frequency, setting the defect size when the main bearing of the tunneling machine fails as a defect size threshold, and obtaining the remaining service life of the main bearing of the tunneling machine based on the defect size threshold and the relationship between the defect size and the defect frequency.

[0034] In addition, the present application also provides a main bearing life prediction device of a tunneling machine, comprising:

[0035] a memory for storing a computer program;

[0036] A processor is configured to implement the tunneling machine main bearing life prediction method as described above when executing the computer program.

[0037] In addition, the application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the tunneling machine main bearing life prediction method as described above.

[0038] The technical scheme of the application has the following beneficial effects:

[0039] (1) The tunneling machine main bearing life prediction method provided by the application constructs the health state index of the tunneling machine main bearing by collecting the vibration signal, the load condition, the lubricating oil temperature and the granularity, accurately monitors the health state of the tunneling machine main bearing, and is beneficial to the life prediction of the tunneling machine main bearing; further, the relationship function between the time period △t and the corresponding vibration characteristic value △T is established, the normal change trend of the characteristic value of the tunneling machine main bearing vibration can be predicted in real time, and the accuracy of the tunneling machine main bearing life prediction is improved; in addition, the whole life of the tunneling machine main bearing is divided into two different performance degradation stages, namely the degradation stage and the critical stage, the service life of the tunneling machine main bearing is predicted according to the situation, and the comprehensive and accurate prediction of the service life of the tunneling machine main bearing is realized.

[0040] (2) The tunneling machine main bearing life prediction method provided by the application improves the accuracy of the tunneling machine main bearing life prediction by collecting the axial and radial vibration signals of the tunneling machine, the lubricating oil temperature and the granularity, comprehensively considering the actual working condition of the tunneling machine main bearing from the above parameters; the change trend of the vibration signal is better predicted by selecting the maximum value, the minimum value, the mean value and the root mean square value of the vibration signal as the characteristic parameters, and the life prediction of the tunneling machine main bearing is facilitated.

[0041] In addition to the objects, features and advantages described above, the application has other objects, features and advantages. The application will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0042] The drawings that form a part of this application provide further understanding of the application, and the illustrative embodiments of the application and their description serve to explain the application. The drawings do not constitute an improper limitation of the application. In the drawings:

[0043] Figure 1 is a step flow chart of the tunneling machine main bearing life prediction method;

[0044] Figure 2 is a structural schematic view of the tunneling machine main bearing;

[0045] Figure 3 This is a schematic diagram of the sensor arrangement on the main bearing of a tunneling machine;

[0046] Figure 4 This is a schematic diagram showing the arrangement of force sensors on the main thrust raceway surface of the main bearing of a tunneling machine;

[0047] Figure 5 This is a schematic diagram showing the arrangement of force sensors on the auxiliary pusher raceway surface of the main bearing of a tunneling machine.

[0048] Wherein, 1-outer ring, 2-first inner ring, 3-second inner ring, 4-main push roller, 5-auxiliary push roller, 6-radial roller, 7-main push cage, 8-auxiliary push cage, 9-radial cage, 10-floating ring, 11-disc spring assembly, A-axial arrangement surface of vibration sensor, B-radial arrangement surface of vibration sensor, 1a-main push raceway surface, 1b-radial raceway surface, 1c-auxiliary push raceway surface, 1a1-main push upper force sensor, 1a2-main push left force sensor, 1a3-main push lower force sensor, 1a4-main push right force sensor, 1c1-auxiliary push upper force sensor, 1c2-auxiliary push left force sensor, 1c3-auxiliary push lower force sensor, 1c4-auxiliary push right force sensor. Detailed Implementation

[0049] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0050] like Figure 2 The schematic diagram of the main bearing structure of a tunneling machine shows that the main bearing mainly consists of raceways, rollers, a cage, floating rings, and disc spring assemblies. The rollers roll within the cage pockets, while the cage slides within the raceways. The disc spring assemblies and floating rings provide preload to the main bearing. During normal operation, there is contact between the rollers and the cage / raceways, as well as between the cage and the raceways. The contact area between the rollers and raceways experiences significant load, and the rolling motion causes changes in the microstructure, leading to fatigue cracks and raceway spalling. Damage to the main bearing seals and the entry of impurities also accelerates wear, resulting in lubrication failure.

[0051] Currently, the prediction of the service life of tunneling machine main bearings is mainly based on ISO 281. However, this method does not consider the impact of actual working conditions on the service life of the tunneling machine main bearings. To further improve the prediction of the service life of tunneling machine main bearings, existing technologies have proposed algorithms that can monitor the load conditions of the tunneling machine main bearings in real time and calculate their service life. However, these algorithms do not consider the impact of lubrication and other factors on service life. Therefore, this invention proposes a method, device, and medium for predicting the service life of tunneling machine main bearings, which can accurately predict the service life of tunneling machine main bearings based on real-time data.

[0052] Embodiment 1

[0053] Referring to Figure 1 The embodiment discloses a tunneling machine main bearing life prediction method, and steps are as follows:

[0054] Step S1: collect motion state data of the tunneling machine main bearing.

[0055] Step S2: construct a health state index of the tunneling machine main bearing based on the motion state data in step S1.

[0056] Step S3: divide the running process of the tunneling machine main bearing into a degradation stage and a critical stage according to the health state index in step S2; it should be noted that the degradation stage indicates that the life of the tunneling machine main bearing is in a stable performance degradation stage, and the life calculation of the tunneling machine main bearing in this stage can be performed through state monitoring and theoretical formula; the critical stage indicates that the tunneling machine main bearing appears to fail, and the life of the tunneling machine main bearing is in a sharp decline stage, and the life calculation of the tunneling machine main bearing in this stage needs to be performed through state monitoring and constructing a relationship.

[0057] Step S4: calculate the life of the tunneling machine main bearing, specifically including:

[0058] Step S4.1: in the degradation stage, calculate the total life of the tunneling machine main bearing according to the motion state data in step S1 and the rotating speed of the tunneling machine main bearing;

[0059] Step S4.2: in the critical stage, calculate the remaining service life of the tunneling machine main bearing according to the failure mode of the tunneling machine main bearing.

[0060] Specifically, in step S1, the motion state data includes a vibration signal set, a load set, a lubricating oil temperature set and a particle number set, the vibration signal set includes axial vibration signals and radial vibration signals, and the load set includes main thrust load, auxiliary thrust load and radial load.

[0061] It should be noted that, in order to monitor the health state of the tunneling machine main bearing, first, the vibration, load, lubricating oil temperature and particle size of the parts of the tunneling machine main bearing are collected. Referring to Figure 3 , Figure 4 and Figure 5In the vibration sensor axial arrangement surface A of the outer ring 1, the vibration sensors are evenly arranged m, in the vibration sensor radial arrangement surface B of the outer ring 1, the vibration sensors are evenly arranged n, and the vibration signals of the main bearing of the heading machine in the axial and radial directions are collected. In addition, the main push upper force sensor 1a1, the main push left force sensor 1a2, the main push lower force sensor 1a3 and the main push right force sensor 1a4 are arranged in the upper and lower and left and right four directions of the main push raceway surface 1a, and the load distribution of the main push raceway surface 1a in the four directions is measured; the force sensor is arranged on the radial raceway surface 1b to measure the maximum load on the radial raceway surface 1b; the auxiliary push upper force sensor 1c1, the auxiliary push left force sensor 1c2, the auxiliary push lower force sensor 1c3 and the auxiliary push right force sensor 1c4 are arranged in the upper and lower and left and right four directions of the auxiliary push raceway surface 1c, and the load distribution of the auxiliary push raceway surface 1c in the four directions is measured. Finally, the temperature sensor and the oil particle counter are arranged at the lubricating oil discharge port of the main bearing of the heading machine, which is used to monitor the temperature change and particle size change of the lubricating oil.

[0062] Specifically, in step S2, for the data collected by the sensor, signal processing technology and artificial intelligence technology are needed to process and construct the health state index of the main bearing of the heading machine. Let Z be the amplitude of the vibration signal at time t after noise reduction processing, and z(t) be the functional relationship between amplitude Z and time t, then Z=z(t). The maximum value, minimum value, mean value and root mean square value of the vibration signal are selected as characteristic parameters, which are as follows:

[0063] ① Maximum value:

[0064] T1=max(Z)=max[z(t)];

[0065] ② Minimum value:

[0066] T2=min(Z)=min[z(t)];

[0067] ③ Mean value:

[0068]

[0069] ④ Root mean square value:

[0070]

[0071] Wherein, Q is the number of collected signals.

[0072] Further, the characteristic values representing the axial vibration of the main bearing of the heading machine are:

[0073] ① Maximum value:

[0074]

[0075] ② Minimum value:

[0076]

[0077] ③Mean value:

[0078]

[0079] ④Root mean square value:

[0080]

[0081] Further, the relationship between the time period△t and its corresponding characteristic value△TA1,△TA2,△TA3 and△TA4 of the axial vibration of the main bearing of the heading machine is established, then△TA1=x(△t),△TA2=y(△t),△TA3=g(△t) and△TA4=f(△t), wherein x, y, g and f respectively represent the corresponding relationship functions. Then it can be inferred that the corresponding characteristic value of the axial vibration of the main bearing of the heading machine near the time t+△t is:

[0082] ①Maximum value:

[0083]

[0084] ②Minimum value:

[0085]

[0086] ③Mean value:

[0087]

[0088] ④Root mean square value:

[0089]

[0090] It should be noted that the characteristic value calculation of the radial vibration in the embodiment is the same as the characteristic value calculation of the axial vibration, and the characteristic value calculation of the radial vibration can be performed by referring to the characteristic value calculation of the axial vibration, which will not be described here.

[0091] Further, the load measured by the main push upper force sensor 1a1 is F 1a1 , the load measured by the main push left force sensor 1a2 is F 1a2 , the load measured by the main push lower force sensor 1a3 is F 1a3 , and the load measured by the main push right force sensor 1a4 is F 1a4 , then according to the distribution circle diameter D 1a , the number q 1a , F 1a1 , F 1a2 , F 1a3 and F 1a4The load distribution X of the entire main thrust raceway surface 1a is fitted 1a = X (D 1a , q 1a , F 1a1 , F 1a2 , F 1a3 , F 1a4 ) ; the maximum load on the radial raceway surface 1b is F 1b1 , the distribution circle diameter D 1b , the number q 1b and F 1b1 of the radial rollers 6 are fitted to the load distribution X of the entire radial raceway surface 1b 1b = X (D 1b , q 1b , F 1b1 ) ; the load size measured by the auxiliary thrust left force sensor 1a2 is F 1c1 , the load size measured by the auxiliary thrust right force sensor 1a4 is F 1c2 , the load size measured by the auxiliary thrust lower force sensor 1a3 is F 1c3 , the load size measured by the auxiliary thrust right force sensor 1a4 is F 1c4 , the distribution circle diameter D 1c , the number q 1c , F 1c1 , F 1c2 , F 1c3 and F 1c4 of the auxiliary thrust rollers 4 are fitted to the load distribution X of the entire auxiliary thrust raceway surface 1c 1c = X (D 1c , q 1ac , F 1ac1 , F 1ac2 , F 1ac3 , F 1ac4 ). The maximum loads of the main thrust raceway surface 1a, the radial raceway surface 1b and the auxiliary thrust raceway surface 1c are F 1amax , F 1bmax and F 1cmax respectively according to the load distribution, and the contact stresses σ 1amax , σ 1bmax and σ 1cmax are respectively solved by the Hertz formula.

[0092] It should be noted that the preferred algorithm for fitting the load distribution in the embodiment is the Newton-Raphson method, and the preferred allowable contact stress in the embodiment is 4000 MPa.

[0093] When the motion state data of the main bearing of the heading machine meets the health state index, the main bearing of the heading machine is in a healthy state, and the health state index is as follows:

[0094] When the main bearing of the tunneling machine is in good condition, it must meet the T requirement. A1 ∈[a1,a2]、T A2 ∈[a3,a4]、T A3 ∈[a5,a6]、T A4 ∈[a7,a8]、T R1 ∈[r1,r2]、T R2 ∈[r3,r4]、T R3 ∈[r5,r6]、T R4 ∈[r7,r8] and σ 1amax σ 1bmax σ 1cmax All are less than the allowable contact stress [σ];

[0095] Among them, T A1 This represents the maximum value of the axial vibration signal of the main bearing of the tunneling machine at time t, where a1 and a2 are based on T. A1(t+Δt) Specific upper and lower limits, T A1(t+Δt) This represents the predicted maximum value of the axial vibration signal of the tunneling machine's main bearing at time t+Δt.

[0096] T A2 This represents the minimum value of the axial vibration signal of the main bearing of the tunneling machine at time t. a3 and a4 are values ​​based on T. A2(t+Δt) Specific upper and lower limits, T A2(t+Δt) This represents the minimum predicted value of the axial vibration signal of the tunneling machine's main bearing at time t+Δt;

[0097] T A3 This represents the mean axial vibration signal of the tunneling machine's main bearing at time t. a5 and a6 are values ​​based on T. A3(t+Δt) Specific upper and lower limits, T A3(t+Δt) This represents the mean predicted value of the axial vibration signal of the main bearing of the tunneling machine at time t+Δt.

[0098] T A4 This represents the root mean square value of the axial vibration signal of the tunneling machine's main bearing at time t. a7 and a8 are values ​​based on T. A4(t+Δt) Specific upper and lower limits, T A4(t+Δt) This represents the predicted root mean square value of the axial vibration signal of the main bearing of the tunneling machine at time t+Δt.

[0099] T R1 This represents the maximum value of the radial vibration signal of the tunneling machine's main bearing at time t, where r1 and r2 are based on T. R1(t+Δt) Specific upper and lower limits, T R1(t+Δt) This represents the predicted maximum value of the radial vibration signal of the tunneling machine's main bearing at time t+Δt.

[0100] T R2 This represents the minimum radial vibration signal of the tunneling machine's main bearing at time t, where r3 and r4 are based on T.R2(t+Δt) determined upper and lower limits, T R2(t+Δt) denotes a minimum value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;

[0101] T R3 denotes the mean value of the radial vibration signal of the main bearing of the heading machine at time t, r5, r6 are determined according to T R3(t+Δt) determined upper and lower limits, T R3(t+Δt) denotes a mean value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;

[0102] T R4 denotes the root mean square value of the radial vibration signal of the main bearing of the heading machine at time t, r7, r8 are determined according to T R4(t+Δt) determined upper and lower limits, T R4(t+Δt) denotes a root mean square value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;

[0103] σ 1amax , σ 1bmax , σ 1cmax respectively denote the main thrust raceway surface contact stress, the radial raceway surface contact stress, and the auxiliary thrust raceway surface contact stress.

[0104] Specifically, in step S3, the specific process of dividing the running process of the main bearing of the heading machine into the degradation stage and the critical stage is as follows:

[0105] The main bearing of the heading machine in the degradation stage satisfies T' A1 ∈[a1,a'1], T' A2 ∈[a3,a'3], T' A3 ∈[a5,a'5], T' A4 ∈[a7,a'7], T' R1 ∈[r1,r'2], T' R2 ∈[r3,r'4], T' R3 ∈[r5,r'5], T' R4 ∈[r7,r'7], W'∈[W min , W mid ], p'∈[p min , p mid ];

[0106] The main bearing of the heading machine in the critical stage satisfies T" A1 ∈[a'1,a2], T" A2 ∈[a'3,a4], T" A3 ∈[a'5,a6], T" A4 ∈[a'7,a8], T" R1 ∈[a'1,a2], T" R2 ∈[a'3,a4], T"R3 ∈[a′5,a6], T″ R4 ∈[a′7,a8], W″∈[W mid ,W max ], p″∈[p mid ,p max ] ;

[0107] Wherein, a2-a′1≥a′1-a1, a4-a′3≥a′3-a3, a6-a′5≥a′5-a5, a8-a′7≥a′7-a7, W max -W mid ≥W mid -W min , p max -p mid ≤p mid -p min , T′ A1 , T′ A2 , T′ A3 , T′ A4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the axial vibration signal in the degradation stage, T′ R1 , T′ R2 , T′ R3 , T′ R4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the radial vibration signal in the degradation stage, T″ A1 , T″ A2 , T″ A3 , T″ A4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the axial vibration signal in the critical stage, T″ R1 , T″ R2 , T″ R3 , T″ R4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the radial vibration signal in the critical stage, W′ represents the temperature of the lubricating oil discharged by the main bearing of the roadheader in the degradation stage, p′ represents the granularity of the lubricating oil discharged by the main bearing of the roadheader in the degradation stage, and the granularity is the number of particles per liter of lubricating oil.

[0108] Specifically, in step S4.1, the process of calculating the total life of the main bearing of the roadheader is as follows:

[0109] According to the rated load coefficient b m of the modern commonly used material, the coefficient f e related to the bearing part geometry, manufacturing precision and material, the number of rollers q, the roller length L and the roller diameter d, the basic rated dynamic load C a1 , C a2 and C rAccording to the load distribution relationship X 1a = X(D 1a , q 1a , F 1a1 , F 1a2 , F 1a3 , F 1a4 ), X 1b = X(D 1b , q 1b , F 1b1 ) and X 1c = X(D 1c , q 1ac , F 1ac1 , F 1ac2 , F 1ac3 , F 1ac4 ), the equivalent dynamic load Q e1 , Q e2 and Q e3 of the main roller group, the auxiliary roller group and the radial roller group are obtained, respectively. Finally, the basic rated life L 10 of the main roller group, the auxiliary roller group and the radial roller group is obtained according to the basic rated dynamic load and the equivalent dynamic load, and the total life of the main bearing of the roadheader is further obtained by the probability product formula. The calculation of the basic rated life L 10 needs to consider the life correction coefficient a related to the characteristics of the lubricating oil and the rotating speed. In order to realize the prediction of the service life of the main bearing of the roadheader, first, the service life L h of the main bearing of the roadheader under the initial working condition and the lubricating condition is obtained, and then the load distribution, the rotating speed and the lubricating oil temperature and other data of the actual working condition are collected in real time, and based on the Palmgren-Miner rule:

[0110]

[0111] the remaining service life L' h of the main bearing of the roadheader can be predicted. Wherein, η represents a group of operating conditions of the bearing, each independent condition s has a possible fatigue life L i , the roadheader bearing operates N i rotations under this condition, N i < L i .

[0112] Specifically, in step S4.2, the process of calculating the remaining service life of the main bearing of the roadheader is as follows:

[0113] Firstly, the failure mode of the main bearing of the roadheader should be determined, and the defect position and initial size of the parts such as the ring, roller or retainer are further determined. The failure modes of the main bearing of the roadheader mainly include ring surface cracking and metal spalling, roller surface cracking and metal spalling and retainer partition cracking, and each failure mode has a corresponding failure frequency, whether in the early stage or the later stage of the failure of the main bearing of the roadheader. Assuming that the rotation speed of the main bearing of the roadheader is n, the rotation frequency is:

[0114] γ n = n / 60;

[0115] Assuming that the number of the main thrust rollers 4 is q 1a , the diameter of the main thrust rollers 4 is d 1a , and the distribution circle diameter of the main thrust rollers 4 is D 1a , the failure frequency of the raceway surface of the outer ring 1 in contact with the main thrust rollers 4 can be expressed as:

[0116]

[0117] The failure frequency of the raceway surface of the first inner ring 2 in contact with the main thrust rollers 4 can be expressed as:

[0118]

[0119] The failure frequency of the main thrust rollers 4 can be expressed as:

[0120]

[0121] The failure frequency of the main thrust retainer 7 can be expressed as:

[0122]

[0123] Similarly, the related failure frequencies of the auxiliary thrust roller group and the radial roller group can be obtained: γ o2a , γ i2a , γ g2a , γ b2a , γ o3a , γ i3a , γ g3a and γ b3a . The defect frequency representing the defect size is obtained by signal decomposition of the failure frequency: γ' oλa , γ' iλa , γ' gλa and γ' bλa (λ = 1, 2, 3), which represent the defect sizes S oλa , S iλa , S gλa and S bλa (λ = 1, 2, 3) respectively. The relationship between the defect size and the defect frequency is established: S oλa = ζoλa (t)γ' oλa ,γ' iλa =ζ iλa (t)γ' iλa ,γ' gλa =ζ gλa (t)γ' gλa and γ' bλa =ζ bλa (t)γ' bλa , where ζ oλa (t), ζ iλa (t), ζ gλa (t) and ζ bλa (t) is the correlation coefficient between defect size and defect frequency, characterizing the defect propagation rate, and is a function of time t. Let the threshold values ​​for defect size be S... oλa-max S iλa-max S gλa-max and S bλa-max Then the time t for the defect to expand to the threshold can be obtained. oλa t iλa t gλa and t bλa The remaining service life of the tunneling machine's main bearing is L. S =min[t oλa ,t iλa ,t gλa ,t bλa ].

[0124] In addition, this embodiment also proposes a device for predicting the life of a tunneling machine main bearing, comprising:

[0125] Memory, used to store computer programs;

[0126] A processor is used to execute the computer program to implement the tunneling machine main bearing life prediction method as described above.

[0127] In addition, this embodiment also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for predicting the life of the main bearing of a tunneling machine.

[0128] The specific calculation example for calculating the life of the main bearing of a tunneling machine with an outer diameter of 5m using this embodiment is as follows:

[0129] (1) In the degradation stage:

[0130] ①Based on the rated load factor b of commonly used modern materials m The coefficient f is related to the geometry, manufacturing precision, and materials of the bearing parts. e, the number of rollers q, the roller length L and the roller diameter d, respectively, to obtain the basic dynamic load rating C of the main roller set, the auxiliary roller set and the radial roller set a1 , C a2 and C r .

[0131] The basic dynamic load rating C of the main roller set a1 :

[0132]

[0133] C a1 = 1 x 141.7980 x 110 7 / 9 x 66 3 / 4 x 100 29 / 27 = 1.7874 x 10 7 N;

[0134] The basic dynamic load rating C of the auxiliary roller set a2 :

[0135]

[0136] C a2 = 1 x 117.3000 x 45 7 / 9 x 144 3 / 4 x 45 29 / 27 = 5.6179 x 10 6 N;

[0137] The basic dynamic load rating C of the radial roller set r :

[0138]

[0139] C r = 1.1 x 55.9280 x 65 7 / 9 x 170 3 / 4 x 40 29 / 27 = 3.9141 x 10 6 N;

[0140] ② According to the load distribution relationship X 1a = X(D 1a , q 1a , F 1a1 , F 1a2 , F 1a3 , F 1a4 ), X 1c = X(D 1c , q 1ac , F 1ac1 , F 1ac2 , F 1ac3 , F 1ac4 ) and X1b = X(D 1b , q 1b , F 1b1 ) respectively obtain the equivalent dynamic load Q e1 , Q e2 and Q e3 of the main roller group, the auxiliary roller group and the radial roller group.

[0141] Equivalent dynamic load Q e1 of the main roller group:

[0142]

[0143] Equivalent dynamic load Q e2 of the auxiliary roller group:

[0144]

[0145] Equivalent dynamic load Q e3 of the auxiliary roller group:

[0146]

[0147] ③According to the basic rated dynamic load and the equivalent dynamic load, L 10 life of the main roller group, the auxiliary roller group and the radial roller group are obtained respectively, and the total life of the main bearing of the roadheader is further obtained by the probability product formula.

[0148] L 10 life of the main roller group:

[0149]

[0150] L 10 life of the auxiliary roller group:

[0151]

[0152] L 10 life of the radial roller group:

[0153]

[0154] The total life of the main bearing of the roadheader is 12394h.

[0155] (2) In the critical stage:

[0156] γ o1a , γ i1a , γ g1a , γ b1a , γ o2a , γ i2a , γ g2a , γ b2a , γo3a , γ i3a , γ g3a , and γ b3a are 72.761 Hz, 49.795 Hz, 47.612 Hz, 2.969 Hz, 71.761 Hz, 48.795 Hz, 46.612 Hz, 2.469 Hz, 70.761 Hz, 47.795 Hz, 45.612 Hz, 1.969 Hz, respectively.

[0157] Signal decomposition of the fault frequency obtains the defect frequency γ' oλa , γ' iλa , γ' gλa , and γ' bλa (λ = 1, 2, 3): 73.761 Hz, 50.795 Hz, 48.612 Hz, 3.969 Hz, 72.761 Hz, 49.795 Hz, 47.612 Hz, 3.469 Hz, 71.761 Hz, 48.795 Hz, 46.612 Hz, 2.969 Hz.

[0158] The defect sizes represented by them are S oλa , S iλa , S gλa , and S bλa (λ = 1, 2, 3): 225.5003 mm 2 , 223.6132 mm 2 , 20.6542 mm 2 , 5.6231 mm 2 , 222.5003 mm 2 , 220.6132 mm 2 , 18.6542 mm 2 , 4.6231 mm 2 , 220.5003 mm 2 , 218.6132 mm 2 , 16.6542 mm 2 , 3.6231 mm 2 .

[0159] The relationship between the defect size and the defect frequency is established: S oλa = ζ oλa (t)γ' oλa , γ' iλa = ζ iλa (t)γ' iλa , γ' gλa = ζ gλa (t)γ' gλa , and γ' bλa = ζ bλa (t)γ'bλa wherein ζ oλa (t), ζ iλa (t), ζ gλa (t) and ζ bλa (t) are the correlation coefficients of the defect size and the defect frequency, and the threshold values of the defect size are S oλa-max , S iλa-max , S gλa-max and S bλa-max , respectively, then the time t oλa = 200h, t iλa = 300h, t gλa = 400h and t bλa = 500h, the remaining service life of the main bearing of the roadheader is L S = min[t oλa , t iλa , t gλa , t bλa ] = 200h.

[0160] The preferred embodiments of the present application have been described above by way of example only, and it should be appreciated that modifications and variations of the present application are possible without departing from the spirit and scope of the application, which are defined in the appended claims. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for predicting the service life of a main bearing of a heading machine, characterized in that The method comprises the following steps: Step S1: collecting motion state data of the main bearing of the tunneling machine and a rotation speed of the main bearing of the tunneling machine; Step S2: constructing a health state index of the main bearing of the tunneling machine based on the motion state data in step S1; Step S3: dividing a running process of the main bearing of the tunneling machine into a degradation stage and a critical stage according to the health state index in step S2, wherein the degradation stage represents that a service life of the main bearing of the tunneling machine is in a stage of stable performance attenuation, and the critical stage represents that the main bearing of the tunneling machine appears failure and the service life of the main bearing of the tunneling machine is in a stage of sharp performance decline; Step S4: calculating the service life of the main bearing of the tunneling machine, specifically comprising: for the degradation stage: calculating a total service life of the main bearing of the tunneling machine according to the motion state data in step S1 and the rotation speed of the main bearing of the tunneling machine; for the critical stage: calculating a remaining service life of the main bearing of the tunneling machine; In step S3, the specific process of dividing the running process of the main bearing of the tunneling machine into the degradation stage and the critical stage is as follows: The main bearing of the heading machine in the degradation phase satisfies T' A1 ∈ [a1, a'1], T' A2 ∈ [a3, a'3], T' A3 ∈ [a5, a'5], T' A4 ∈ [a7, a'7], T' R1 ∈ [r1, r'2], T' R2 ∈ [r3, r'4], T' R3 ∈ [r5, r'5], T' R4 ∈ [r7, r'7], W' ∈ [W min , W mid ], p' ∈ [p min , p mid ]; The main bearing of the heading machine in the critical stage satisfies T" A1 ∈[a′1,a2]、T″ A2 ∈[a′3,a4]、T″ A3 ∈[a′5,a6]、T″ A4 ∈[a′7,a8]、T″ R1 ∈[a′1,a2]、T″ R2 ∈[a′3,a4]、T″ R3 ∈[a′5,a6]、T″ R4 ∈[a′7,a8]、W″∈[W mid ,W max ]、p″∈[p mid ,p max ]; wherein a2-a'1≥a'1-a1, a4-a'3≥a'3-a3, a6-a'5≥a'5-a5, a8-a'7≥a'7-a7, W max -W mid ≥W mid -W min , p max -p mid ≤p mid -p min , T' A1 , T' A2 , T' A3 , T' A4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the axial vibration signal in the degradation stage, T' R1 , T' R2 , T' R3 , T' R4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the radial vibration signal in the degradation stage, T" A1 , T" A2 , T" A3 , T" A4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the axial vibration signal in the critical stage, T" R1 , T" R2 , T" R3 , T" R4 respectively represent the maximum value, the minimum value, the mean value, the root mean square value of the radial vibration signal in the critical stage, W' represents the temperature of the lubricating oil discharged by the main bearing of the roadheader in the degradation stage, p' represents the granularity of the lubricating oil discharged by the main bearing of the roadheader in the degradation stage, and the granularity is the number of particles per liter of lubricating oil.

2. The main bearing life prediction method of a tunneling machine according to claim 1, characterized by, In step S1, the motion state data comprises a vibration signal set, a load set, a lubricating oil temperature set and a particle number set, the vibration signal set comprises an axial vibration signal and a radial vibration signal, and the load set comprises a main thrust load, an auxiliary thrust load and a radial load.

3. The main bearing life prediction method of a tunneling machine according to claim 2, characterized by, In step S2, the health state index is constructed by the vibration signal set and the load set, and when the motion state data of the main bearing of the tunneling machine meets the health state index, the main bearing of the tunneling machine is in a healthy state, and the health state index is as follows: When the main bearing of the tunneling machine is in good condition, it must meet the T requirement. A1 ∈[a1,a2]、T A2 ∈[a3,a4]、T A3 ∈[a5,a6]、T A4 ∈[a7,a8]、T R1 ∈[r1,r2]、T R2 ∈[r3,r4]、T R3 ∈[r5,r6]、T R4 ∈[r7,r8] and σ 1amax σ 1bmax σ 1cmax Less than the allowable contact stress [σ]; Wherein, T A1 represents the maximum value of the axial vibration signal of the main bearing of the tunneling machine at time t, a1 and a2 are upper and lower limits determined according to T A1(t+Δt) , and T A1(t+Δt) represents the maximum value prediction of the axial vibration signal of the main bearing of the tunneling machine at time t+Δt. T A2 denotes the minimum value of the axial vibration signal of the main bearing of the heading machine at time t, a3, a4 are upper and lower limits determined according to T A2(t+Δt) , and T A2(t+Δt) denotes the minimum value prediction of the axial vibration signal of the main bearing of the heading machine at time t+Δt. T A3 denotes the mean value of the heading machine main bearing axial vibration signal at time t, a5, a6 are upper and lower limits determined according to T A3(t+Δt) A3(t+Δt) denotes the mean value prediction of the heading machine main bearing axial vibration signal at time t+Δt;​ T A4 denotes the root mean square value of the heading machine main bearing axial vibration signal at time t, a7, a8 are upper and lower limits determined according to T A4(t+Δt) , and T A4(t+Δt) denotes the root mean square value of the heading machine main bearing axial vibration signal at time t+Δt. T R1 This represents the maximum value of the radial vibration signal of the tunneling machine's main bearing at time t, where r1 and r2 are based on T. R1(t+Δt) Specific upper and lower limits, T R1(t+Δt) This represents the predicted maximum value of the radial vibration signal of the tunneling machine's main bearing at time t+Δt. T R2 denotes the minimum value of the radial vibration signal of the main bearing of the heading machine at time t, r3, r4 are upper and lower limits determined according to T R2(t+Δt) , and T R2(t+Δt) denotes the minimum value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt. T R3 denotes the mean value of the radial vibration signal of the main bearing of the heading machine at time t, r5, r6 are upper and lower limits determined according to T R3(t+Δt) R3(t+Δt) denotes the mean value prediction of the radial vibration signal of the main bearing of the heading machine at time t+Δt;​ T R4 denotes the root mean square value of the radial vibration signal of the main bearing of the heading machine at time t, r7, r8 are upper and lower limits determined according to T R4(t+Δt) , and T R4(t+Δt) denotes the root mean square value of the radial vibration signal of the main bearing of the heading machine at time t+Δt. σ 1amax , σ 1bmax , σ 1cmax respectively represent the main thrust raceway surface contact stress, the radial raceway surface contact stress, and the auxiliary thrust raceway surface contact stress.

4. The main bearing life prediction method of a tunneling machine according to claim 3, characterized by, In step S4, the process of calculating the total service life of the main bearing of the tunneling machine in the degradation stage is as follows: obtaining basic rated dynamic loads of a main thrust roller group, an auxiliary thrust roller group and a radial roller group of the main bearing of the tunneling machine, then bringing data in the load set into a load distribution relationship to calculate equivalent dynamic loads of the main thrust roller group, the auxiliary thrust roller group and the radial roller group respectively, and calculating a basic rated life of the main bearing of the tunneling machine based on the basic rated dynamic loads and the equivalent dynamic loads, and further obtaining a total service life of the main bearing of the tunneling machine by a probability product formula.

5. The main bearing life prediction method of a tunneling machine according to claim 4, characterized in that, In step S4, the process of calculating the remaining service life of the main bearing of the tunneling machine in the critical stage is as follows: determining a failure mode of the main bearing of the tunneling machine, a defect size corresponding to the failure mode and a defect frequency, establishing a relationship between the defect size and the defect frequency, setting the defect size of the main bearing of the tunneling machine at failure as a defect size threshold, and obtaining the remaining service life of the main bearing of the tunneling machine based on the defect size threshold and the relationship between the defect size and the defect frequency.

6. A main bearing life prediction device for a heading machine, characterized by, The method comprises the following steps: a memory for storing a computer program; a processor for implementing the tunneling machine main bearing life prediction method according to any one of claims 1 to 5 when executing the computer program.

7. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium and is executed by the processor to implement the tunneling machine main bearing life prediction method according to any one of claims 1 to 5.

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