Method and system for monitoring real-time abrasion of composite material bearing and calculating abrasion loss

Abstract

The invention discloses a method for real-time wear monitoring and wear loss calculation of a composite material bearing, which comprises the following steps: acquiring the change of a resistance value monitored by a lubrication state identification sensor in real time, identifying a wear state according to the change of the resistance value, and calculating the wear frequency according to the wear state; acquiring the change of the resistance value of the life prediction sensor monitored by the life prediction sensor in real time, establishing a curve model between the abrasion loss and the resistance value of the life prediction sensor, and calculating the average abrasion loss and the total abrasion loss of each start and stop according to the curve model and the resistance value change of the life prediction sensor; acquiring the thickness wear condition of the resistance layer of the end-of-life sensor collected by the end-of-life sensor, calculating the residual wear times of the whole life cycle corresponding to the residual wear loss according to the set end-of-life thickness, the average wear loss and the total wear loss, and predicting the residual service life of the bearing bush according to the residual wear times. The method can calculate the abrasion loss in real time, can monitor the residual service life of the bearing bush in real time, and is high in prediction accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of bearing wear monitoring, and in particular to a method and system for real-time wear monitoring and wear amount calculation of a composite material bearing. Background Art

[0002] Elastic metal-plastic bearings are widely used in large equipment such as hydro-turbine generators, wind turbines, gearboxes, roller presses, etc., and are one of their key components. Real-time calculation of wear is crucial to ensure the safe operation of bearings.

[0003] With the advancement of science and technology, bearings play an important role in many fields. However, with the increasing demand for frequent start-stop operations in new power systems, wear mainly occurs during the start-stop process. In particular, frequent start-stops in the hydropower industry will accelerate bearing wear; the working conditions of wind power and roller presses are mixed lubrication states, and changes in alternating lubrication states will also cause wear; at present, although there is wear ring monitoring technology, it is impossible to display the amount of wear in real time, and only abnormalities can be detected through alarms; the Chinese invention patent application with publication number CN117968507A, the invention name is "a sliding bearing life prediction sensor and its manufacturing method and prediction method", discloses that the life prediction sensor predicts the life of the sliding bearing, can calculate the amount of wear in stages, and lacks real-time monitoring capabilities; the Chinese invention patent application with publication number CN118010348A, the invention name is "elastic metal plastic bearing lubrication state identification sensor and monitoring method and application", discloses that the lubrication state identification sensor is installed in the elastic metal plastic bearing theoretical dynamic pressure lubrication oil film temperature highest area to monitor the lubrication state of the oil film temperature highest area, but mainly focuses on the lubrication state, not the number of wear. However, with the need for frequent starts and stops, monitoring the number of wear times and accurately predicting bearing life are crucial to ensuring the reliability and safety of the unit. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for real-time wear monitoring and wear calculation of composite material bearings, which can calculate the wear amount in real time and realize real-time monitoring of the remaining service life of the bearing shell with high prediction accuracy.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for real-time wear monitoring and wear calculation of a composite material bearing, characterized by comprising:

[0007] Obtaining a change in a resistance value of a lubrication state identification sensor monitored in real time by the lubrication state identification sensor, identifying a bearing wear state according to the change in the resistance value of the lubrication state identification sensor, and calculating the number of wear times according to the bearing wear state;

[0008] Obtain the change in the resistance value of the life prediction sensor monitored in real time by the life prediction sensor, establish a curve model between the wear amount and the resistance value of the life prediction sensor based on the principle that the resistance value increases as the wear amount of the resistance material decreases, and calculate the average wear amount for each start and stop and the total wear amount when the number of starts and stops reaches a preset threshold number of times based on the curve model and the change in the resistance value of the life prediction sensor;

[0009] Obtain the end-of-life sensor to collect the wear condition of the resistance layer thickness of the end-of-life sensor, calculate the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount, and predict the remaining service life of the bearing shell according to the remaining wear times.

[0010] Furthermore, the specific method for calculating the number of wear times according to the bearing wear state includes:

[0011] Under fluid lubrication conditions, when the monitored resistance value is lower than the set resistance threshold, it is determined to be in a shutdown state. When the resistance value is higher than the set resistance threshold, it is determined to be in a startup state. Each startup and shutdown process is counted as one wear.

[0012] Under mixed lubrication conditions, the number of wear times is not counted when the lubrication state is in mixed lubrication state.

[0013] Furthermore, the specific method of calculating the average wear amount of each start-stop and the total wear amount when the number of starts and stops reaches a preset number threshold according to the curve model and the change in the life prediction sensor resistance value includes:

[0014] The resistance value of the initial operation of the on-site installation is R0, and the resistance value after starting and stopping n0 times is R1. The calculation formula for the change range ΔR of the resistance value during the n0 starts and stops is:

[0015] ΔR=R1-R0;

[0016] Among them, n0 is the number of starts and stops, Δh is the wear amount;

[0017] The calculation formula for the average wear h0 for each start and stop is:

[0018]

[0019] Among them, h0 is the average wear amount per start and stop;

[0020] When the number of starts and stops reaches n times, the accumulated total wear amount h n The calculation formula is:

[0021] h n =h0×n;

[0022] Among them, n is the set number of thresholds, h n is the total accumulated wear amount.

[0023] Furthermore, the calculation formula for calculating the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount is:

[0024]

[0025] Among them, n1 is the number of wear times, h end is the end-of-life resistor layer thickness.

[0026] Furthermore, the formula for predicting the remaining service life of the bearing bush according to the number of wear times is:

[0027] y=n1÷30÷12;

[0028] Where y is the remaining useful life.

[0029] Furthermore, the lubrication state identification sensor is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. Position and close to the outer diameter The monitoring end of the lubrication status identification sensor is flush with the tile surface.

[0030] Furthermore, the life prediction sensor is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. The position is installed at the upper and lower diagonals of each group of tiles and close to the outer diameter or inner diameter. Further, the end-of-life sensor is installed at a distance of 100 mm from the oil inlet end of the elastic metal plastic bush to the length of the bush. The life prediction sensor is installed at a position symmetrical to the radial direction and at the diagonal of the upper and lower ends of each group of tiles. The position between.

[0031] In a second aspect, an embodiment of the present invention provides a system for real-time wear monitoring and wear calculation of a composite material bearing, comprising: a lubrication state identification sensor, a life prediction sensor, a life end sensor and a data processing module, wherein the lubrication state identification sensor is used to monitor the change of the resistance value of the lubrication state identification sensor in real time;

[0032] The data processing module is used to identify the bearing wear state according to the change of the resistance value of the lubrication state identification sensor, and calculate the number of wear times according to the bearing wear state;

[0033] The life prediction sensor is used to monitor the change of the resistance value of the life prediction sensor in real time;

[0034] The data processing module is used to establish a curve model between the wear amount and the resistance value of the life prediction sensor according to the principle that the wear amount of the resistor material decreases while the resistance value increases, and calculate the average wear amount of each start and stop and the total wear amount when the number of starts and stops reaches a preset number threshold according to the curve model and the change of the resistance value of the life prediction sensor;

[0035] The end-of-life sensor is used to collect the wear condition of the resistance layer thickness of the end-of-life sensor;

[0036] The data processing module is used to calculate the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount, and predict the remaining service life of the bearing shell according to the remaining wear times.

[0037] Further, the data processing module includes a wear times calculation unit, and the wear times calculation unit adopts different calculation methods according to different industrial controls of the bearing, specifically: under fluid lubrication conditions, when the monitored resistance value is lower than the set resistance threshold, it is determined to be a shutdown state, and when the resistance value is higher than the set resistance threshold, it is determined to be a start state, and each start-stop process is counted as one wear;

[0038] Under mixed lubrication conditions, the number of wear times is not counted when the lubrication state is in mixed lubrication state.

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

[0040] A method and system for real-time wear monitoring and wear calculation of composite material bearings provided in an embodiment of the present invention has the following advantages:

[0041] 1) Significantly improve the prediction accuracy of bearing life, optimize the life cycle and maintenance cycle planning, effectively avoid economic losses caused by excessive wear, realize the transformation from fault maintenance to condition maintenance, and thus promote intelligent operation and maintenance.

[0042] 2) Through the coordinated work of state recognition sensors, life prediction sensors and end-of-life sensors, the wear prediction accuracy is ensured to be close to 100%, significantly improving the safety and reliability of the bearing.

[0043] 3) Accurate monitoring and prediction provide technical support for the intelligent operation and maintenance of the units, which not only enhances the safety and reliability of equipment, but also helps the digital transformation of green energy equipment.

[0044] 4) The sensor is replaceable to ensure the safety, stability and efficiency of monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0046] Figure 1 A flow chart of a method for real-time wear monitoring and wear calculation of a composite material bearing provided by the first embodiment of the present invention;

[0047] Figure 2 A perspective view of a lubrication status recognition sensor;

[0048] Figure 3 Stereoscopic diagram of the sensor for life prediction;

[0049] Figure 4 Schematic diagram of the working of the sensor for life prediction;

[0050] Figure 5 is a perspective view of an end-of-life sensor;

[0051] Figure 6 The installation position diagram of three types of sensors on the elastic metal plastic tile;

[0052] Figure 7 A diagram showing the installation positions of the lubrication status identification sensor, the life prediction sensor, and the life end sensor on the first component tile;

[0053] Figure 8 A diagram showing the installation positions of the lubrication status identification sensor, the life prediction sensor, and the life end sensor on the second component tile;

[0054] Fig. 9 The installation position diagram of the lubrication status recognition sensor on the single-piece bearing is as follows;

[0055] Fig.10 This is the full life cycle model curve diagram with a wear amount of 0.10mm;

[0056] Fig.11 A statistical graph of the number of wear times monitored by the resistance value of the state recognition sensor;

[0057] Fig.12 Operating data trend chart of sensor resistance value for life prediction;

[0058] Fig.13 A structural block diagram of a system for real-time wear monitoring and wear calculation of composite material bearings provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0060] Example 1

[0061] like Figure 1 As shown, a method for real-time wear monitoring and wear calculation of composite material bearings provided by the first embodiment of the present invention is applicable to a data processing module in a system for real-time wear monitoring and wear calculation of composite material bearings. The method comprises the following steps:

[0062] Obtaining a change in a resistance value of a lubrication state identification sensor monitored in real time by the lubrication state identification sensor, identifying a bearing wear state according to the change in the resistance value of the lubrication state identification sensor, and calculating the number of wear times according to the bearing wear state;

[0063] Obtain the change in the resistance value of the life prediction sensor monitored in real time by the life prediction sensor, establish a curve model between the wear amount and the resistance value of the life prediction sensor based on the principle that the resistance value increases as the wear amount of the resistance material decreases, and calculate the average wear amount for each start and stop and the total wear amount when the number of starts and stops reaches a preset threshold number of times based on the curve model and the change in the resistance value of the life prediction sensor;

[0064] Obtain the end-of-life sensor to collect the wear condition of the resistance layer thickness of the end-of-life sensor, calculate the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount, and predict the remaining service life of the bearing shell according to the remaining wear times.

[0065] In this embodiment, the lubrication state recognition sensor 1, the life prediction sensor 2, and the life end sensor 3 are all existing sensors. Figure 2 , showing a stereoscopic view of a lubrication state recognition sensor, Figure 3 shows a perspective view of a life prediction sensor, Figure 4 The working schematic diagram of the lifetime prediction sensor is shown. Figure 5 A perspective view of an end-of-life sensor is shown.

[0066] The lubrication state identification sensor 1 is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. Position, close to the outer diameter The wear status is monitored by the change of resistance value, which changes with the change of oil film thickness (thinning or disappearance).

[0067] Among them, the specific method of calculating the number of wear times according to the bearing wear state includes:

[0068] Under fluid lubrication conditions (such as hydropower units), when the monitored resistance value is lower than the set resistance threshold, that is, the resistance valley value is less than 1*10 4 Ω, it is judged as shutdown state. When the resistance value is higher than the set resistance threshold, that is, the resistance valley value is greater than 1*10 4 Ω, it is judged as the start-up state, and each start-up and shutdown process is counted as one wear; under mixed lubrication conditions (such as roller presses, wind turbines), when the lubrication state is in the mixed lubrication state, that is, the resistance value peak is greater than 8*10 4 Ω, not counted as the number of wear times. By real-time monitoring of the change in resistance value, the number of starts and stops of the unit can be identified. Among them, fluid lubrication refers to a certain distance between the two friction pairs without contact, the lubricant fills the space between them, the friction coefficient is very small (0.001~0.01) and depends on the viscosity of the lubricant, the interaction between the lubricant and the friction pair, and their relative movement speed. Mixed lubrication refers to the alternation between fluid lubrication and solid lubrication (self-lubricating medium), which means that there is a certain oil film thickness and a small amount of surface contact between the friction pairs, so its friction coefficient is between solid lubrication and fluid lubrication (0.001~0.10). The resistance value valley represents the minimum value displayed by the state identification data, that is, the oil film is reduced or disappears. The resistance value peak represents the maximum value displayed by the state identification data, that is, the oil film is well formed.

[0069] The life prediction sensor 2 is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. The position of each set of tiles is located at the diagonal of the upper and lower ends, close to the outer diameter or inner diameter. The position between ; This installation method helps to accurately monitor the wear of the thrust washer. Working principle of life prediction sensor 2: Life prediction sensor 2 monitors the change of the resistance value of the life prediction sensor in real time. Based on the principle that the resistance value increases due to the wear of the resistor material, a curve model between the wear amount and the resistance value is established to accurately judge the wear amount. By recording the change of the resistance value when the thickness of the resistor layer of the life prediction sensor 2 is thinned by 0.01 to 0.03 mm, a function curve is used to calculate the relationship between the wear amount and the resistance value, and a full life cycle model of the life prediction sensor 2 when the wear amount is a certain value is established, thereby retaining the thickness of the resistor layer corresponding to the linear change.

[0070] The specific method of calculating the average wear amount of each start-stop and the total wear amount when the number of starts and stops reaches a preset number threshold according to the curve model and the resistance value change of the life prediction sensor 2 includes:

[0071] The resistance value of the initial operation of the on-site installation is R0, and the resistance value after starting and stopping n0 times is R1. The calculation formula for the change range ΔR of the resistance value during the n0 starts and stops is:

[0072] ΔR=R1-R0;

[0073] Among them, n0 is the number of starts and stops, Δh is the wear amount;

[0074] The calculation formula for the average wear h0 for each start and stop is:

[0075]

[0076] Among them, h0 is the average wear amount per start and stop;

[0077] When the number of starts and stops reaches n times, the accumulated total wear amount h n The calculation formula is:

[0078] h n =h0×n;

[0079] Among them, n is the set number of thresholds, h n is the total accumulated wear amount.

[0080] The end-of-life sensor 3 is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. The position is symmetrical with the life prediction sensor 3 in the radial direction, and the diagonal lines of the upper and lower ends of each group of tiles are set at The position between ensures accurate monitoring of wear. The working principle of the end-of-life sensor 3 is: when the thickness of the resistor layer is worn to the set standard thickness, the end-of-life signal is triggered, and the average wear amount each time is calculated according to the wear condition. The wear amount monitoring of the end-of-life sensor 3 records the change in resistance value when the thickness of the sensor resistor layer is thinned by 0.01 to 0.03 mm; when the resistance value changes slightly or tends to a linear trend, focus on the thickness of the resistor layer corresponding to the linear or nonlinear change of the resistance value and the wear amount.

[0081] The end-of-life sensor resistance value is linear or nonlinear relationship model:

[0082] ① Linear relationship: refers to a linear equation with two variables, expressed as y=k·x+b, where k is the slope and b is the intercept.

[0083] ②Exponential relationship: refers to the relationship between a variable y and another variable x, which can be expressed as y = a·b x , where b>0, b is called the base, x is called the exponent, both are constants;

[0084] ③ Quadratic function relationship: refers to the relationship between a variable y and another variable x, which can be expressed as y=kx 2 +bx+c, where k is the opening direction and width, b is the position of the symmetry point and the fixed point, and c is the intercept, all of which are constants.

[0085] The calculation formula for the remaining wear times of the full life cycle corresponding to the remaining wear amount is calculated based on the set end-of-life thickness, average wear amount and total wear amount:

[0086]

[0087] Among them, n1 is the number of wear times, h end is the end-of-life resistor layer thickness.

[0088] The formula for predicting the remaining service life of the bearing based on the number of wear times is:

[0089] y=n1÷30÷12;

[0090] Where y is the remaining useful life, 30 represents the number of days in a month, and 12 represents the number of months in a year.

[0091] The above embodiment is described in detail below using specific examples:

[0092] A verification unit uses FZB3G elastic metal plastic bearings (12 pieces / set) produced by Dalian Sanhuan Composite Materials Technology Development Co., Ltd. (hereinafter referred to as "Sanhuan Composites") as carriers, among which 1#~12# thrust bearings are equipped with oil film temperature sensors and lubrication status identification sensors 1, 1#, 4#, 7#, 10# thrust bearings are equipped with bearing body temperature sensors, oil inlet temperature sensors, life prediction sensors 2 and life end sensors 3, forming intelligent elastic metal plastic thrust bearings, and equipped with thrust bearing online monitoring system for monitoring. The installation positions of lubrication status identification sensors 1, life prediction sensors 2 and life end sensors 3 on the elastic metal plastic bearings are shown in Figure 1. Figure 6 . Figure 7 This is the installation position diagram of the lubrication state recognition sensor 1, the life prediction sensor 2, and the life end sensor 3 on the first component tile. Figure 8 This is the installation position diagram of the lubrication state recognition sensor 1, the life prediction sensor 2, and the life end sensor 3 on the second component tile. Fig. 9 The following is a diagram showing the installation position of the lubrication state recognition sensor 1 on the single-piece shoe.

[0093] When the thickness of the resistance layer of the life prediction sensor 2 is 0.50mm, by recording the change in resistance value every time the resistance layer is thinned by 0.01 to 0.30mm on the wear tester, the resistance change law under different thicknesses can be analyzed. Specifically, when the thickness of the resistance layer is worn away from 0mm to 0.30mm, the change in resistance value is small, showing a trend close to a straight line; when the thickness of the resistance layer is worn to between 0.30mm and 0.40mm, the resistance value increases linearly; and when the thickness of the resistance layer is worn to between 0.40mm and 0.50mm, the resistance value rises sharply, showing a nonlinear relationship. Therefore, based on the analysis results of this full life cycle model curve, the service life of the thrust bearing working layer can be predicted by monitoring the relationship between the resistance value and the wear amount within this thickness range.

[0094] Before leaving the factory, the initial thickness of the resistance layer of the life prediction sensor 2 is 0.10 mm. By monitoring the change of the resistance value when the resistance layer decreases by 0.01 mm on the wear tester, a full life cycle model curve with a wear amount of 0.10 mm is established, such as Fig.10 shown.

[0095] On-site operation data of a verification unit:

[0096] The digital bearing was installed in a verification unit at the end of May 2024, and started trial operation on June 5 and ran until October 20. The wear frequency statistics of the resistance value monitoring of the lubrication state identification sensor are shown in Fig.11 The operating data trend chart of the life prediction sensor resistance value is shown in Fig.12 .

[0097] Remaining wear and service life of a verification unit:

[0098] ① According to the full life cycle model curve and the operational changes in the resistance value of a verification unit, the resistance value changes of the life prediction sensor are: 1# resistance value increased by 2.5%, 7# resistance value increased by 2.0%. These changes correspond to a full life cycle wear of nearly 0.01mm.

[0099] ② According to the analysis of the state recognition resistance value: from June 5 to October 20, the number of wear times was 142 times (134 days of operation, an average of once a day start and stop).

[0100] ③ According to the calculation of wear times and wear amount, the average wear amount for each start and stop is 7.04*10 -5 mm.

[0101] ④ When the end-of-life resistor layer thickness is 0.50mm, the remaining wear amount and the number of wear times in the entire life cycle are:

[0102]

[0103] ⑤ Based on the above calculation, the number of operating years corresponding to the remaining wear is: 6960 ÷ 30 days ÷ 12 months ≈ 20 years.

[0104] By integrating the data from status recognition sensors, life prediction sensors, and end-of-life sensors, the amount of wear and its service life can be predicted, thereby achieving intelligent operation and periodic maintenance.

[0105] A method for real-time wear monitoring and wear calculation of a composite material bearing provided by an embodiment of the present invention has the following advantages:

[0106] 1) Significantly improve the prediction accuracy of bearing life, optimize the life cycle and maintenance cycle planning, effectively avoid economic losses caused by excessive wear, realize the transformation from fault maintenance to condition maintenance, and thus promote intelligent operation and maintenance.

[0107] 2) Through the coordinated work of state recognition sensors, life prediction sensors and end-of-life sensors, the wear prediction accuracy is ensured to be close to 100%, significantly improving the safety and reliability of the bearing.

[0108] 3) Accurate monitoring and prediction provide technical support for the intelligent operation and maintenance of the units, which not only enhances the safety and reliability of equipment, but also helps the digital transformation of green energy equipment.

[0109] 4) The sensors in the solution are replaceable to ensure the security, stability and efficiency of monitoring data.

[0110] Based on the above advantages, the embodiments of the present invention can be widely used in wear monitoring systems of equipment such as wind power, hydropower, gear boxes, roller presses, etc., to promote the popularization and application of intelligent monitoring technology.

[0111] Example 2

[0112] like Fig.13 As shown, another embodiment of the present invention provides a system for real-time wear monitoring and wear calculation of composite bearings, comprising: a lubrication state identification sensor 1, a life prediction sensor 2, a life end sensor 3 and a data processing module 4, wherein the lubrication state identification sensor 1 is used to monitor the change of the resistance value of the lubrication state identification sensor in real time;

[0113] The data processing module 4 is used to identify the bearing wear state according to the change of the resistance value of the lubrication state identification sensor, and calculate the number of wear times according to the bearing wear state;

[0114] The life prediction sensor 2 is used to monitor the change of the resistance value of the life prediction sensor in real time;

[0115] The data processing module 4 is used to establish a curve model between the wear amount and the life prediction sensor resistance value according to the principle that the wear amount of the resistor material decreases while the resistance value increases, and calculate the average wear amount of each start and stop and the total wear amount when the number of starts and stops reaches a preset number threshold according to the curve model and the change of the life prediction sensor resistance value;

[0116] The end-of-life sensor 3 is used to collect the wear condition of the resistance layer thickness of the end-of-life sensor;

[0117] The data processing module 4 is used to calculate the remaining wear times of the whole life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount, and predict the remaining service life of the bearing shell according to the remaining wear times.

[0118] Among them, the data processing module 4 includes a wear count calculation unit, which uses different calculation methods according to the bearing under different industrial controls. Specifically, under fluid lubrication conditions, when the monitored resistance value is lower than the set resistance threshold, it is judged as a shutdown state, and when the resistance value is higher than the set resistance threshold, it is judged as a start-up state. Each start-up and shutdown process is counted as one wear; under mixed lubrication conditions, when the lubrication state is in a mixed lubrication state, it is not counted as a wear count.

[0119] Another embodiment of the present invention provides a system for real-time wear monitoring and wear calculation of composite bearings, which can realize real-time monitoring of bearing wear, provide accurate maintenance cycles, and ensure safe and stable operation of the unit. Through the coordinated work of state recognition sensors, life prediction sensors, and end-of-life sensors, the accuracy of wear prediction is ensured to be close to 100%, significantly improving the safety and reliability of bearings. Through precise monitoring and prediction, it not only provides intelligent solutions for traditional equipment, but also helps promote the digital transformation and intelligent upgrading of green energy equipment.

[0120] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for real-time wear monitoring and wear calculation of composite bearings, characterized in that: include: Obtaining a change in a resistance value of a lubrication state identification sensor monitored in real time by the lubrication state identification sensor, identifying a bearing wear state according to the change in the resistance value of the lubrication state identification sensor, and calculating the number of wear times according to the bearing wear state; Obtain the change in the resistance value of the life prediction sensor monitored in real time by the life prediction sensor, establish a curve model between the wear amount and the resistance value of the life prediction sensor based on the principle that the resistance value increases as the wear amount of the resistance material decreases, and calculate the average wear amount for each start and stop and the total wear amount when the number of starts and stops reaches a preset threshold number of times based on the curve model and the change in the resistance value of the life prediction sensor; Obtain the end-of-life sensor to collect the wear condition of the resistance layer thickness of the end-of-life sensor, calculate the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount, and predict the remaining service life of the bearing shell according to the remaining wear times.

2. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 1, characterized in that: The specific method for calculating the number of wear times according to the bearing wear state includes: Under fluid lubrication conditions, when the monitored resistance value is lower than the set resistance threshold, it is determined to be in a shutdown state. When the resistance value is higher than the set resistance threshold, it is determined to be in a startup state. Each startup and shutdown process is counted as one wear. Under mixed lubrication conditions, the number of wear times is not counted when the lubrication state is in mixed lubrication state.

3. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 1, characterized in that: The specific method of calculating the average wear amount of each start-stop and the total wear amount when the number of starts and stops reaches a preset number threshold according to the curve model and the change in the life prediction sensor resistance value includes: The resistance value of the initial operation of the on-site installation is R0, and the resistance value after starting and stopping n0 times is R1. The calculation formula for the change range ΔR of the resistance value during the n0 starts and stops is: ΔR=R1-R0; Among them, n0 is the number of starts and stops, Δh is the wear amount; The calculation formula for the average wear h0 for each start and stop is: Among them, h0 is the average wear amount per start and stop; When the number of starts and stops reaches n times, the accumulated total wear amount h n The calculation formula is: h n =h0×n; Among them, n is the set number of thresholds, h n is the total accumulated wear amount.

4. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 3 is characterized in that: The calculation formula for calculating the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount is: Among them, n1 is the number of wear times, h end is the end-of-life resistor layer thickness.

5. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 4, characterized in that: The formula for predicting the remaining service life of the bearing bush according to the number of wear times is: y=n1÷30÷12; Where y is the remaining useful life.

6. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 1, characterized in that: The lubrication state identification sensor is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. Position and close to the outer diameter The monitoring end of the lubrication status identification sensor is flush with the tile surface.

7. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 6, characterized in that: The life prediction sensor is installed at a distance of the bearing bush length from the oil inlet end of the elastic metal plastic bush. The position is installed at the upper and lower diagonals of each group of tiles and close to the outer diameter or inner diameter. location.

8. The method for real-time wear monitoring and wear calculation of composite material bearings according to claim 7, characterized in that: The end-of-life sensor is installed at a distance of the length of the bearing bush from the oil inlet end of the elastic metal plastic bush. The life prediction sensor is installed at a position symmetrical to the radial direction and at the diagonal of the upper and lower ends of each group of tiles. The position between.

9. A system for real-time wear monitoring and wear calculation of composite material bearings, characterized in that: include: A lubrication state identification sensor, a life prediction sensor, a life end sensor and a data processing module, wherein the lubrication state identification sensor is used to monitor the change of the resistance value of the lubrication state identification sensor in real time; The data processing module is used to identify the bearing wear state according to the change of the resistance value of the lubrication state identification sensor, and calculate the number of wear times according to the bearing wear state; The life prediction sensor is used to monitor the change of the resistance value of the life prediction sensor in real time; The data processing module is used to establish a curve model between the wear amount and the resistance value of the life prediction sensor according to the principle that the wear amount of the resistor material decreases while the resistance value increases, and calculate the average wear amount of each start and stop and the total wear amount when the number of starts and stops reaches a preset number threshold according to the curve model and the change of the resistance value of the life prediction sensor; The end-of-life sensor is used to collect the wear condition of the resistance layer thickness of the end-of-life sensor; The data processing module is used to calculate the remaining wear times of the full life cycle corresponding to the remaining wear amount according to the set end-of-life thickness, average wear amount and total wear amount, and predict the remaining service life of the bearing shell according to the remaining wear times.

10. The system for real-time wear monitoring and wear calculation of composite material bearings according to claim 9, characterized in that: The data processing module includes a wear times calculation unit, and the wear times calculation unit adopts different calculation methods according to different industrial control of the bearing, specifically: under fluid lubrication conditions, when the monitored resistance value is lower than the set resistance threshold, it is determined to be a shutdown state, and when the resistance value is higher than the set resistance threshold, it is determined to be a start state, and each start-stop process is counted as one wear; Under mixed lubrication conditions, the number of wear times is not counted when the lubrication state is in mixed lubrication state.

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