Sliding bearing wear monitoring method, monitoring system, equipment and readable storage medium

Abstract

The invention relates to a sliding bearing wear monitoring method, monitoring system and equipment and a readable storage medium. The sliding bearing wear monitoring method comprises the following steps: collecting a thermoelectric force signal between a shaft and a bearing when the sliding bearing works; identifying a thermoelectric potential abnormal peak in the thermoelectric potential signal; wear characteristic parameters are extracted based on the thermoelectric force signals, the wear characteristic parameters comprise wear intensity, wear frequency and wear width, and the wear intensity is a root-mean-square value of thermoelectric force in a continuous working interval of the sliding bearing; the wear frequency is the occurrence frequency of the thermoelectric force abnormal peaks in the continuous working interval of the sliding bearing; the wear width is the proportion of the duration of the thermoelectric force abnormal value in the continuous working interval of the sliding bearing; and generating an alarm triggering condition combination according to the threshold values of the wear intensity, the wear frequency and the wear width, and generating a multi-stage alarm mechanism based on alarm triggering conditions. According to the invention, the accuracy of wear monitoring of the sliding bearing can be realized, the possibility of misjudgment and missed judgment is reduced, and potential wear problems can be found earlier.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power engineering and structural seismic analysis, and particularly relates to a sliding bearing wear monitoring method, a monitoring system, equipment and a readable storage medium. BACKGROUND

[0002] Sliding bearings are widely used in high-speed and heavy-load rotating machinery such as motors, engines and machine tools. When working normally, the dynamic pressure oil film is formed between the shaft and the bearing through the lubricating medium such as lubricating oil and grease, which greatly reduces the friction coefficient to bear the impact load and reduce wear. However, when the sliding bearing is affected by poor lubrication, excessive load and harsh working environment, it is easy to produce abnormal wear, gluing and fatigue spalling, which may affect the stability of the equipment, or even cause shutdown, component scrap and safety accidents.

[0003] At present, the real-time monitoring methods of sliding bearings mainly include temperature monitoring, vibration monitoring, online oil detection and oil film thickness monitoring. Among them, temperature and vibration monitoring are not sensitive to early wear, cannot directly monitor the wear state and are prone to false positives or false negatives; online oil detection and oil film thickness monitoring have limitations such as high installation and maintenance requirements and high cost, and are not suitable for all mechanical equipment.

[0004] Thermoelectric monitoring method as a new type of sliding bearing monitoring method can quickly judge the wear state by collecting the thermoelectric potential change generated between the shaft and the bearing due to contact, and has strong mechanical applicability. However, the existing shaft system wear monitoring method based on thermoelectric signal only judges wear by the size of thermoelectric signal, which has single data form and cannot evaluate the lubrication and wear state of the shaft and bearing, and has low early warning accuracy and is prone to false positives or false negatives.

[0005] In summary, the existing technology has the problems of single monitoring method, limited wear state judgment basis, low early warning accuracy and insufficient alarm strategy. SUMMARY

[0006] In view of the above problems of the prior art, the present application provides a sliding bearing wear monitoring method, a monitoring system, equipment and a readable storage medium, which can realize the accuracy of sliding bearing wear monitoring, reduce the possibility of false positives and false negatives, and find potential wear problems earlier.

[0007] Specifically, the present application provides a sliding bearing wear monitoring method, comprising the steps of:

[0008] collecting the thermoelectric potential signal between the shaft and the bearing during the operation of the sliding bearing;

[0009] identifying the thermoelectric potential abnormal peak in the thermoelectric potential signal, the thermoelectric potential abnormal peak being a continuous interval in which the thermoelectric potential value exceeds a preset threshold value;

[0010] extract a wear characteristic parameter based on the thermoelectric potential signal, the wear characteristic parameter including a wear intensity, a wear frequency, and a wear width, wherein the wear intensity is a root mean square value of the thermoelectric potential in a continuous working interval; the wear frequency is a number of occurrences of a thermoelectric potential abnormal peak in the continuous working interval; and the wear width is a proportion of a duration of a thermoelectric potential abnormal value in the continuous working interval.

[0011] generate a combination of alarm triggering conditions according to thresholds of the wear intensity, the wear frequency, and the wear width, and generate a multi-level alarm mechanism based on the alarm triggering conditions.

[0012] According to an embodiment of the present application, the wear intensity is a root mean square value of the thermoelectric potential in a continuous working interval, and the calculation formula is:

[0013] wherein, is a number of sampling points of one continuous working interval, is a thermoelectric potential value under each sampling point; is the wear intensity, i.e., a root mean square value of the thermoelectric potential in one continuous working interval;

[0014] The calculation formula of the wear width is:

[0015] wherein, is a number of sampling points of one thermoelectric potential abnormal peak; is the wear frequency, i.e., a number of occurrences of a thermoelectric potential abnormal peak in one continuous working interval.

[0016] According to an embodiment of the present application, the preset threshold E0 is set according to material characteristics of the shaft and the bearing, running conditions, lubrication conditions, and comparison and comprehensive setting of thermoelectric potential data of a normal running state and an abnormal wear state of a similar device.

[0017] According to an embodiment of the present application, a first threshold, a second threshold, and a third threshold are set to correspond to the wear intensity, the wear frequency, and the wear width, respectively.

[0018] If any one of the wear characteristic parameters exceeds the corresponding threshold, a mild alarm is triggered.

[0019] If any two of the wear characteristic parameters exceed the corresponding thresholds at the same time, a moderate alarm is triggered.

[0020] If all the wear characteristic parameters exceed the corresponding thresholds, a severe alarm is triggered.

[0021] ​​According to one embodiment of the present application, the thresholds of the wear intensity, wear frequency and wear width are determined by means of preliminary theoretical calculation, data and test verification for different sliding bearing forms.

[0022] According to one embodiment of the present application, when a mild alarm is triggered, it is checked whether the related test parameters of the sliding bearing temperature, lubricating medium state, rotating speed and load are abnormal;

[0023] When a moderate alarm is triggered, a power reduction operation is performed and the changes of the related test parameters of the equipment are continuously monitored, and after shutdown, the wear condition of the sliding bearing is checked and maintenance measures are taken;

[0024] When a severe alarm is triggered, an emergency shutdown is triggered, and after shutdown, the sliding bearing is disassembled and inspected to determine whether there is wear and ablation phenomenon.

[0025] According to one embodiment of the present application, the mild alarm and the moderate alarm are automatically reset within a set time after the wear characteristic parameters continuously fall below the corresponding thresholds, and the severe alarm needs to be manually reset.

[0026] The present application also provides a sliding bearing wear monitoring system for implementing the foregoing sliding bearing wear monitoring method, the sliding bearing wear monitoring system comprising:

[0027] A data acquisition unit is configured to acquire a thermoelectric potential signal between a shaft and a bearing during operation of the sliding bearing;

[0028] An identification unit is configured to identify a thermoelectric potential abnormal peak in the thermoelectric potential signal, the thermoelectric potential abnormal peak being a continuous interval in which the thermoelectric potential value exceeds a preset threshold;

[0029] An extraction unit is configured to extract wear characteristic parameters based on the thermoelectric potential signal, the wear characteristic parameters including wear intensity, wear frequency and wear width, wherein the wear intensity is a root mean square value of the thermoelectric potential in a continuous working interval; the wear frequency is the number of times of occurrence of the thermoelectric potential abnormal peak in the continuous working interval; and the wear width is a proportion of the duration of the thermoelectric potential abnormal value in the continuous working interval;

[0030] A threshold setting unit is configured to set thresholds of the wear intensity, wear frequency and wear width;

[0031] An alarm trigger condition generation unit is configured to generate an alarm trigger condition combination according to the thresholds of the wear intensity, wear frequency and wear width;

[0032] An alarm mechanism generation unit is configured to generate a multi-level alarm mechanism based on the alarm trigger condition.

[0033] The application further provides a sliding bearing wear monitoring device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the sliding bearing wear monitoring method according to any one of the preceding embodiments when executing the computer program.

[0034] The application further provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program implements the steps of the sliding bearing wear monitoring method according to any one of the preceding embodiments when executed by a processor.

[0035] The sliding bearing wear monitoring method, the monitoring system, the device and the computer readable storage medium provided by the application comprehensively evaluate the wear condition between the shaft and the bearing from the overall wear state of the sliding bearing, the frequency of abnormal wear, the duration of lubrication failure and the wear trend caused by the lubrication failure, etc., cover all stages of wear according to different change rules of different wear characteristic parameters, and the alarm strategy is formulated through the parameter threshold, so that the accuracy of monitoring is improved, the possibility of misjudgment and omission is reduced, and potential wear problems can be found earlier.

[0036] It should be understood that the above general description and the following detailed description of the application are exemplary and illustrative, and are intended to provide further explanation of the application. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings are included to provide further explanation of the application, which are incorporated into and constitute a part of the present application, and show embodiments of the application, and together with the present specification, serve to explain the principles of the application. In the drawings:

[0038] Figure 1 A flow chart of a sliding bearing wear monitoring method according to an embodiment of the application is shown.

[0039] Figure 2 A schematic diagram of a thermoelectric potential abnormal peak according to an embodiment of the application is shown.

[0040] Figure 3 A logic diagram for implementing a mild alarm according to an embodiment of the application is shown.

[0041] Figure 4 A logic diagram for implementing a moderate alarm according to an embodiment of the application is shown.

[0042] Figure 5 A logic diagram for implementing a severe alarm according to an embodiment of the application is shown.

[0043] Figure 6 A structural schematic diagram of a sliding bearing wear monitoring system according to an embodiment of the application is shown.

[0044] Figure 7 A system block diagram of a sliding bearing wear monitoring device is shown. DETAILED DESCRIPTION

[0045] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict.

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0047] It should be noted that the terms used herein are only intended to describe specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0048] Unless specifically stated otherwise, the relative arrangement of components and steps, numerical expressions, and numerical values set forth in the various embodiments described herein are not limitations on the scope of the present application. It should also be understood that the dimensions of the various parts shown in the drawings are not necessarily to scale. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the specification, if appropriate. In all examples shown and discussed herein, any specific value should be interpreted as merely illustrative, and not as a limitation. Therefore, other examples of the exemplary embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0049] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0050] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0051] Figure 1 A flowchart of a sliding bearing wear monitoring method according to an embodiment of the present invention is shown. As shown, a sliding bearing wear monitoring method includes the following steps:

[0052] S1. Collect the thermoelectric potential signal between the shaft and the bearing when the sliding bearing is working. This signal is the basic data reflecting the friction and heat generation state between the shaft and the bearing. During normal operation, the oil film isolation stabilizes the thermoelectric potential signal. During abnormal wear, the increased friction causes the signal to fluctuate. This step provides the raw information for subsequent wear state analysis.

[0053] S2, identify abnormal thermoelectric potential peaks in the thermoelectric potential signal, and determine the continuous interval where the thermoelectric potential value exceeds the preset threshold as an abnormal thermoelectric potential peak, thereby distinguishing the thermoelectric potential changes corresponding to normal operation and abnormal wear. The purpose of this step is to filter out signal segments directly related to abnormal wear from the original signal, and establish a judgment basis for subsequent extraction of wear features. Figure 2 A schematic diagram of an abnormal thermoelectric potential peak according to an embodiment of the present invention is shown. As shown in the figure, the vertical axis (thermoelectric potential e) iThe horizontal axis (sampling points) represents the amplitude of the thermoelectric potential signal generated by friction between the shaft and bearing, reflecting the intensity of the frictional heating. The horizontal axis represents the time series nodes of signal acquisition, used to quantify the duration of the thermoelectric potential anomaly. A threshold value E0 is set as the criterion for distinguishing between "normal thermoelectric potential" and "abnormal thermoelectric potential." When the thermoelectric potential exceeds the threshold value E0, it is determined to be an abnormal value, corresponding to an abnormal wear state of the sliding bearing. The number of abnormal peak sampling points is d. i It directly reflects the duration of an abnormal wear event and serves as the basis for calculating the subsequent "wear width" characteristic parameter.

[0054] S3 extracts wear characteristic parameters based on thermoelectric potential signals. These parameters include wear intensity, wear frequency, and wear width. Wear intensity is represented by the root mean square value of the thermoelectric potential within a continuous operating range, used to quantify the overall severity of wear on the sliding bearing. Wear frequency is calculated by counting the number of abnormal thermoelectric potential peaks within the continuous operating range, reflecting the frequency of abnormal wear. Wear width is calculated as the proportion of time spent with abnormal thermoelectric potential values ​​within the continuous operating range, reflecting the duration of lubrication failure or wear. These three wear characteristic parameters comprehensively describe the wear state of the sliding bearing from different dimensions.

[0055] S4 generates alarm trigger condition combinations based on thresholds for wear intensity, wear frequency, and wear width. Based on these combinations, a multi-level alarm mechanism is generated to achieve graded early warning of the wear status of sliding bearings, so as to take corresponding measures in a timely manner and ensure the stable operation of the equipment.

[0056] In some examples, the wear intensity is the root mean square value of the thermoelectric potential over a continuous operating range, calculated using the following formula: ;

[0057] in, The number of sampling points for a continuous working interval. The thermoelectric potential value at each sampling point; Wear intensity is a parameter that directly reflects the severity of overall wear on a sliding bearing.

[0058] Formula for calculating wear width: ;

[0059] in, The number of sampling points for a single thermoelectric potential anomaly peak. Wear frequency and wear width reflect the duration of lubrication failure or abnormal wear, providing a basis for judging the wear development trend.

[0060] In some examples, the preset threshold E0 is set comprehensively based on the material properties of the shaft and bearing, operating conditions, lubrication conditions, and thermoelectric potential data comparing the normal operating conditions and abnormal wear conditions of similar equipment. Specifically, in the process of monitoring sliding bearing wear, the identification of abnormal thermoelectric potential peaks is a key step in screening abnormal wear signals. The preset threshold E0 serves as the criterion for identifying abnormal peaks, and its setting accuracy directly determines the accuracy of subsequent wear feature extraction and alarm judgment. Therefore, it needs to be determined comprehensively by combining multiple key factors.

[0061] From the perspective of threshold setting, the material properties of the shaft and bearing are fundamental prerequisites. Friction pairs composed of different materials exhibit varying fundamental thermoelectric effects, directly impacting the baseline range of the thermoelectric potential signal under normal and abnormal conditions. Operating conditions and lubrication conditions are core variables that align with actual operating scenarios. High and low speeds, heavy and light loads, and the state of the oil film formed by different lubrication media and methods all lead to differences in frictional heat generation patterns and corresponding thermoelectric potential signal amplitudes, which must be considered in the setting to adapt to specific application scenarios. Simultaneously, comparing thermoelectric potential data from similar equipment under normal operation and abnormal wear conditions allows for verification of the threshold's rationality based on practical experience with similar equipment, avoiding setting deviations caused by considering only a single factor. Through a comprehensive balance of these multiple factors, the preset threshold E0 not only matches the inherent characteristics and operational requirements of the monitored object but also relies on experience with similar equipment to ensure its accuracy in distinguishing between normal and abnormal thermoelectric potential signals, providing a reliable basis for subsequent accurate identification of abnormal thermoelectric potential peaks and extraction of wear characteristic parameters.

[0062] In some examples, in the graded early warning stage of sliding bearing wear, it is necessary to first set corresponding judgment criteria for three wear characteristic parameters, namely, setting a first threshold corresponding to wear intensity, a second threshold corresponding to wear frequency, and a third threshold corresponding to wear width. By matching dedicated thresholds for wear indicators of different dimensions, it is ensured that the abnormal state of each parameter can be accurately identified, laying a clear basis for the logical judgment of subsequent graded alarms. Figure 3 The diagram illustrates the logic of a mild alarm implementation according to an embodiment of the present invention. As shown, a mild alarm is triggered if any of the wear characteristic parameters exceeds its corresponding threshold. The core function of this alarm level is to detect potential wear trends early. In other words, even if only a single parameter is abnormal, it means that the sliding bearing may have experienced initial lubrication fluctuations or slight wear. A mild alarm can promptly alert staff to pay attention to the equipment status, conduct basic parameter verification and strengthen monitoring, and prevent the potential wear hazards from developing further.

[0063] Figure 4The diagram illustrates the logic of a moderate alarm implementation according to an embodiment of the present invention. As shown, a moderate alarm is triggered if any two wear characteristic parameters simultaneously exceed their respective thresholds. This level of alarm indicates that the wear condition has entered a stage of cumulative aggravation. The coordinated abnormality of the two parameters often means that the duration of lubrication failure is prolonged or the frequency of abnormal wear is increased, posing a risk of deepening wear intensity. In this case, a moderate alarm can prompt staff to take targeted intervention measures, such as reducing power operation to alleviate bearing load, planning shutdowns to inspect wear marks, etc., to proactively control the rate of wear development.

[0064] Figure 5 A schematic diagram illustrating the logic of a severe alarm implementation according to an embodiment of the present invention is shown. As shown, a severe alarm is triggered if all wear characteristic parameters exceed their respective thresholds. This alarm level is designed for emergency scenarios where the sliding bearing is in a state of continuous and severe wear. The synchronous abnormality of the three parameters indicates a severe imbalance in the friction state. If not handled promptly, it can easily lead to major failures such as bearing seizure and component scrapping. The function of the severe alarm is to force an emergency shutdown operation through the highest level of warning signal, while simultaneously prompting disassembly and inspection of wear and erosion conditions, thereby minimizing the risk of equipment damage and safety hazards.

[0065] In some examples, setting thresholds for wear intensity, wear frequency, and wear width is a prerequisite for implementing graded alarms. Since sliding bearings come in various forms (such as differences in structural design, material combination, and application scenarios), their wear characteristics and corresponding thermoelectric potential signal patterns are different. Therefore, it is necessary to comprehensively determine the thresholds for specific bearing types by combining theoretical calculations, data references, and experimental verification to ensure that the thresholds can accurately reflect the wear state and adapt to the actual operating characteristics of different bearings. Theoretical calculations provide the basis for threshold setting. Based on the thermoelectric properties of sliding bearing materials, frictional heat generation models, and lubrication theories, the theoretical boundary ranges of wear intensity, wear frequency, and wear width under normal operation and abnormal wear conditions are derived from the physical mechanism level, ensuring that the threshold conforms to the wear law of sliding bearings. Data references analyze the long-term operation records of similar or identical sliding bearings, extracting their characteristic parameters under different conditions such as normal operation, light wear, and severe wear, so that the threshold setting is close to actual operating experience and avoids the disconnect between theoretical calculations and engineering practice. Experimental verification simulates operating conditions with different wear intensities, collects corresponding thermoelectric potential signals, and calculates characteristic parameters to verify whether the thresholds derived from theoretical calculations and data can effectively distinguish different wear levels. The thresholds are then fine-tuned and calibrated to ensure that the set thresholds can accurately trigger alarms at all levels, providing a reliable judgment standard for graded early warning of sliding bearing wear.

[0066] In some examples, when a minor alarm is triggered, the relevant test parameters of sliding bearing temperature, lubrication medium condition, speed, and load are checked for abnormalities. By using the status of these auxiliary parameters, it is verified whether the abnormality of a single wear characteristic parameter is due to a real wear hazard, and a basis is provided for whether to strengthen monitoring in the future.

[0067] When a moderate alarm is triggered, reduce power operation and continuously monitor changes in relevant equipment test parameters. After shutdown, inspect the sliding bearing for wear and take maintenance measures. When a moderate alarm is triggered, it means that the wear risk has entered a stage of cumulative aggravation. At this time, it is necessary to reduce the bearing load by reducing power operation, while continuously monitoring changes in relevant equipment test parameters to track the wear development trend. After shutdown, it is necessary to directly inspect the actual wear condition of the sliding bearing, such as whether there are scratches, dents, etc., and take targeted maintenance measures to slow down the wear process and prevent the fault from deteriorating further through proactive intervention.

[0068] When a severe alarm is triggered, an emergency stop is initiated. After shutdown, the sliding bearing is disassembled and inspected to determine if there is any wear or burning. When a severe alarm is triggered, it indicates that the sliding bearing is in an emergency state of continuous and severe wear. Triggering an emergency stop at this time can immediately terminate the dangerous operating condition and prevent major failures such as bearing seizure and component scrapping. Disassembling and inspecting the sliding bearing after shutdown to determine if there is any wear or burning provides a direct basis for fault repair and subsequent preventive measures.

[0069] In some examples, minor and moderate alarms automatically reset after a set time following a sustained drop in wear characteristic parameters below the corresponding threshold. This design prevents persistent alarm states caused by brief parameter fluctuations, ensuring the alarm system can respond promptly and restore the status. Severe alarms, due to the severity of the corresponding faults, require manual reset to ensure that personnel have confirmed the fault has been resolved, preventing accidental restarts of equipment before potential hazards are eliminated, and further ensuring operational safety. Preferably, minor and moderate alarms automatically reset 1 minute after the parameter remains below the corresponding threshold.

[0070] The following describes in detail the specific implementation of the sliding bearing wear monitoring method provided by this invention, using a monitoring scenario of a certain type of engine sliding main bearing as an example:

[0071] 1. Setting the preset threshold

[0072] For the sliding main bearing of this type of engine (shaft material is 40Cr, bearing material is tin-based Babbitt alloy, rated speed is 1066 r / min, rated load is 7280 kW, lubrication method is pressure oil supply), through preliminary theoretical calculations (thermocouple thermoelectric potential formula), historical normal operation data statistics, and abnormal wear test verification, it was determined that:

[0073] The preset threshold for abnormal thermoelectric potential peaks is E0 = 500mV;

[0074] The first threshold for wear intensity is 300mV; the second threshold for wear frequency is 20 times per continuous working interval; and the threshold for wear width is 15%.

[0075] 2. Thermoelectric signal acquisition and characteristic parameter calculation

[0076] The thermoelectric potential signal between the shaft and bearing is collected, with a sampling frequency of 10kHz and a continuous working interval of 1 minute (i.e., n = 10000 × 60 = 600000 sampling points). Abnormal thermoelectric potential peaks are identified: if the thermoelectric potential e at a certain sampling point... i If the value is >500mV, it is considered an outlier. The number of sampling points with consecutive outliers is d. i Calculate wear characteristic parameters:

[0077] Wear strength ;

[0078] Wear width .

[0079] 3. Alarm Triggering and Response

[0080] Mild alarm: If the wear intensity If the wear rate is >300mV, the wear frequency m>20, or the wear width D>15%, a minor alarm will be triggered. At this time, check the bearing temperature (normally ≤80℃), lubricating oil (color should be light yellow, viscosity index ≥100, no obvious metal particles), speed (stable at ±5r / min), load (stable at ±20kW), and other parameters, and strengthen real-time monitoring.

[0081] Moderate alarm: If (wear intensity) >300mV and wear frequency m>20) or (wear intensity) A moderate alarm is triggered if the voltage exceeds 300mV and the wear width D > 15% or if the wear frequency m > 20 and the wear width D > 15%. In this case, reduce the power to 80% of the rated power and continuously monitor parameter changes. After shutdown, disassemble the bearing and check for scratches, dents, or other wear marks. If any are found, replace the lubricating oil and repair the bearing.

[0082] Severe alarm: If the wear intensity A severe alarm is triggered if the voltage exceeds 300mV, the wear frequency m > 20, and the wear width D > 15%. Immediately shut down the machine to prevent bearing seizure. After shutdown, disassemble and inspect the bearing; if wear and burning are found, replace the shaft and bearing assembly.

[0083] 4. Reset Mechanism

[0084] After a mild or moderate alarm, if the corresponding characteristic parameter remains below the threshold for 30 seconds, it will automatically reset after 1 minute; a severe alarm requires manual confirmation and troubleshooting before manual reset.

[0085] As can be easily understood from the above embodiments, the present invention achieves accurate monitoring and early warning of sliding bearing wear through multi-dimensional feature extraction and hierarchical alarm strategy, effectively ensuring the stable operation of rotating machinery.

[0086] Figure 6 A schematic diagram of a sliding bearing wear monitoring system according to an embodiment of the present invention is shown. As shown in the figure, the present invention also provides a sliding bearing wear monitoring system 600 for implementing the aforementioned sliding bearing wear monitoring method. The sliding bearing wear monitoring system 600 includes:

[0087] Data acquisition unit 601 is used to acquire the thermoelectric potential signal between the shaft and the bearing when the sliding bearing is working;

[0088] The identification unit 602 is used to identify abnormal thermoelectric potential peaks in the thermoelectric potential signal. The abnormal thermoelectric potential peak is a continuous range in which the thermoelectric potential value exceeds a preset threshold.

[0089] The extraction unit 603 is used to extract wear characteristic parameters based on the thermoelectric potential signal. The wear characteristic parameters include wear intensity, wear frequency, and wear width. Among them, the wear intensity is the root mean square value of the thermoelectric potential within the continuous working range; the wear frequency is the number of times the abnormal peak of the thermoelectric potential occurs within the continuous working range; and the wear width is the proportion of the duration of the abnormal value of the thermoelectric potential within the continuous working range.

[0090] The threshold setting unit 604 is used to set thresholds for wear intensity, wear frequency, and wear width.

[0091] The alarm trigger condition generation unit 605 is used to generate a combination of alarm trigger conditions based on thresholds for wear intensity, wear frequency and wear width.

[0092] The alarm mechanism generation unit 606 is used to generate a multi-level alarm mechanism based on alarm triggering conditions.

[0093] Figure 7A system block diagram of a sliding bearing wear monitoring device according to an embodiment of the present invention is shown. As shown, the monitoring device 700 may include an internal communication bus 701, a processor 702, a read-only memory (ROM) 703, a random access memory (RAM) 704, and a communication port 705. When applied to a personal computer, the monitoring device 700 may also include a hard disk 706. The internal communication bus 701 enables data communication between the components of the monitoring device 700. The processor 702 can make judgments and issue prompts. In some embodiments, the processor 702 may consist of one or more processors. The communication port 705 enables data communication between the monitoring device 700 and external devices. In some embodiments, the monitoring device 700 can send and receive information and data from a network through the communication port 705. The monitoring device 700 may also include different forms of program storage units and data storage units, such as the hard disk 706, the read-only memory (ROM) 703, and the random access memory (RAM) 704, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 702. Processor 702 executes these instructions to implement the main part of the method. The results processed by processor 702 are transmitted to the user equipment via communication port 705 and displayed on the user interface.

[0094] The above-described sliding bearing wear monitoring method can be implemented as a computer program, stored in hard disk 706, and loaded into processor 702 for execution to implement the sliding bearing wear monitoring method of this application.

[0095] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the aforementioned sliding bearing wear monitoring methods.

[0096] The specific implementation methods and technical effects of the sliding bearing wear monitoring equipment and computer-readable storage medium can be found in the embodiments of the sliding bearing wear monitoring method provided by the present invention, and will not be repeated here.

[0097] This invention provides a sliding bearing wear monitoring method, system, device, and readable storage medium. Its advantages lie in: utilizing three wear characteristic parameters—wear intensity, wear frequency, and wear width—to comprehensively evaluate the wear condition between the shaft and bearing from aspects such as the overall wear state, the frequency of abnormal wear, the duration of lubrication failure, and the resulting wear trend. It covers the entire wear stage based on the different variation patterns of these parameters and formulates a logical combination alarm strategy using parameter thresholds, thereby improving monitoring accuracy, reducing the possibility of false alarms and missed alarms, and enabling earlier detection of potential wear problems.

[0098] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0099] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0100] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0101] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0102] It will be apparent to those skilled in the art that various modifications and variations can be made to the exemplary embodiments described above without departing from the spirit and scope of the invention. Therefore, it is intended that this invention cover modifications and variations falling within the scope of the appended claims and their equivalents.

Claims

1. A method for monitoring wear of sliding bearings, comprising the following steps: Collect the thermoelectric potential signal between the shaft and the bearing when the sliding bearing is working; Identify abnormal thermoelectric potential peaks in the thermoelectric potential signal, wherein the abnormal thermoelectric potential peaks are sustained intervals in which the thermoelectric potential value exceeds a preset threshold. Wear characteristic parameters are extracted based on the thermoelectric potential signal. These wear characteristic parameters include wear intensity, wear frequency, and wear width. The wear intensity is the root mean square value of the thermoelectric potential within the continuous working range of the sliding bearing; the wear frequency is the number of times the abnormal peak of the thermoelectric potential occurs within the continuous working range; the wear width is the proportion of the duration of the abnormal value of the thermoelectric potential within the continuous working range. An alarm trigger condition combination is generated based on the thresholds of wear intensity, wear frequency, and wear width, and a multi-level alarm mechanism is generated based on the alarm trigger conditions.

2. The sliding bearing wear monitoring method as described in claim 1, characterized in that, The wear intensity is the root mean square value of the thermoelectric potential within the continuous operating range of the sliding bearing, and the calculation formula is: ; in, The number of sampling points in one of the continuous working intervals, The thermoelectric potential value at each sampling point; The wear intensity is the root mean square value of the thermoelectric potential of a continuous working range. The formula for calculating the wear width is as follows: ; in, The number of sampling points for one of the aforementioned thermoelectric potential anomaly peaks; Wear frequency refers to the number of times an abnormal thermoelectric potential peak occurs within a continuous operating range.

3. The sliding bearing wear monitoring method as described in claim 1, characterized in that, The preset threshold E0 is set comprehensively based on the material properties of the shaft and bearing, operating conditions, lubrication conditions, and thermoelectric potential data of similar equipment in normal operation and abnormal wear states.

4. The sliding bearing wear monitoring method as described in claim 1, characterized in that, The first threshold, the second threshold, and the third threshold are set to correspond to the wear intensity, the wear frequency, and the wear width, respectively. If any of the wear characteristic parameters exceeds its corresponding threshold, a minor alarm is triggered. If any two of the wear characteristic parameters exceed the corresponding threshold at the same time, a moderate alarm will be triggered. If all of the wear characteristic parameters exceed the corresponding threshold, a severe alarm is triggered.

5. The sliding bearing wear monitoring method as described in claim 4, characterized in that, The thresholds for wear intensity, wear frequency, and wear width are determined through preliminary theoretical calculations, data analysis, and experimental verification for different types of sliding bearings.

6. The sliding bearing wear monitoring method as described in claim 4, characterized in that, When a minor alarm is triggered, check whether the relevant test parameters of sliding bearing temperature, lubrication medium condition, speed, and load are abnormal. When a moderate alarm is triggered, power reduction operation is performed and the changes in relevant test parameters of the equipment are continuously monitored. After shutdown, the wear of the sliding bearing is checked and maintenance measures are taken. When a severe alarm is triggered, an emergency shutdown is initiated. After shutdown, the sliding bearing is disassembled and inspected to determine whether there is any wear or burning.

7. The sliding bearing wear monitoring method as described in claim 4, characterized in that, Mild and moderate alarms will automatically reset within a set time after the wear characteristic parameter remains below the corresponding threshold, while severe alarms require manual reset.

8. A sliding bearing wear monitoring system, used to implement the sliding bearing wear monitoring method as described in claim 1, characterized in that, The sliding bearing wear monitoring system includes: The data acquisition unit is used to acquire the thermoelectric potential signal between the shaft and the bearing when the sliding bearing is working. The identification unit is used to identify abnormal thermoelectric potential peaks in the thermoelectric potential signal, wherein the abnormal thermoelectric potential peaks are the continuous range in which the thermoelectric potential value exceeds a preset threshold. An extraction unit is used to extract wear characteristic parameters based on the thermoelectric potential signal. The wear characteristic parameters include wear intensity, wear frequency, and wear width. The wear intensity is the root mean square value of the thermoelectric potential within the continuous working range of the sliding bearing; the wear frequency is the number of times abnormal thermoelectric potential peaks occur within the continuous working range; and the wear width is the proportion of the duration of abnormal thermoelectric potential values ​​within the continuous working range. A threshold setting unit is used to set the threshold values ​​for wear intensity, wear frequency, and wear width; An alarm trigger condition generation unit is used to generate a combination of alarm trigger conditions based on the thresholds of wear intensity, wear frequency, and wear width. An alarm mechanism generation unit is used to generate a multi-level alarm mechanism based on the alarm triggering conditions.

9. A sliding bearing wear monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the sliding bearing wear monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the sliding bearing wear monitoring method as described in any one of claims 1-7.

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