Intelligent monitoring and early warning system for lubricating state of a cart operating mechanism

Abstract

The application discloses a large vehicle operation mechanism lubrication state intelligent monitoring and early warning system and relates to the technical field of lubrication state monitoring.The system comprises a large vehicle data acquisition and preprocessing module, a lubrication abnormality preliminary prediction module, an abnormality condition deep analysis module, an abnormality condition analysis threshold dynamic adjustment module and a hierarchical early warning module.The large vehicle data acquisition and preprocessing module is used for real-time data acquisition.The lubrication abnormality preliminary prediction module preliminarily predicts whether abnormality conditions exist in bearing lubrication.The abnormality condition deep analysis module is used for deep analysis and confirmation of lubrication abnormality conditions.The abnormality condition analysis threshold dynamic adjustment module is used for adaptive adjustment of the threshold in lubrication abnormality condition analysis.The hierarchical early warning module is used for triggering corresponding early warning according to abnormality condition levels.The double analysis mechanism of preliminary prediction and deep analysis is adopted, false abnormality signals are effectively eliminated, and the accuracy of lubrication state judgment is improved.

Description

Technical Field

[0001] This invention relates to the field of lubrication condition monitoring technology, specifically an intelligent monitoring and early warning system for the lubrication condition of a trolley operating mechanism. Background Technology

[0002] Trolley traveling mechanisms are widely used in ports, mines, metallurgy, and other industries. As core equipment operating under heavy loads and continuous conditions, the normal operation of their lubrication systems directly determines the service life and operational safety of the equipment. The lubrication system of trolley traveling mechanisms primarily provides lubrication for critical moving components such as bearings and gears, preventing dry friction, wear, and other malfunctions. Abnormal lubrication conditions, such as insufficient oil, dry friction, or sudden temperature rises, can easily lead to component damage, or even equipment shutdowns and safety accidents, causing significant economic losses if not detected and addressed promptly. Existing lubrication condition monitoring systems for trolley traveling mechanisms generally have certain shortcomings. Current monitoring methods mostly rely on manual inspections or single-parameter monitoring, failing to achieve real-time and accurate monitoring of bearing lubrication conditions. When lubrication abnormalities occur, alarm information is not triggered immediately, especially for bearing dry friction. Rapidly deteriorating faults such as friction and lubrication failure often result in significant bearing wear by the time alarm signals are issued, missing the optimal maintenance window. Existing technologies, some monitoring systems rely solely on a single temperature parameter to determine lubrication status, failing to consider the correlation between temperature change trends, rates of temperature change, and the trolley's operating conditions, leading to high false alarm and missed alarm rates. The lack of joint analysis of key influencing factors such as trolley operating time and working environment prevents secondary confirmation of lubrication anomalies, further reducing the accuracy and timeliness of monitoring and judgment. The inability to combine working duration and environmental factors for joint analysis results in insufficient timely early warning. Some crane lubrication monitoring systems only focus on oil supply status, failing to track and predict the actual operating status of bearings after lubrication in real time, making it difficult to address rapidly deteriorating faults such as dry friction. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism, in order to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring and early warning system for the lubrication status of a trolley operating mechanism, the system comprising a trolley data acquisition and preprocessing module, a preliminary prediction module for lubrication anomalies, a deep analysis module for anomalies, a dynamic adjustment module for anomaly analysis thresholds, and a graded early warning module;

[0005] The trolley data acquisition and preprocessing module is used to acquire in real time the temperature data of the bearings of the trolley running mechanism, the trolley running status parameters, and the characteristic parameters of the working environment, and to preprocess the acquired data.

[0006] The lubrication anomaly prediction module uses the bearing temperature change trend and bearing temperature change rate to make a preliminary prediction of whether there is an abnormality in bearing lubrication.

[0007] The abnormal situation in-depth analysis module is used to perform in-depth analysis and confirmation of lubrication abnormalities by using bearing vibration data and lubricating oil level when abnormalities are detected in the preliminary prediction.

[0008] The abnormal situation analysis threshold dynamic adjustment module is used to adaptively adjust the threshold in the lubrication abnormal situation analysis based on the monitored working time of the trolley, the load of the trolley running mechanism and environmental characteristics data.

[0009] The graded early warning module is used to classify the detected lubrication abnormalities and trigger corresponding early warnings for different abnormal situations.

[0010] Furthermore, according to claim 1, the intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism is characterized in that: the trolley data acquisition and preprocessing module includes a data acquisition unit and a data preprocessing unit; the data acquisition unit is used to acquire in real time the temperature data of the bearings of the trolley running mechanism, the vibration frequency of the bearings, the trolley running status data, and the characteristic parameters of the working environment; the trolley status parameters include the continuous running time, cumulative running time, and load pressure of the trolley; the characteristic parameters of the working environment include the dust concentration and humidity in the environment; the data processing unit is used to denoise the acquired bearing temperature data and bearing vibration data, and to process the acquired data of different dimensions through normalization processing.

[0011] Furthermore, the lubrication anomaly preliminary prediction module is used to analyze the real-time acquired bearing temperature, analyze the bearing temperature change trend and bearing temperature change rate within the preliminary prediction observation period, compare the actual monitored value with the set temperature change rate threshold, and make a preliminary prediction of the bearing lubrication condition; and an interference analysis mechanism is set during the analysis process to analyze and warn of the situation where the collected data is received.

[0012] Furthermore, the duration of the initial prediction observation period is set to t; within the initial prediction observation period, n temperature value samples are taken, and the collected temperatures are recorded as {T1, T2, ..., Tn}; a temperature change rate threshold u0 is set; the temperature change trend and temperature change rate are analyzed within the initial prediction observation period, and the analysis results are as follows:

[0013] If the temperature change shows a non-monotonic increasing trend or the temperature change rate is less than the temperature change rate threshold, it is preliminarily judged that the bearing has no potential lubrication abnormality.

[0014] If the temperature change trend shows a monotonically increasing trend but the temperature change rate is less than the temperature change rate threshold, or the temperature change shows a non-monotonically increasing trend but the temperature change rate is greater than or equal to the temperature change rate threshold, it is determined that the collected data is interfered, and the interference analysis mechanism is triggered; after the interference analysis mechanism is triggered, the temperature change trend and the temperature change rate are analyzed in the next m preliminary prediction observation cycles: if the temperature monitoring results of m consecutive cycles are all that the collected data is interfered, a data interference warning is triggered; if it is determined in the mth preliminary prediction observation cycle that there is no potential lubrication abnormality in the bearing initially judged or there is a potential lubrication abnormality in the bearing initially judged, the interference analysis mechanism is解除, and the analysis result of the mth preliminary prediction observation cycle is used as the standard; when the preliminary abnormal prediction conditions are not sufficient or the preliminary prediction result does not match the in-depth analysis result, the interference analysis mechanism is automatically triggered, achieving the effect of automatically verifying the collected temperature data, further excluding the influence of interfered data on the abnormal judgment result, improving the judgment accuracy of lubrication abnormality, and reducing the false alarm and missed alarm rates of lubrication abnormality;

[0015] If the temperature change shows a monotonically increasing trend and the temperature change rate is greater than or equal to the temperature change rate threshold, it indicates that the bearing has abnormal temperature rise, and it is initially judged that the bearing has potential lubrication abnormality, and the in-depth analysis of lubrication abnormality is triggered; by jointly analyzing the temperature change trend and the temperature change rate, a preliminary prediction of the current lubrication abnormality is carried out, and lubrication abnormality can be quickly captured, such as early signals of situations like oil shortage, dry friction, and sudden high temperature rise, avoiding irreversible damage caused by a few seconds of delay.

[0016] Furthermore, the abnormal condition in-depth analysis module is used to analyze and confirm the lubrication abnormal condition according to the analysis result by jointly analyzing the vibration frequency data of the bearing and the lubricating oil oil pressure, and setting vibration frequency thresholds and lubricating oil oil pressure thresholds for comparative analysis.

[0017] Furthermore, the vibration frequency of the bearing obtained in real time is denoted as w, the lubricating oil oil pressure obtained is denoted as p, and vibration frequency threshold w0 and lubricating oil oil pressure threshold p0 are set; the comparative analysis results of the data collected in real time and the set thresholds are as follows:

[0018] If w < w0 and p ≥ p0, it indicates that both the vibration frequency and the lubricating oil oil pressure of the bearing are within the threshold range, and the delay observation mode is triggered; after the delay observation mode is triggered, continuous x analyses of the vibration frequency and the lubricating oil oil pressure of the bearing are set. If the analysis results of x consecutive times are all w < w0 and p < p0, it is determined that there is no lubrication abnormal condition in the bearing, and it is determined that the collected bearing temperature data is suspected of being distorted, and the interference analysis mechanism is triggered to re-analyze the bearing temperature data;

[0019] If w < w0 and p < p0, it indicates that the bearing vibration frequency is less than the threshold but the lubricating oil pressure is low, and it is determined that there is mild lubrication abnormality in the bearing;

[0020] If w ≥ w0 and p ≥ p0, it indicates that the bearing vibration frequency is high but the lubricating oil pressure is greater than or equal to the threshold, and it is determined that there is moderate lubrication abnormality in the bearing;

[0021] If w ≥ w0 and p < p0, it indicates that both the bearing vibration frequency and the lubricating oil pressure are greater than or equal to the threshold, and it is determined that there is severe lubrication abnormality in the bearing.

[0022] Further, the abnormal condition analysis threshold dynamic adjustment module obtains the threshold dynamic adjustment coefficient by analyzing the monitored data of the working duration of the trolley, the load of the trolley running mechanism, and the characteristic data of the environment; the environmental characteristic data includes the dust concentration and environmental humidity of the environment where the trolley running mechanism works; the obtained threshold adjustment coefficient is divided by the threshold in the lubrication abnormal condition analysis to obtain the adjusted threshold.

[0023] Further, the cumulative working duration of the trolley running mechanism is denoted as a1, the load of the trolley running mechanism is denoted as a2; the load of the trolley running mechanism is denoted as b; the dust concentration of the environment where the trolley running mechanism works is denoted as c, and the environmental humidity is denoted as s; the cumulative working duration, the load of the trolley running mechanism, the dust concentration, and the environmental humidity are weighted and coupled for analysis to obtain the threshold dynamic adjustment coefficient k; calculated according to the following formula: k = f1×a1 + f2×a2 + f3×c + f4×s; the f1, f2, f3, and f4 respectively represent the influence weights of the cumulative working duration, the load of the trolley running mechanism, the dust concentration, and the environmental humidity on the threshold dynamic adjustment coefficient; the obtained threshold dynamic adjustment coefficient is used to adjust the temperature growth rate threshold and the vibration frequency threshold; the adjusted temperature change rate threshold is: u 01 = u0 / k; the adjusted vibration frequency threshold is: w 01 = w0 / k; when the threshold dynamic adjustment coefficient increases, the temperature growth rate threshold and the vibration frequency threshold decrease accordingly; when the load and cumulative running duration of the trolley running mechanism and the dust concentration and humidity in the environment are relatively high, the risk of bearing wear will increase significantly. Therefore, using the threshold dynamic adjustment coefficient obtained from the changes in the dust concentration and humidity in the environment can reduce the risk brought by the actual environment to the bearing lubrication abnormality and improve the adaptability and timeliness of the lubrication abnormality monitoring.

[0024] Furthermore, the graded early warning module is used to take corresponding early warning and handling strategies based on the detected bearing lubrication abnormalities: for minor lubrication abnormalities, it generates an early warning of insufficient lubricating oil pressure to remind staff to carry out equipment maintenance; for moderate lubrication abnormalities, it issues an audible and visual warning to remind staff to take emergency measures; for severe lubrication abnormalities, it immediately triggers the trolley to reduce load or shut down for protection, and simultaneously notifies staff to carry out maintenance.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] This invention, by setting up a preliminary prediction module for bearing lubrication anomalies, combines temperature change trends and temperature change rates for preliminary prediction of current lubrication anomalies. This allows for rapid capture of early signals of lubrication anomalies, avoiding irreversible damage caused by delays. The invention employs a dual analysis mechanism of preliminary prediction and in-depth analysis. It not only performs preliminary predictions based on temperature change trends and rates but also conducts in-depth analysis of bearing vibration frequency and lubricating oil pressure to further confirm the existence of lubrication anomalies, effectively eliminating false anomaly signals and improving the accuracy of lubrication status assessment. Furthermore, this invention includes an interference analysis mechanism that automatically triggers interference when preliminary anomaly prediction conditions are insufficient or when the preliminary prediction results do not match the in-depth analysis results. The interference analysis mechanism automatically verifies the collected temperature data, further eliminating the influence of interference data on the anomaly judgment results, improving the accuracy of lubrication anomaly judgment, and reducing the false alarm and false negative rates of lubrication anomalies. This invention also includes a threshold dynamic adjustment module that jointly analyzes the running time of the trolley, load, and dust concentration and humidity in the environment to obtain a threshold dynamic adjustment coefficient, adaptively adjusting the threshold used for comparative analysis. When dust concentration and humidity are high, the risk of bearing wear increases significantly. Therefore, using the threshold dynamic adjustment coefficient obtained from changes in dust concentration and humidity in the environment can reduce the risk of bearing lubrication anomalies caused by the actual environment, improving the adaptability and timeliness of lubrication anomaly monitoring. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the structure of an intelligent monitoring and early warning system for the lubrication status of a large vehicle running mechanism according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] like Figure 1 As shown, the present invention provides a technical solution, an intelligent monitoring and early warning system for the lubrication status of a trolley operating mechanism, the system including a trolley data acquisition and preprocessing module, a lubrication anomaly preliminary prediction module, an anomaly in-depth analysis module, an anomaly analysis threshold dynamic adjustment module, and a graded early warning module;

[0030] The trolley data acquisition and preprocessing module is used to collect the temperature data of the bearings of the trolley running mechanism, the trolley running status parameters, and the characteristic parameters of the working environment in real time, and to preprocess the collected data.

[0031] The lubrication anomaly prediction module uses the bearing temperature change trend and bearing temperature change rate to make a preliminary prediction of whether there are abnormalities in bearing lubrication.

[0032] The abnormal situation in-depth analysis module is used to conduct in-depth analysis and confirmation of lubrication abnormalities by using bearing vibration data and lubricating oil level when abnormalities are detected in the preliminary prediction.

[0033] The abnormal situation analysis threshold dynamic adjustment module is used to adaptively adjust the threshold in the lubrication abnormal situation analysis based on the monitored working time of the trolley, the load of the trolley running mechanism and environmental characteristics data;

[0034] The graded early warning module is used to classify the detected lubrication abnormalities and trigger corresponding early warnings for different abnormal situations.

[0035] According to claim 1, a trolley running mechanism lubrication status intelligent monitoring and early warning system is characterized in that: the trolley data acquisition and preprocessing module includes a data acquisition unit and a data preprocessing unit; the data acquisition unit is used to acquire in real time the bearing temperature data, bearing vibration frequency, trolley running status data, and characteristic parameters of the working environment of the trolley running mechanism; the trolley status parameters include the continuous running time, cumulative running time, and load pressure of the trolley; the characteristic parameters of the working environment include the dust concentration and ambient humidity in the environment; the data and processing unit is used to denoise the acquired bearing temperature data and bearing vibration data, and to process the acquired data of different dimensions through normalization processing.

[0036] The lubrication anomaly preliminary prediction module is used to analyze the real-time acquired bearing temperature, analyze the bearing temperature change trend and bearing temperature change rate within the preliminary prediction observation period, compare the actual monitored value with the set temperature change rate threshold, and make a preliminary prediction of the bearing lubrication condition; and an interference analysis mechanism is set in the analysis process to analyze and warn of the situation where the collected data is received.

[0037] Set the duration of the preliminary prediction observation period as \(t\); set \(n\) temperature value samplings within the preliminary prediction observation period, and record the collected temperatures as \(\{T_1, T_2, \cdots, T_n\}\); set the temperature change rate threshold \(u_0\); analyze the temperature change trend and temperature change rate within the preliminary prediction observation period, and the analysis results are as follows:

[0038] If the temperature change shows a non-monotonic increasing trend or the temperature change rate is less than the temperature change rate threshold, preliminarily judge that there is no potential lubrication abnormality in the bearing;

[0039] If the temperature change trend shows a monotonic increasing trend but the temperature change rate is less than the temperature change rate threshold, or the temperature change shows a non-monotonic increasing trend but the temperature change rate is greater than or equal to the temperature change rate threshold, it is judged that the collected data is interfered, and then trigger the interference analysis mechanism; after triggering the interference analysis mechanism, analyze the temperature change trend and temperature change rate in the next \(m\) preliminary prediction observation periods: if the temperature monitoring results of \(m\) consecutive periods are all that the collected data is interfered, trigger the data interference warning; if it is judged in the \(m\)th preliminary prediction observation period that there is no potential lubrication abnormality in the bearing preliminarily or there is potential lubrication abnormality in the bearing preliminarily,解除 the interference analysis mechanism, and take the analysis result of the \(m\)th preliminary prediction observation period as the standard;

[0040] If the temperature change shows a monotonic increasing trend and the temperature change rate is greater than or equal to the temperature change rate threshold, it indicates that there is abnormal temperature rise in the bearing, then preliminarily judge that there is potential lubrication abnormality in the bearing, and trigger the in-depth analysis of lubrication abnormality.

[0041] The in-depth analysis module for abnormal situations is used to analyze and confirm the lubrication abnormal situation through the joint analysis of the vibration frequency data and the lubricating oil pressure of the bearing, set the vibration frequency threshold and the lubricating oil pressure threshold for comparative analysis, and analyze according to the analysis results.

[0042] Record the vibration frequency of the bearing obtained in real time as \(w\), record the obtained lubricating oil pressure as \(p\), set the vibration frequency threshold \(w_0\) and the lubricating oil pressure threshold \(p_0\); the comparative analysis results of the data collected in real time and the set thresholds are as follows:

[0043] If \(w < w_0\) and \(p \geq p_0\), it means that both the vibration frequency and the lubricating oil pressure of the bearing are within the threshold range, and then trigger the delay observation mode; after triggering the delay observation mode, set the continuous analysis of the vibration frequency and lubricating oil pressure of the bearing for \(x\) times. If the analysis results of \(x\) consecutive times are all \(w < w_0\) and \(p < p_0\), judge that there is no lubrication abnormal situation in the bearing, and judge that the collected bearing temperature data is suspected of being distorted, and then trigger the interference analysis mechanism to re-analyze the bearing temperature data;

[0044] If w < w0 and p < p0, it indicates that the bearing vibration frequency is less than the threshold but the lubricating oil pressure is on the low side, and it is judged that there is mild lubrication abnormality in the bearing;

[0045] If w ≥ w0 and p ≥ p0, it indicates that the bearing vibration frequency is on the high side but the lubricating oil pressure is greater than or equal to the threshold, and it is judged that there is moderate lubrication abnormality in the bearing;

[0046] If w ≥ w0 and p < p0, it indicates that both the bearing vibration frequency and the lubricating oil pressure are greater than or equal to the threshold, and it is judged that there is severe lubrication abnormality in the bearing.

[0047] The abnormal situation analysis threshold dynamic adjustment module obtains the threshold dynamic adjustment coefficient by analyzing the monitored data of the working duration of the trolley, the load of the trolley running mechanism and the characteristic data of the environment; the environmental characteristic data includes the dust concentration and environmental humidity of the environment where the trolley running mechanism works; the obtained threshold adjustment coefficient is divided by the threshold in the lubrication abnormality analysis to obtain the adjusted threshold.

[0048] Record the cumulative working duration of the trolley running mechanism as a1, and record the load of the trolley running mechanism as a2; record the load of the trolley running mechanism as b; record the dust concentration of the environment where the trolley running mechanism works as c, and record the environmental humidity as s; perform weighted coupling analysis on the cumulative working duration, the load of the trolley running mechanism, the dust concentration and the environmental humidity to obtain the threshold dynamic adjustment coefficient k; calculate according to the following formula: k = f1×a1 + f2×a2 + f3×c + f4×s; f1, f2, f3 and f4 respectively represent the influence weights of the cumulative working duration, the load of the trolley running mechanism, the dust concentration and the environmental humidity on the threshold dynamic adjustment coefficient; use the obtained threshold dynamic adjustment coefficient to adjust the temperature growth rate threshold and the vibration frequency threshold, for example, the adjusted temperature change rate threshold is: u 01 = u0 / k; the adjusted vibration frequency threshold is: w 01 = w0 / k; when the threshold dynamic adjustment coefficient increases, the temperature growth rate threshold and the vibration frequency threshold will be reduced accordingly.

[0049] The hierarchical warning module is used to adopt corresponding warning processing strategies according to the detected bearing lubrication abnormal situation: for mild lubrication abnormality, generate a warning of insufficient lubricating oil pressure to remind the staff to perform equipment maintenance; for moderate lubrication abnormality, issue an audible and visual warning to remind the staff to take emergency measures; for severe lubrication abnormality, immediately trigger the trolley to reduce the load or stop for protection, and simultaneously notify the staff to perform maintenance.

[0050] Example 1: Set the duration of the preliminary prediction observation period to t=2min; set n=4 temperature value samplings within the preliminary prediction observation period, and record the collected temperatures as {T1=35℃, T2=36℃, T3=35℃, T2=37℃}; obtain the temperature change rate u=1℃ / min; set the temperature change rate threshold u0=4℃ / min; if the temperature change shows a non-monotonic increasing trend or the temperature change rate is less than the temperature change rate threshold, it is preliminarily judged that there is no potential lubrication abnormality in the bearing.

[0051] Example 2: Set the duration of the preliminary prediction observation period to t=2min; set n=4 temperature value samplings within the preliminary prediction observation period, and record the collected temperatures as {T1=35℃, T2=38℃, T3=41℃, T2=45℃}; obtain the temperature change rate u=5℃ / min; set the temperature change rate threshold u0=4℃ / min; if the temperature change shows a monotonically increasing trend and the temperature change rate is greater than or equal to the temperature change rate threshold, it indicates that the bearing has abnormal temperature rise, and it is preliminarily judged that the bearing has potential lubrication abnormality, thus triggering lubrication abnormality depth analysis;

[0052] The real-time vibration frequency of the bearing is recorded as w=1000Hz, and the lubricating oil pressure is recorded as p=0.6MPa. The vibration frequency threshold w0=800Hz and the lubricating oil pressure threshold p0=0.3MPa are set. The comparison and analysis results of the real-time data and the set thresholds are as follows: If w≥w0 and p≥p0, it means that the bearing vibration frequency is too high, but the lubricating oil pressure is greater than or equal to the threshold. It is judged that there is a moderate lubrication abnormality in the bearing, and an audible and visual warning is issued to remind the staff to take emergency measures.

[0053] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An intelligent monitoring and early warning system for the lubrication status of a large trolley running mechanism, characterized in that: The system includes a large vehicle data acquisition and preprocessing module, a lubrication anomaly preliminary prediction module, an anomaly in-depth analysis module, an anomaly analysis threshold dynamic adjustment module, and a graded early warning module; The trolley data acquisition and preprocessing module is used to acquire in real time the temperature data of the bearings of the trolley running mechanism, the trolley running status parameters, and the characteristic parameters of the working environment, and to preprocess the acquired data. The lubrication anomaly prediction module uses the bearing temperature change trend and bearing temperature change rate to make a preliminary prediction of whether there is an abnormality in bearing lubrication. The abnormal situation in-depth analysis module is used to perform in-depth analysis and confirmation of lubrication abnormalities by using bearing vibration data and lubricating oil level when abnormalities are detected in the preliminary prediction. The abnormal situation analysis threshold dynamic adjustment module is used to adaptively adjust the threshold in the lubrication abnormal situation analysis based on the monitored working time of the trolley, the load of the trolley running mechanism and environmental characteristic data. The graded early warning module is used to classify the detected lubrication abnormalities and trigger corresponding early warnings for different abnormal situations.

2. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 1, characterized in that: The trolley data acquisition and preprocessing module includes a data acquisition unit and a data preprocessing unit. The data acquisition unit is used to acquire in real time the temperature data of the bearings of the trolley running mechanism, the vibration frequency of the bearings, the trolley running status data, and the characteristic parameters of the working environment. The trolley status parameters include the continuous running time, cumulative running time, and load pressure of the trolley. The characteristic parameters of the working environment include the dust concentration and humidity in the environment. The data processing unit is used to denoise the acquired bearing temperature data and bearing vibration data, and to process the acquired data of different dimensions through normalization processing.

3. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 1, characterized in that: The preliminary prediction module for lubrication anomalies is used to analyze the real-time acquired bearing temperature, analyze the bearing temperature change trend and bearing temperature change rate within the preliminary prediction observation period, compare the actual monitored value with the set temperature change rate threshold, and make a preliminary prediction of the bearing lubrication condition; and an interference analysis mechanism is set in the analysis process to analyze and warn of the situation where the collected data is received.

4. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 3, characterized in that: The initial prediction observation period is set to t; n temperature samples are taken within the initial prediction observation period, and the collected temperatures are recorded as {T1, T2, ..., Tn}; a temperature change rate threshold u0 is set; the temperature change trend and temperature change rate are analyzed within the initial prediction observation period, and the analysis results are as follows: If the temperature change shows a non-monotonic increasing trend or the temperature change rate is less than the temperature change rate threshold, it is preliminarily judged that the bearing has no potential lubrication abnormality. If the temperature change trend shows a monotonically increasing trend but the temperature change rate is less than the temperature change rate threshold, or the temperature change shows a non-monotonically increasing trend but the temperature change rate is greater than or equal to the temperature change rate threshold, it is determined that the collected data is interfered, and the interference analysis mechanism is triggered; after the interference analysis mechanism is triggered, the temperature change trend and the temperature change rate are analyzed in the next m preliminary prediction observation cycles: If the temperature monitoring results of m consecutive cycles are all that the collected data is interfered, a data interference warning is triggered; if it is determined in the mth preliminary prediction observation cycle that there is no potential lubrication abnormality in the preliminary judgment of the bearing or there is a potential lubrication abnormality in the preliminary judgment of the bearing, the interference analysis mechanism is解除, and the analysis result of the mth preliminary prediction observation cycle shall prevail; If the temperature change shows a monotonically increasing trend and the temperature change rate is greater than or equal to the temperature change rate threshold, it indicates that the bearing has abnormal temperature rise, and it is preliminarily judged that the bearing has a potential lubrication abnormality, and the in-depth analysis of lubrication abnormality is triggered.

5. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 1, characterized in that: The abnormal condition in-depth analysis module is used to analyze and confirm the lubrication abnormal condition according to the analysis result by jointly analyzing the vibration frequency data and the lubricating oil oil pressure of the bearing, setting vibration frequency thresholds and lubricating oil oil pressure thresholds for comparative analysis.

6. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 5, characterized in that: The vibration frequency of the bearing obtained in real time is denoted as w, the obtained lubricating oil oil pressure is denoted as p, and the vibration frequency threshold w0 and the lubricating oil oil pressure threshold p0 are set; the comparative analysis results of the data collected in real time and the set thresholds are as follows: If w < w0 and p ≥ p0, it means that both the vibration frequency and the lubricating oil oil pressure of the bearing are within the threshold range, and the delay observation mode is triggered; after the delay observation mode is triggered, x consecutive analyses of the vibration frequency and the lubricating oil oil pressure of the bearing are set. If the analysis results of x consecutive times are all w < w0 and p < p0, it is determined that there is no lubrication abnormality in the bearing, and it is determined that the collected bearing temperature data is suspected of being distorted, and the interference analysis mechanism is triggered to re-analyze the bearing temperature data; If w < w0 and p < p0, it means that the vibration frequency of the bearing is less than the threshold but the lubricating oil oil pressure is low, and it is determined that there is a mild lubrication abnormality in the bearing; If w ≥ w0 and p ≥ p0, it means that the vibration frequency of the bearing is high but the lubricating oil oil pressure is greater than or equal to the threshold, and it is determined that there is a moderate lubrication abnormality in the bearing; If w ≥ w0 and p < p0, it means that both the vibration frequency and the lubricating oil oil pressure of the bearing are greater than or equal to the threshold, and it is determined that there is a severe lubrication abnormal condition in the bearing.

7. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 1, characterized in that: The abnormal condition analysis threshold dynamic adjustment module obtains the threshold dynamic adjustment coefficient by analyzing the monitored working duration data of the trolley, the load of the trolley running mechanism and the characteristic data of the environment; the environmental characteristic data includes the dust concentration and the environmental humidity of the environment where the trolley running mechanism works; the obtained threshold adjustment coefficient is divided by the threshold in the lubrication abnormal condition analysis to obtain the adjusted threshold.

8. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 7, characterized in that: The cumulative working time of the trolley traveling mechanism is denoted as a1, and the load of the trolley traveling mechanism is denoted as a2; the load of the trolley traveling mechanism is denoted as b; the dust concentration of the environment in which the trolley traveling mechanism works is denoted as c, and the ambient humidity is denoted as s; a weighted coupling analysis of the cumulative working time, the load of the trolley traveling mechanism, the dust concentration, and the ambient humidity is performed to obtain the threshold dynamic adjustment coefficient k; the obtained threshold dynamic adjustment coefficient is used to adjust the temperature growth rate threshold and the vibration frequency threshold; the adjusted temperature change rate threshold is: u 01 =u0 / k; where u 01 This represents the adjusted temperature change rate threshold; the adjusted vibration frequency threshold is: w 01 =w0 / k; where w 01 This represents the adjusted vibration frequency threshold; as the threshold dynamic adjustment coefficient increases, the temperature growth rate threshold and the vibration frequency threshold decrease accordingly.

9. The intelligent monitoring and early warning system for the lubrication status of a trolley running mechanism according to claim 1, characterized in that: The graded early warning module is used to take corresponding early warning and handling strategies based on the detected bearing lubrication abnormalities: for minor lubrication abnormalities, it generates an early warning of insufficient lubricating oil pressure to remind staff to carry out equipment maintenance; for moderate lubrication abnormalities, it issues an audible and visual warning to remind staff to take emergency measures; for severe lubrication abnormalities, it immediately triggers the trolley to reduce load or shut down for protection, and simultaneously notifies staff to carry out maintenance.

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