Monorail crane lubrication on-line monitoring and analyzing system and method

Abstract

The invention relates to the technical field of industrial equipment monitoring, and discloses a monorail crane lubrication on-line monitoring analysis system and method, and the system comprises a sensor module, a data processing and analysis module, a fault early warning module, a lubrication management optimization module, a data communication and display module, an oil path control module, and an electric power and connection module. The sensor module is used for collecting cleanliness, viscosity, water activity, moisture content, density and temperature of lubricating liquid of the hydraulic system of the monorail crane in real time; the sensor module comprises a sensor array, and the sensor array comprises a GYD12 mining intrinsic safety type multi-parameter oil liquid sensor, a temperature sensor, a pressure sensor, a flow sensor and a pollutant sensor. Through the data processing and analyzing module, the system can continuously track the state of lubricating oil, the timeliness of data is ensured, the dependence on manual inspection is greatly reduced, it is ensured that equipment can find and process abnormities in time in the operation process, and large faults caused by small problems are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment monitoring, and in particular to an online monitoring and analysis system and method for lubrication of a monorail crane. Background Art

[0002] The core of a monorail crane lubrication online monitoring and analysis system is to use advanced sensor technology, combined with information technology, to achieve real-time monitoring of lubricant quality and equipment status, so as to prevent equipment failure in advance, extend equipment service life, and improve production efficiency. Although some monitoring systems on the current market have partial monitoring functions, it is often difficult to achieve comprehensive, real-time and intelligent monitoring, especially in multi-parameter monitoring and fault prediction, there is still a large technical gap.

[0003] As a result, it is often difficult for traditional monorail crane equipment to monitor the state changes of lubricating oil in time during operation, especially in terms of key parameters such as temperature, pressure, flow and pollutant concentration. There is a lack of real-time monitoring means. This deficiency easily leads to the failure to detect abnormal lubricating oil status in time, thus causing equipment failure or even shutdown, resulting in production losses. In addition, traditional equipment maintenance methods mainly rely on regular inspections and manual diagnosis, which is not only inefficient, but also easy to miss potential problems, causing small problems to accumulate into major failures;

[0004] In addition, lubrication management issues during equipment operation are also one of the main reasons for frequent failures. The state of the lubricating oil directly affects the wear and operating performance of the equipment. However, due to the lack of real-time lubrication status monitoring, the equipment often continues to operate under poor lubrication conditions, which eventually leads to premature wear of the equipment, increased maintenance costs and downtime. In addition, trend analysis of equipment failures has not been effectively applied in traditional methods, and is usually only handled after the failure occurs. This passive maintenance method not only increases equipment downtime, but also has a negative impact on the stability of the production line. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a monorail crane lubrication online monitoring and analysis system and method, which solves the problem of high manual dependence and inability to detect faults in a timely manner.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a monorail crane lubrication online monitoring and analysis system, including a sensor module, a data processing and analysis module, a fault warning module, a lubrication management optimization module, a data communication and display module, an oil circuit control module and a power and connection module;

[0007] The sensor module is used to collect the cleanliness, viscosity, water activity, moisture content, density and temperature of the lubricating fluid of the monorail crane hydraulic system in real time;

[0008] The sensor module includes a sensor array, which includes a GYD12 mining intrinsically safe multi-parameter oil sensor, a temperature sensor, a pressure sensor, a flow sensor and a pollutant sensor;

[0009] The data processing and analysis module is used to receive, process and analyze the data collected by the sensor module, and automatically diagnose and identify abnormal conditions of the lubricating oil state in combination with intelligent algorithms;

[0010] The data processing and analysis module includes a core processing unit, an intelligent algorithm unit and a display unit;

[0011] The core processing unit receives data from the sensor module and performs preliminary processing on the data;

[0012] The intelligent algorithm unit performs the following operations based on the received temperature, pressure, flow and pollutant concentration data:

[0013] a. Continuous data tracking: Use data processing algorithms to continuously track the status of lubricating oil to ensure the real-time and timeliness of data;

[0014] The data processing algorithm formula is:

[0015] D filtered =α·D current +(1-α)·D previous

[0016] Where D filtered is the filtered lubricating oil parameter data, D current is the original lubricating oil data collected by the current sensor, D previous is the lubricating oil data after filtering at the last moment, α is the filtering coefficient, between 0 and 1, used to smooth the data, and the typical value is 0.1 to 0.3;

[0017] b. Automatic diagnosis: Combined with intelligent algorithms, it automatically analyzes monitoring data and identifies abnormalities in the lubricating oil status;

[0018] The intelligent algorithm uses a lubricant status scoring formula to score the overall lubricant status; an abnormality detection and fault warning formula to detect abnormalities in the lubricant status;

[0019] The lubricating oil condition scoring formula is:

[0020]

[0021] Among them, S is the lubricant condition score, which is a comprehensive value, and w iis the weight coefficient of the i-th lubricant parameter, reflecting the importance of this parameter to the overall lubricant state, D i The filtered data of the i-th lubricating oil parameter at the current moment, D i,nominal is the nominal value of the i-th lubricant parameter, indicating the expected value of the parameter under normal operating conditions, D i,nominal is the normal range of the i-th lubricant parameter, which is used for normalization to eliminate the differences in different parameter units and dimensions, and n is the total number of lubricant parameters involved in the scoring;

[0022] The anomaly detection and fault warning formula is:

[0023]

[0024] W is the warning signal, 1 indicates a fault warning, 0 indicates normal status, S is the lubricating oil status score, S th It is the warning threshold of lubricating oil status score. If it exceeds this value, it means that the lubricating oil status is abnormal. ΔD i is the change of the i-th lubricant parameter within the time interval Δt, T i,th is the change rate threshold of the i-th lubricant parameter. If the rate exceeds this, it indicates that there may be a sudden fault. Δt is the time interval;

[0025] The abnormalities include but are not limited to abnormal increase in temperature, sudden drop in pressure and excessive concentration of pollutants;

[0026] The fault warning module provides fault warning based on historical data analysis and trend prediction, and sends warning information remotely through the network to reduce the downtime caused by sudden equipment failure;

[0027] The lubrication management optimization module is used to monitor data in real time and provide suggestions for lubricant replacement and addition to ensure that the equipment is always in the best lubrication state;

[0028] The data communication and display module is used to transmit the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, and visually display the monitoring data and analysis results through the display screen;

[0029] The oil circuit control module is responsible for controlling the flow direction of the lubricating oil by reducing pressure and throttling, ensuring that the sensor can monitor under stable conditions, and returning the lubricating oil to the return oil pipeline of the hydraulic system;

[0030] The power and connection module is responsible for providing power support for each module of the system and realizing data and power transmission between various components.

[0031] Preferably, the fault warning module includes a historical data analysis unit, a trend prediction unit and a warning notification unit;

[0032] The historical data analysis unit is used to store and analyze historical monitoring data, and to identify potential problems and abnormal trends in equipment operation by comparing historical data with current data;

[0033] The trend prediction unit predicts the possible future change trend of the lubricating oil state through a prediction algorithm based on historical data and current data;

[0034] The prediction algorithm is:

[0035] P i (t) = D i,current +T i ·t

[0036] P i (t) is the predicted value of the i-th lubricant parameter after time t in the future, D i,current is the value of the i-th lubricating oil parameter at the current moment, T i is the rate of change of the i-th lubricant parameter, and t is the future time interval;

[0037] The early warning notification unit is responsible for sending fault early warning information remotely to maintenance personnel through the network when the predicted or current data exceeds a set threshold.

[0038] Preferably, the lubrication management optimization module includes a lubrication status analysis unit and a replacement suggestion unit;

[0039] The lubrication state analysis unit is used to monitor the viscosity, temperature, water content and other parameters of the lubricating oil in real time through an algorithm to analyze whether the state of the lubricating oil is within the optimal range. The algorithm is:

[0040]

[0041] Where L is the lubrication status score, which is used to evaluate the health status of the current lubricant, and w j is the weight coefficient of the jth lubricant parameter, D j is the value of the jth lubricating oil parameter at the current moment (such as viscosity, temperature, etc.), D j,optimal is the optimal value of the jth lubricant parameter, D j,range is the normal range of the jth lubricant parameter, and m is the total number of lubricant parameters involved in the scoring

[0042] The replacement suggestion unit provides suggestions for replacing or adding lubricating oil based on the lubrication status score, ensuring that the lubrication status of the equipment is always maintained within an optimal range.

[0043] Preferably, the data communication and display module includes a communication interface unit and a display unit;

[0044] The communication interface unit transmits the detection data to the main control system or industrial ring network via RS485;

[0045] The device is used to provide an intuitive user interface to display lubricant status, diagnostic results and warning information in real time.

[0046] Preferably, the oil circuit control module includes a pressure reduction control unit, a throttling control unit and a lubricating oil reflux unit;

[0047] The decompression control unit is responsible for decompressing the lubricating oil in the hydraulic system;

[0048] The throttling control unit controls the flow rate of the lubricating oil;

[0049] The oil return unit is used to return the monitored lubricating oil to the oil return pipeline of the hydraulic system through the return pipeline.

[0050] Preferably, the power and connection module includes a power management unit and a connection unit;

[0051] The power management unit is used to provide a stable power supply for various sensors, processors and other electrical components in the system;

[0052] The connection unit ensures data and power transmission between modules in the system by connecting MHYVRP1*4 cables.

[0053] A monorail crane lubrication online monitoring and analysis method comprises the following steps:

[0054] S1. Collecting data through the sensor module: using the sensor array in the sensor module to collect the cleanliness, viscosity, water activity, moisture content, density and temperature data of the lubricating oil in real time;

[0055] S2. Data processing and analysis: The YHC12 mining intrinsically safe oil collection terminal in the data processing and analysis module performs preliminary processing and analysis on the collected data. The processed data is displayed on the display unit of the data processing and analysis module for real-time viewing by operators. Combined with the data collected by the intelligent algorithm unit, the lubricating oil status is automatically diagnosed. When the lubricating oil index exceeds the set threshold, the intelligent algorithm unit issues a fault warning according to the set algorithm model;

[0056] S3, Fault warning and remote notification: When an abnormal situation is detected, the fault warning module will issue a fault warning and send the warning information remotely to the relevant maintenance personnel through the warning notification unit. Then, by analyzing the change trend of historical data and current data, the historical data analysis unit and trend prediction unit will analyze it to predict the potential failure of the equipment and formulate a preventive maintenance plan;

[0057] S4, Lubrication management optimization: The system monitors key indicators of lubricants, such as viscosity, temperature and moisture content, in real time through the lubrication status analysis unit in the lubrication management optimization module. Then, based on the lubrication status scoring results, the replacement suggestion unit will provide suggestions for lubricant replacement or addition;

[0058] S5, data communication and display: Through the data communication and display module, the system transmits the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, and then displays the monitoring data, diagnosis results and early warning information through the display unit;

[0059] S6, oil circuit control: The oil circuit control module is used to reduce pressure and throttle the flow direction of the lubricating oil, ensuring that the lubricating oil enters the sensor for monitoring under appropriate conditions, and the processed lubricating oil is returned to the oil return line of the hydraulic system;

[0060] S7, power connection: Through the power and connection module, provide stable power support for each module in the system, and ensure data and power transmission between modules through the connection unit.

[0061] Preferably, in the step S3, the historical data analysis unit in the fault warning module has a segmented analysis function, and can analyze the lubricating oil status according to different working stages of the equipment.

[0062] Preferably, in step S5, the display unit supports touch operation, allowing the operator to perform system settings and data query directly on the display screen.

[0063] Preferably, in step S6, the oil reflux unit includes a lubricating oil filtering function to remove possible contaminants before the lubricating oil is returned to the hydraulic system.

[0064] The present invention provides a monorail crane lubrication online monitoring and analysis system and method, which has the following beneficial effects:

[0065] 1. Through the intelligent algorithm in the data processing and analysis module, the system can continuously track and intelligently analyze the status of lubricating oil to ensure the timeliness and accuracy of the data. The intelligent algorithm can not only process and analyze lubricating oil data in real time, but also automatically diagnose abnormal lubrication conditions, reducing dependence on manual inspections, ensuring that lubrication problems are discovered and handled in time during equipment operation, and avoiding equipment failures caused by poor lubrication.

[0066] 2. The present invention uses the historical data analysis unit and trend prediction unit of the fault warning module. The system predicts potential lubrication failures based on the collected historical data and real-time data of the lubricating oil. This intelligent lubrication predictive maintenance can identify lubrication problems in advance and reduce equipment downtime caused by lubrication failures, thereby improving equipment utilization. The timely lubrication warning mechanism not only ensures the continuous operation of the equipment, but also reduces the cost of lubrication maintenance.

[0067] 3. The present invention ensures that the equipment is always operating in the best lubrication state through real-time monitoring and intelligent analysis of the lubricating oil status. Intelligent lubrication management can effectively reduce the friction and wear of the equipment and improve the overall performance and operating efficiency of the equipment. In addition, the fault trend analysis function analyzes the historical data of the equipment's lubrication status to identify possible lubrication problems in advance, helping maintenance personnel take preventive measures to avoid serious lubrication failures.

[0068] 4. The present invention ensures that the equipment always operates under the best lubrication state through optimized intelligent lubrication management, reduces product defects caused by poor lubrication, and thus improves the quality of the final product. Good maintenance and management of the intelligent lubrication system not only reduces production losses, but also enhances the reliability and economy of the production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a system module distribution diagram of the present invention;

[0070] Figure 2 It is a schematic diagram of the data processing and analysis module of the present invention;

[0071] Figure 3 This is a schematic diagram of a fault warning module of the present invention;

[0072] Figure 4 It is a schematic diagram of the lubrication management optimization module of the present invention;

[0073] Figure 5 It is a schematic diagram of the data communication and display module of the present invention;

[0074] Figure 6 It is a schematic diagram of the oil circuit control module of the present invention;

[0075] Figure 7 It is a schematic diagram of the power and connection module of the present invention;

[0076] Figure 8 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Embodiment 1:

[0079] Please refer to the attached Figure 1 -Attached Figure 2 , an embodiment of the present invention provides a monorail crane lubrication online monitoring and analysis system, including a sensor module, a data processing and analysis module, a fault warning module, a lubrication management optimization module, a data communication and display module, an oil circuit control module and a power and connection module;

[0080] The sensor module is used to collect the cleanliness, viscosity, water activity, moisture content, density and temperature of the lubricating fluid in the monorail crane hydraulic system in real time;

[0081] The sensor module includes a sensor array, which includes a GYD12 mining intrinsically safe multi-parameter oil sensor, a temperature sensor, a pressure sensor, a flow sensor and a pollutant sensor;

[0082] The data processing and analysis module is used to receive, process and analyze the data collected by the sensor module, and automatically diagnose and identify abnormal conditions of the lubricating oil in combination with intelligent algorithms;

[0083] The data processing and analysis module includes a core processing unit, an intelligent algorithm unit, and a display unit;

[0084] The core processing unit receives data from the sensor module and performs preliminary processing on the data;

[0085] Based on the received temperature, pressure, flow and pollutant concentration data, the intelligent algorithm unit performs the following operations:

[0086] a. Continuous data tracking: Use data processing algorithms to continuously track the status of lubricating oil to ensure the real-time and timeliness of data;

[0087] The data processing algorithm formula is:

[0088] D filtered =α·D current +(1-α)·D previous

[0089] Where D filtered is the filtered lubricating oil parameter data, D current is the original lubricating oil data collected by the current sensor, Dprevious is the lubricating oil data after filtering at the last moment, α is the filtering coefficient, between 0 and 1, used to smooth the data, and the typical value is 0.1 to 0.3;

[0090] b. Automatic diagnosis: Combined with intelligent algorithms, it automatically analyzes monitoring data and identifies abnormalities in the lubricating oil status;

[0091] The intelligent algorithm uses a lubricant status scoring formula to score the overall lubricant status; an anomaly detection and fault warning formula to detect abnormalities in the lubricant status;

[0092] The lubricant condition scoring formula is:

[0093]

[0094] Among them, S is the lubricant condition score, which is a comprehensive value, and w i is the weight coefficient of the i-th lubricant parameter, reflecting the importance of this parameter to the overall lubricant state, D i The filtered data of the i-th lubricating oil parameter at the current moment, D i,nominal is the nominal value of the i-th lubricant parameter, indicating the expected value of the parameter under normal operating conditions, D i,nominal is the normal range of the i-th lubricant parameter, which is used for normalization to eliminate the differences in different parameter units and dimensions, and n is the total number of lubricant parameters involved in the scoring;

[0095] The anomaly detection and fault warning formula is:

[0096]

[0097] W is the warning signal, 1 indicates a fault warning, 0 indicates normal status, S is the lubricating oil status score, S th It is the warning threshold of lubricating oil status score. If it exceeds this value, it means that the lubricating oil status is abnormal. ΔD i is the change of the i-th lubricant parameter within the time interval Δt, T i,th is the change rate threshold of the i-th lubricant parameter. If the rate exceeds this, it indicates that there may be a sudden fault. Δt is the time interval;

[0098] Abnormalities include but are not limited to abnormal increase in temperature, sudden drop in pressure and excessive concentration of pollutants;

[0099] The fault warning module provides fault warning based on historical data analysis and trend prediction, and sends warning information remotely through the network to reduce downtime caused by sudden equipment failures;

[0100] The lubrication management optimization module is used to monitor data in real time and provide suggestions for lubricant replacement and addition to ensure that the equipment is always in the best lubrication state;

[0101] The data communication and display module is used to transmit the processed data to the main control system or industrial ring network through RS485 or Ethernet interface, and visually display the monitoring data and analysis results through the display screen;

[0102] The oil circuit control module is responsible for controlling the flow of lubricating oil by reducing pressure and throttling, ensuring that the sensor can monitor under stable conditions, and returning the lubricating oil to the return oil pipeline of the hydraulic system;

[0103] The power and connection module is responsible for providing power support for each module of the system and realizing data and power transmission between components.

[0104] Specifically, the sensor module includes a sensor array, which includes a GYD12 mining intrinsically safe multi-parameter oil sensor, a temperature sensor, a pressure sensor, a flow sensor and a pollutant sensor, which are used to collect the cleanliness, viscosity, water activity, moisture content, density and temperature data of the lubricating oil in real time. The collected data is preliminarily processed by the core processing unit of the data processing and analysis module, which filters and denoises the data to ensure the accuracy of the data. Subsequently, the data is transmitted to the intelligent algorithm unit, which uses a pre-set algorithm to analyze the data, including continuous data tracking and automatic diagnosis. The intelligent algorithm unit first continuously tracks the state of the lubricating oil through the data processing algorithm formula to ensure the real-time and timeliness of the data. Then, the overall lubricating oil state is scored through the lubricating oil state scoring formula, and the abnormal situation in the lubricating oil state is identified through the abnormal detection and fault warning formula. When the lubricating oil indicators (such as temperature, pressure, flow, pollutant concentration, etc.) exceed the set threshold, a fault warning is issued. The fault warning module further analyzes the current data and historical data based on the historical data analysis and trend prediction functions. The module can identify possible faults in advance and send the early warning information to the relevant maintenance personnel remotely through the early warning notification unit to reduce the downtime caused by sudden equipment failures. At the same time, the lubrication management optimization module monitors the key indicators of the lubricant (such as viscosity, temperature and moisture content) in real time, and provides suggestions for replacing or adding lubricants in combination with the lubrication status score to ensure that the equipment is always in the best lubrication state. The data communication and display module transmits the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, and displays the monitoring data, diagnostic results and early warning information in real time through the display screen for operators to view and make decisions. The oil circuit control module is responsible for reducing pressure and throttling to control the flow direction of the lubricant, ensuring that the lubricant enters the sensor for monitoring under appropriate conditions, and returns the processed lubricant to the return oil pipeline of the hydraulic system. The entire system is powered by the power and connection module, and the connection unit ensures the stability and reliability of data and power transmission between modules. Through the cooperation of the above solutions, the modules realize the comprehensive monitoring and optimization management of the monorail crane hydraulic system, and improve the operation safety and efficiency of the equipment.

[0105] Please refer to the attached Figure 3 -Attached Figure 4 ,The fault warning module includes a historical data analysis unit, a trend prediction unit and a warning notification unit;

[0106] The historical data analysis unit is used to store and analyze historical monitoring data, and to identify potential problems and abnormal trends in equipment operation by comparing historical data with current data;

[0107] The trend prediction unit predicts the possible future change trend of lubricant status through prediction algorithms based on historical data and current data;

[0108] The prediction algorithm is:

[0109] P i (t) = D i,current +T i ·t

[0110] P i (t) is the predicted value of the i-th lubricant parameter after time t in the future, D i,current is the value of the i-th lubricating oil parameter at the current moment, T i is the rate of change of the i-th lubricant parameter, and t is the future time interval;

[0111] The early warning notification unit is responsible for sending fault warning information to maintenance personnel remotely through the network when the prediction or current data exceeds the set threshold;

[0112] The lubrication management optimization module includes a lubrication status analysis unit and a replacement suggestion unit;

[0113] The lubrication status analysis unit is used to monitor the viscosity, temperature, moisture content and other parameters of the lubricating oil in real time through an algorithm to analyze whether the state of the lubricating oil is within the optimal range. The algorithm is:

[0114]

[0115] Where L is the lubrication status score, which is used to evaluate the health status of the current lubricant, and w j is the weight coefficient of the jth lubricant parameter, D j is the value of the jth lubricating oil parameter at the current moment (such as viscosity, temperature, etc.), D j,optimal is the optimal value of the jth lubricant parameter, D j,range is the normal range of the jth lubricant parameter, and m is the total number of lubricant parameters involved in the scoring

[0116] The replacement recommendation unit provides recommendations for lubricant replacement or addition based on the lubrication condition score, ensuring that the lubrication condition of the equipment is always kept within the optimal range.

[0117] Specifically, the historical data analysis unit of the fault warning module first retrieves and loads historical monitoring data from the database, and compares these data with the data currently collected by the sensor module. By analyzing the changes in historical data and current data, potential problems and abnormal trends in equipment operation are identified, such as abnormal increase in lubricating oil temperature or abnormal decrease in pressure. Next, the trend prediction unit calculates the possible change trend of the lubricating oil state in the future based on the results provided by the historical data analysis unit using a prediction algorithm, especially through formula P i (t) = D i,current +T it predicts the value of lubricant parameter P of item i after time t in the future i (t), where D i,current is the current data, T i is the change rate of the i-th lubricant parameter. If the predicted result or current data exceeds the preset safety threshold, the early warning notification unit will immediately trigger an alarm and send the fault warning information remotely to the relevant maintenance personnel through the network so that maintenance measures can be taken in time. At the same time, the lubrication status analysis unit in the lubrication management optimization module monitors the key parameters such as the viscosity, temperature and moisture content of the lubricant in real time, and uses the algorithm to Calculate the lubrication status score L, where the weight of each parameter w j It is set according to its importance to the lubrication effect of the equipment. Based on the scoring results, the replacement recommendation unit determines whether the lubricant needs to be replaced or supplemented, and provides corresponding operational suggestions to ensure that the lubrication status of the equipment is always kept within the optimal range. Through this collaborative work, each unit monitors and optimizes the equipment operation status in real time to ensure that the equipment can perform preventive maintenance in time before potential failures occur, while maintaining the best lubrication effect, extending the service life of the equipment and improving operating efficiency.

[0118] Please refer to the attached Figure 5 -Attached Figure 7 , the data communication and display module includes a communication interface unit and a display unit;

[0119] The communication interface unit transmits the detection data to the main control system or industrial ring network via RS485;

[0120] Used to provide an intuitive user interface to display lubricant status, diagnostic results and warning information in real time;

[0121] The oil circuit control module includes a pressure reducing control unit, a throttling control unit and a lubricating oil return unit;

[0122] The pressure reducing control unit is responsible for reducing the pressure on the lubricating oil in the hydraulic system;

[0123] The throttling control unit controls the flow rate of the lubricating oil;

[0124] The oil return unit is used to return the monitored lubricating oil to the return oil pipeline of the hydraulic system through the return pipeline;

[0125] The power and connection module includes a power management unit and a connection unit;

[0126] The power management unit is used to provide a stable power supply to various sensors, processors and other electrical components in the system;

[0127] The connection unit ensures data and power transmission between modules in the system by connecting MHYVRP1*4 cables.

[0128] Specifically, the communication interface unit of the data communication and display module transmits the detected lubricating oil status data, diagnostic results and warning information to the main control system or industrial ring network through the RS485 interface, thereby realizing remote monitoring and data sharing. The display unit displays this information in real time through an intuitive user interface, which is convenient for operators to monitor the equipment status and provide decision support. In the oil circuit control module, the pressure reduction control unit first reduces the pressure of the lubricating oil in the hydraulic system to ensure that the lubricating oil is in a suitable pressure range before entering the sensor. Then, the throttling control unit controls the flow rate of the lubricating oil so that it flows at a stable speed in the sensor module to ensure the measurement data. Accuracy: The monitored lubricating oil is returned to the oil return pipeline of the hydraulic system through the oil return unit and the return pipeline, ensuring the recycling of lubricating oil and reducing resource waste. The power management unit in the power and connection module is responsible for providing a stable power supply for various sensors, processors and other electrical components in the system to ensure continuous and reliable operation of the system. The connection unit ensures smooth data and power transmission between modules by connecting MHYVRP1*4 cables to achieve effective collaboration between modules. Through the close cooperation of these units, the system can achieve accurate monitoring and real-time response to the status of lubricating oil to ensure efficient and safe operation of the equipment.

[0129] Embodiment 2:

[0130] Please refer to the attached Figure 8 , a monorail crane lubrication online monitoring and analysis method, comprising the following steps:

[0131] S1. Collecting data through the sensor module: using the sensor array in the sensor module to collect the cleanliness, viscosity, water activity, moisture content, density and temperature data of the lubricating oil in real time;

[0132] S2. Data processing and analysis: The YHC12 mining intrinsically safe oil collection terminal in the data processing and analysis module performs preliminary processing and analysis on the collected data. The processed data is displayed on the display unit of the data processing and analysis module for real-time viewing by operators. Combined with the data collected by the intelligent algorithm unit, the lubricating oil status is automatically diagnosed. When the lubricating oil index exceeds the set threshold, the intelligent algorithm unit issues a fault warning according to the set algorithm model;

[0133] S3, Fault warning and remote notification: When an abnormal situation is detected, the fault warning module will issue a fault warning and send the warning information remotely to the relevant maintenance personnel through the warning notification unit. Then, by analyzing the change trend of historical data and current data, the historical data analysis unit and trend prediction unit will analyze it to predict the potential failure of the equipment and formulate a preventive maintenance plan;

[0134] S4, Lubrication management optimization: The system monitors key indicators of lubricants, such as viscosity, temperature and moisture content, in real time through the lubrication status analysis unit in the lubrication management optimization module. Then, based on the lubrication status scoring results, the replacement suggestion unit will provide suggestions for lubricant replacement or addition;

[0135] S5, data communication and display: Through the data communication and display module, the system transmits the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, and then displays the monitoring data, diagnosis results and early warning information through the display unit;

[0136] S6, oil circuit control: The oil circuit control module is used to reduce pressure and throttle the flow direction of the lubricating oil, ensuring that the lubricating oil enters the sensor for monitoring under appropriate conditions, and the processed lubricating oil is returned to the oil return line of the hydraulic system;

[0137] S7, power connection: through the power and connection module, provide stable power support for each module in the system, and ensure data and power transmission between modules through the connection unit;

[0138] In step S3, the historical data analysis unit in the fault warning module has a segmented analysis function, which can analyze the lubricating oil status according to different working stages of the equipment;

[0139] In step S5, the display unit supports touch operation, allowing the operator to perform system settings and data query directly on the display screen;

[0140] In step S6, the oil return unit includes a lubricating oil filtering function to remove possible contaminants before the lubricating oil returns to the hydraulic system.

[0141] Specifically, in step S1, by collecting data through the sensor module, the system can obtain key indicators such as the cleanliness, viscosity, water activity, moisture content, density and temperature of the lubricating oil in real time, ensuring that the equipment is always under monitoring, thereby effectively preventing equipment failures caused by the degradation of lubricating oil performance; in step S2, the data processing and analysis module not only performs preliminary processing and analysis on the collected data, and displays it to the operator in real time through the display unit, but also combines with the intelligent algorithm unit to automatically diagnose the lubricating oil status. When it is detected that the lubricating oil index exceeds the set threshold, the system can issue a fault warning in time. This processing method improves the accuracy of fault detection. The accuracy and response speed of the fault warning module are improved, reducing the possibility of human misjudgment. In step S3, the historical data analysis unit in the fault warning module has a segmented analysis function, which can accurately analyze the lubricating oil status according to the different working stages of the equipment. Through this segmented analysis, the system can identify potential problems in each working stage, especially those abnormalities that will only appear in a certain stage, thereby improving the accuracy of fault warning. At the same time, the warning information is sent to maintenance personnel in a timely manner through the remote notification function to ensure that the equipment can be maintained in time and reduce unexpected downtime. In step S4, the lubrication management optimization module monitors the key indicators of lubricating oil in real time and concludes Based on the lubrication status scoring results, it provides suggestions for lubricant replacement or addition to ensure that the lubrication status of the equipment is always maintained within the optimal range. This not only reduces equipment wear caused by poor lubrication, but also extends the service life of the equipment. In step S5, the data communication and display module not only transmits the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, but also allows operators to directly perform system settings and data queries through a display unit that supports touch operation. This function improves the convenience and efficiency of on-site operations, allowing operators to adjust system settings more quickly and optimize equipment operation. In step S6, the oil In the hydraulic control module, the system precisely controls the flow direction, flow rate and pressure of the lubricating oil to ensure that the lubricating oil enters the sensor for monitoring under appropriate conditions. The lubricating oil filtering function of the oil return unit effectively removes possible contaminants before the lubricating oil returns to the hydraulic system, thereby improving the cleanliness of the hydraulic system and reducing equipment failures caused by contamination. Finally, in step S7, the power and connection module provides stable power support for each module in the system to ensure the reliable operation of the system under various working conditions. At the same time, the connection unit ensures seamless collaboration between modules through efficient power and data transmission, further improving the overall performance of the system.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A monorail crane lubrication online monitoring and analysis system, characterized in that: It includes sensor module, data processing and analysis module, fault warning module, lubrication management optimization module, data communication and display module, oil circuit control module and power and connection module; The sensor module is used to collect the cleanliness, viscosity, water activity, moisture content, density and temperature of the lubricating fluid of the monorail crane hydraulic system in real time; The sensor module includes a sensor array, which includes a GYD12 mining intrinsically safe multi-parameter oil sensor, a temperature sensor, a pressure sensor, a flow sensor and a pollutant sensor; The data processing and analysis module is used to receive, process and analyze the data collected by the sensor module, and automatically diagnose and identify abnormal conditions of the lubricating oil state in combination with intelligent algorithms; The data processing and analysis module includes a core processing unit, an intelligent algorithm unit and a display unit; The core processing unit receives data from the sensor module and performs preliminary processing on the data; The intelligent algorithm unit performs the following operations based on the received temperature, pressure, flow and pollutant concentration data: a. Continuous data tracking: Use data processing algorithms to continuously track the status of lubricating oil to ensure the real-time and timeliness of data; The data processing algorithm formula is: D filtered =α·D current +(1-a)·D previous Where D filtered is the filtered lubricating oil parameter data, D current is the original lubricating oil data collected by the current sensor, D previous is the lubricating oil data after filtering at the last moment, α is the filtering coefficient, between 0 and 1, used to smooth the data, and the typical value is 0.1 to 0.3; b. Automatic diagnosis: Combined with intelligent algorithms, it automatically analyzes monitoring data and identifies abnormalities in the lubricating oil status; The intelligent algorithm uses a lubricant status scoring formula to score the overall lubricant status; an abnormality detection and fault warning formula to detect abnormalities in the lubricant status; The lubricating oil condition scoring formula is: Among them, S is the lubricant condition score, which is a comprehensive value, and w i is the weight coefficient of the i-th lubricant parameter, reflecting the importance of this parameter to the overall lubricant state, D i The filtered data of the i-th lubricating oil parameter at the current moment, D i,nominal is the nominal value of the i-th lubricant parameter, indicating the expected value of the parameter under normal operating conditions, D i,nominal is the normal range of the i-th lubricant parameter, which is used for normalization to eliminate the differences in different parameter units and dimensions, and n is the total number of lubricant parameters involved in the scoring; The anomaly detection and fault warning formula is: W is the warning signal, 1 indicates a fault warning, 0 indicates normal status, S is the lubricating oil status score, S th It is the warning threshold of lubricating oil status score. If it exceeds this value, it means that the lubricating oil status is abnormal. ΔD i is the change of the i-th lubricant parameter within the time interval Δt, T i,th is the change rate threshold of the i-th lubricant parameter. If the rate exceeds this, it indicates that there may be a sudden fault. Δt is the time interval; The abnormalities include but are not limited to abnormal increase in temperature, sudden drop in pressure and excessive concentration of pollutants; The fault warning module provides fault warning based on historical data analysis and trend prediction, and sends warning information remotely through the network to reduce the downtime caused by sudden equipment failure; The lubrication management optimization module is used to monitor data in real time and provide suggestions for lubricant replacement and addition to ensure that the equipment is always in the best lubrication state; The data communication and display module is used to transmit the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, and visually display the monitoring data and analysis results through the display screen; The oil circuit control module is responsible for controlling the flow direction of the lubricating oil by reducing pressure and throttling, ensuring that the sensor can monitor under stable conditions, and returning the lubricating oil to the return oil pipeline of the hydraulic system; The power and connection module is responsible for providing power support for each module of the system and realizing data and power transmission between various components.

2. The monorail crane lubrication online monitoring and analysis system according to claim 1 is characterized in that: The fault warning module includes a historical data analysis unit, a trend prediction unit and a warning notification unit; The historical data analysis unit is used to store and analyze historical monitoring data, and to identify potential problems and abnormal trends in equipment operation by comparing historical data with current data; The trend prediction unit predicts the possible future change trend of the lubricating oil state through a prediction algorithm based on historical data and current data; The prediction algorithm is: P i (t)=D i,current +T i ·t P i (t) is the predicted value of the i-th lubricant parameter after time t in the future, D i,current is the value of the i-th lubricating oil parameter at the current moment, T i is the rate of change of the i-th lubricant parameter, and t is the future time interval; The early warning notification unit is responsible for sending fault early warning information remotely to maintenance personnel through the network when the predicted or current data exceeds a set threshold.

3. The monorail crane lubrication online monitoring and analysis system according to claim 1 is characterized in that: The lubrication management optimization module includes a lubrication status analysis unit and a replacement suggestion unit; The lubrication state analysis unit is used to monitor the viscosity, temperature, water content and other parameters of the lubricating oil in real time through an algorithm to analyze whether the state of the lubricating oil is within the optimal range. The algorithm is: Where L is the lubrication status score, which is used to evaluate the health status of the current lubricant, and w j is the weight coefficient of the jth lubricant parameter, D j is the value of the jth lubricating oil parameter at the current moment (such as viscosity, temperature, etc.), D j,optimal is the optimal value of the jth lubricant parameter, D j,range is the normal range of the jth lubricant parameter, and m is the total number of lubricant parameters involved in the scoring The replacement suggestion unit provides suggestions for replacing or adding lubricating oil based on the lubrication status score, ensuring that the lubrication status of the equipment is always maintained within an optimal range.

4. The monorail crane lubrication online monitoring and analysis system according to claim 1 is characterized in that: The data communication and display module includes a communication interface unit and a display unit; The communication interface unit transmits the detection data to the main control system or industrial ring network via RS485; The device is used to provide an intuitive user interface to display lubricant status, diagnostic results and warning information in real time.

5. The monorail crane lubrication online monitoring and analysis system according to claim 1 is characterized in that: The oil circuit control module includes a pressure reduction control unit, a throttling control unit and a lubricating oil return unit; The decompression control unit is responsible for decompressing the lubricating oil in the hydraulic system; The throttling control unit controls the flow rate of the lubricating oil; The oil return unit is used to return the monitored lubricating oil to the oil return pipeline of the hydraulic system through the return pipeline.

6. The monorail crane lubrication online monitoring and analysis system according to claim 1 is characterized in that: The power and connection module includes a power management unit and a connection unit; The power management unit is used to provide a stable power supply for various sensors, processors and other electrical components in the system; The connection unit ensures data and power transmission between modules in the system by connecting MHYVRP1*4 cables.

7. A monorail crane lubrication online monitoring and analysis method, according to a monorail crane lubrication online monitoring and analysis system according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Collecting data through the sensor module: using the sensor array in the sensor module to collect the cleanliness, viscosity, water activity, moisture content, density and temperature data of the lubricating oil in real time; S2. Data processing and analysis: The YHC12 mining intrinsically safe oil collection terminal in the data processing and analysis module performs preliminary processing and analysis on the collected data. The processed data is displayed on the display unit of the data processing and analysis module for real-time viewing by operators. Combined with the data collected by the intelligent algorithm unit, the lubricating oil status is automatically diagnosed. When the lubricating oil index exceeds the set threshold, the intelligent algorithm unit issues a fault warning according to the set algorithm model; S3, Fault warning and remote notification: When an abnormal situation is detected, the fault warning module will issue a fault warning and send the warning information remotely to the relevant maintenance personnel through the warning notification unit. Then, by analyzing the change trend of historical data and current data, the historical data analysis unit and trend prediction unit will analyze it to predict the potential failure of the equipment and formulate a preventive maintenance plan; S4, Lubrication management optimization: The system monitors key indicators of lubricants, such as viscosity, temperature and moisture content, in real time through the lubrication status analysis unit in the lubrication management optimization module. Then, based on the lubrication status scoring results, the replacement suggestion unit will provide suggestions for lubricant replacement or addition. S5, data communication and display: Through the data communication and display module, the system transmits the processed data to the main control system or industrial ring network through the RS485 or Ethernet interface, and then displays the monitoring data, diagnosis results and early warning information through the display unit; S6, oil circuit control: The oil circuit control module is used to reduce pressure and throttle the flow direction of the lubricating oil, ensuring that the lubricating oil enters the sensor for monitoring under appropriate conditions, and the processed lubricating oil is returned to the oil return line of the hydraulic system; S7, power connection: Through the power and connection module, provide stable power support for each module in the system, and ensure data and power transmission between modules through the connection unit.

8. The monorail crane lubrication online monitoring and analysis method according to claim 7 is characterized in that: In the step S3, the historical data analysis unit in the fault warning module has a segmented analysis function, and can analyze the lubricating oil status according to different working stages of the equipment.

9. The monorail crane lubrication online monitoring and analysis method according to claim 7 is characterized in that: In the step S5, the display unit supports touch operation, allowing the operator to perform system settings and data query directly on the display screen.

10. The monorail crane lubrication online monitoring and analysis method according to claim 7, characterized in that: In the step S6, the oil return unit includes a lubricating oil filtering function to remove possible contaminants before the lubricating oil returns to the hydraulic system.

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