Equipment wear diagnosis control system based on lubrication state monitoring technology

Abstract

The invention relates to the technical field of lubrication monitoring, and discloses an equipment wear diagnosis control system based on a lubrication state monitoring technology, which comprises a monitoring master station, a plurality of monitoring substations, a sensor and a cloud data analysis and display platform, and the monitoring substation is responsible for acquiring the lubrication state and vibration data of the equipment in real time, comparing the lubrication state and vibration data with historical lubrication state data, and generating a lubrication state health coefficient. Through cooperative use of the monitoring master station and the monitoring substations, effective monitoring and classification of the equipment state can be realized according to a threshold value in a lubrication state health coefficient, so that different wear diagnosis control processing strategies can be effectively selected, and when the health coefficient is lower than a preset value, the system can give out an early warning to prevent the wear from being damaged. Maintenance personnel are prompted to pay attention to potential problems of the equipment, then small faults are prevented from being evolved into major faults, the risks of equipment shutdown and maintenance are reduced, and the reliability and safety of the equipment are improved.

Description

Technical Field

[0001] The invention relates to the technical field of lubrication monitoring, and in particular to an equipment wear diagnosis and control system based on lubrication state monitoring technology. Background Art

[0002] With the continuous improvement of industrial automation level, the operating efficiency and reliability of mechanical equipment have become crucial factors in the production process. However, during long-term high-load operation, mechanical equipment will inevitably wear and age, especially the wear of key components will directly affect the normal operation and production efficiency of the equipment. Traditional equipment maintenance methods mostly rely on regular inspections and manual experience judgment, which not only consumes a lot of manpower and material resources, but also makes it difficult to detect potential faults in time, resulting in frequent sudden downtime incidents and huge economic losses to enterprises.

[0003] In order to solve the above problems, a variety of equipment status monitoring systems based on sensor technology have emerged in recent years. These systems collect equipment operation data in real time by installing various sensors, such as vibration, temperature, pressure and other parameters, and use data analysis technology to evaluate the equipment status. However, most of the existing monitoring systems focus on the monitoring of a single parameter, lack a comprehensive assessment of the equipment's lubrication status, and cannot accurately predict equipment wear problems caused by poor lubrication. In addition, the data processing capacity of the existing system is limited, making it difficult to achieve efficient processing and analysis of a large amount of monitoring data, which affects the accuracy and timeliness of fault warnings. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an equipment wear diagnosis and control system based on lubrication status monitoring technology, which solves the problems mentioned in the above background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an equipment wear diagnosis and control system based on lubrication status monitoring technology, including a monitoring main station, multiple monitoring substations, sensors, and a cloud data analysis and display platform. The monitoring main station is used to aggregate and analyze the data of each monitoring substation, and the monitoring substation is responsible for real-time collection of equipment lubrication status and vibration data, and compares it with historical lubrication status data to generate a lubrication status health coefficient.

[0006] Preferably, the monitoring main station adopts a high-performance microcontroller (MCU) as the control core, is equipped with a large-capacity flash memory and a high-speed data conversion chip to ensure the efficiency of data processing and storage, and is configured with a wireless communication module for wireless communication with the monitoring substation and the cloud.

[0007] Preferably, the monitoring substation is installed in the lubrication system of each industrial equipment and is equipped with a variety of sensors, including vibration sensors, viscosity sensors, and cleanliness sensors, which can collect key parameters such as temperature, pressure, flow, cleanliness, acid value and moisture content of the lubricating oil in real time.

[0008] Preferably, the lubrication health coefficient is obtained in the following manner:

[0009] The monitoring substation collects the key parameters of the lubricating oil in real time through the sensor, and then compares the real-time collected data with the historical lubrication status data. In order to ensure the accuracy and consistency of the data, the real-time collected data is preprocessed, including denoising and normalization.

[0010] Preferably, the calculation formula of the lubrication state health coefficient is as follows:

[0011]

[0012] Where (HC) is the lubrication health coefficient, ranging from 0 to 1. The closer the value is to 1, the healthier the lubrication state is.

[0013] (C i ) is the health coefficient of the (i)th key parameter, calculated as: Among them, (V i ) is the (i)th parameter value collected in real time, (L i ) is the lower threshold of the (i)th parameter, (U i ) is the upper threshold of the (i)th parameter;

[0014] (W i ) is the weight of the (i)th parameter, reflecting the importance of this parameter to the overall lubrication state.

[0015] Preferably, the formula for data preprocessing is: Where (X') is the normalized value, (x i ) is the original data value, (m) is the moving average window size (referring to the number of consecutive data points included in the calculation of each moving average), (X min ) and (X max ) are the minimum and maximum values ​​of the denoised data set, respectively.

[0016] Preferably, the monitoring master station selects different wear diagnosis control processing strategies according to the threshold value in the lubrication state health coefficient, specifically:

[0017] When (L i ≤V i ≤U i ), the equipment operates normally and no action is required;

[0018] When (V i <L i ) or (V i >U i ), the parameter value exceeds the normal range, the equipment has potential problems, and an early warning needs to be issued;

[0019] When (V i ) exceeds the set safety threshold (such as (V i <L i -Δ) or (V i >U i +Δ))), it is considered as equipment failure and needs to be shut down for maintenance immediately.

[0020] Preferably, the cloud data analysis and display platform uses a browser and a mobile application (APP) to allow users to remotely browse the operating status of the device.

[0021] Preferably, the system has a distributed architecture, can be flexibly deployed, and is applicable to equipment groups of various sizes and layouts.

[0022] Preferably, the monitoring main station, multiple monitoring substations, sensors, and cloud data analysis and display platform are connected via a wireless communication module to facilitate real-time data transmission.

[0023] The present invention provides an equipment wear diagnosis and control system based on lubrication state monitoring technology. It has the following beneficial effects:

[0024] 1. The present invention uses the calculation formula of the lubrication health coefficient to quantitatively evaluate the lubrication status of the equipment. The value range of the coefficient is between 0 and 1. The closer it is to 1, the healthier the lubrication status is. This provides an intuitive method to judge the lubrication condition of the equipment and helps maintenance personnel better understand the operating status of the equipment.

[0025] 2. The monitoring main station in the present invention realizes effective monitoring and classification of equipment status according to the threshold value in the lubrication status health coefficient, and can effectively select different wear diagnosis control and processing strategies. When the health coefficient is lower than the preset value, the system will issue an early warning to prompt maintenance personnel to pay attention to potential problems of the equipment, thereby avoiding small faults from evolving into major faults, reducing the risk of equipment shutdown and maintenance, and improving the reliability and safety of the equipment.

[0026] 3. The present invention uses an intelligent monitoring and early warning mechanism combined with the flexibility of a distributed architecture to enable users to uniformly manage equipment in different geographical locations, significantly improving the efficiency and scientificity of equipment management. Through real-time data analysis and visual display, users can make decisions quickly, thereby improving overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a schematic diagram of the structure of an equipment wear diagnosis and control system based on lubrication status monitoring technology. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0029] Example:

[0030] Please see attached Figure 1 An embodiment of the present invention provides an equipment wear diagnosis and control system based on lubrication status monitoring technology, including a monitoring main station, multiple monitoring substations, sensors, and a cloud data analysis and display platform. The monitoring main station is used to aggregate and analyze the data of each monitoring substation, and the monitoring substation is responsible for real-time collection of equipment lubrication status and vibration data, and compares it with historical lubrication status data to generate a lubrication status health coefficient.

[0031] The monitoring main station, multiple monitoring substations, sensors, and cloud data analysis and display platforms are connected via wireless communication modules to facilitate real-time data transmission. The monitoring main station uses a high-performance microcontroller to perform data analysis and upload the results to the cloud data analysis platform, ultimately allowing users to remotely monitor equipment status through browsers and mobile applications, thereby achieving comprehensive monitoring of the equipment's lubrication status and fault warning.

[0032] The monitoring main station adopts a high-performance microcontroller (MCU) as the control core, is equipped with a large-capacity flash memory and a high-speed data conversion chip to ensure the efficiency of data processing and storage, and is configured with a wireless communication module for wireless communication with the monitoring substation and the cloud.

[0033] The monitoring substation is installed in the lubrication system of each industrial equipment and is equipped with a variety of sensors, including vibration sensors, viscosity sensors, and cleanliness sensors, which can collect key parameters such as temperature, pressure, flow, cleanliness, acid value and moisture content of the lubricating oil in real time.

[0034] Specifically, the monitoring main station adopts a high-performance microcontroller (MCU) as the control core. Its advantages lie in its powerful data processing capabilities and flexible control logic. The large-capacity flash memory ensures that a large amount of monitoring data can be stored, while the high-speed data conversion chip realizes the rapid acquisition and processing of sensor signals. This efficient data processing capability enables the system to respond to changes in equipment status in real time and perform accurate analysis. In addition, the wireless communication module configured in the monitoring main station can conduct efficient wireless communication with the monitoring substation and the cloud, ensuring timely data transmission and remote monitoring.

[0035] At the same time, monitoring substations are installed in the lubrication systems of various industrial equipment and are equipped with a variety of sensors, including vibration sensors, viscosity sensors and cleanliness sensors. The function of these sensors is to collect key parameters of lubricating oil in real time, such as temperature, pressure, flow, cleanliness, acid value and moisture content. The vibration sensor can monitor the vibration of the equipment during operation and identify potential mechanical failures in time; the viscosity sensor ensures that the lubricating oil is within the appropriate viscosity range to avoid insufficient lubrication or excessive friction caused by too high or too low viscosity; the cleanliness sensor can detect impurities in the lubricating oil to ensure the cleanliness of the lubricating oil, thereby protecting the normal operation of the equipment.

[0036] Through this multi-level monitoring system, comprehensive and real-time monitoring of the equipment's lubrication status can be achieved. The key parameters collected in real time are transmitted to the monitoring main station through the wireless communication module. After analysis and processing, equipment status reports and early warning information can be generated. This timely information feedback mechanism can not only help maintenance personnel quickly identify and solve problems and avoid equipment failures and downtime, but also optimize lubrication management strategies and improve equipment operating efficiency and reliability.

[0037] The lubrication health coefficient is obtained in the following manner:

[0038] The monitoring substation collects the key parameters of the lubricating oil in real time through the sensor, and then compares the real-time collected data with the historical lubrication status data. In order to ensure the accuracy and consistency of the data, the real-time collected data is preprocessed, including denoising and normalization.

[0039] The calculation formula of the lubrication health coefficient is as follows:

[0040]

[0041] Where (HC) is the lubrication health coefficient, ranging from 0 to 1. The closer the value is to 1, the healthier the lubrication state is.

[0042] (C i ) is the health coefficient of the (i)th key parameter, calculated as: Among them, (Vi ) is the (i)th parameter value collected in real time, (L i ) is the lower threshold of the (i)th parameter, (U i ) is the upper threshold of the (i)th parameter;

[0043] (W i ) is the weight of the (i)th parameter, reflecting the importance of this parameter to the overall lubrication state.

[0044] It can be seen from the formula that the value of the lubrication health factor (HC) can be used to judge the lubrication status of the equipment: if (HC≥0.8), the lubrication status is good; if (0.5≤HC<0.8), the lubrication status is general and needs attention; if (HC<0.5), the lubrication status is poor and immediate measures need to be taken. By calculating and interpreting the formula of the lubrication health factor (HC), the lubrication status of the equipment can be effectively evaluated, thereby providing a scientific basis for equipment maintenance. This graded judgment not only improves the safety and reliability of equipment operation, but also effectively reduces maintenance costs and extends the service life of the equipment.

[0045] The formula for data preprocessing is: Where (X') is the normalized value, (x i ) is the original data value, (m) is the moving average window size (referring to the number of consecutive data points included in the calculation of each moving average), (X min ) and (X max ) are the minimum and maximum values ​​of the denoised data set, respectively.

[0046] Specifically, the data preprocessing formula can be used to convert the original data value (x i ) is converted into a standardized range (X'∈[0,1]) to make different data sets or features comparable, and using moving average (window size is m), the formula can effectively smooth the data and reduce the impact of noise, thereby more accurately reflecting the trend of the data. In addition, using the minimum value after denoising (X min ) and the maximum value (X max ) is normalized to ensure that the results are not disturbed by extreme values, which helps to ensure the stability and accuracy of various data collected by the sensor in real time when compared with historical lubrication status data.

[0047] The monitoring master station selects different wear diagnosis control processing strategies according to the threshold value of the lubrication health coefficient, specifically:

[0048] When (L i ≤V i ≤U i ), the equipment operates normally and no action is required;

[0049] When (V i <L i ) or (V i >U i ), the parameter value exceeds the normal range, the equipment has potential problems, and an early warning needs to be issued;

[0050] When (V i ) exceeds the set safety threshold (such as (V i <L i -Δ) or (V i >U i +Δ))), it is considered as equipment failure and needs to be shut down for maintenance immediately.

[0051] Specifically, the system achieves effective monitoring and classification of equipment status by setting clear thresholds. When the equipment is in a normal state, no intervention is required; in the early warning state, the system can issue an alarm in time to prompt further inspection, thereby preventing small problems from turning into major failures; and in the fault state, rapid shutdown and maintenance can prevent equipment damage. Such a strategy not only improves the reliability and safety of the equipment, but also optimizes maintenance decisions, extends the service life of the equipment, and ultimately improves overall operational efficiency.

[0052] The cloud data analysis and display platform uses browsers and mobile applications (APP) to allow users to remotely browse the operating status of the equipment. Users can view the operating status of the equipment in real time. The platform uploads data from the monitoring main station to the cloud, processes it using big data analysis technology, and displays key parameters and trends through visual charts. The intelligent early warning function ensures that users are notified in time when the equipment status is abnormal, helping them to respond and make decisions quickly, which not only improves the efficiency and scientificity of equipment management, but also reduces the risk of failure and optimizes maintenance strategies.

[0053] The system has a distributed architecture and can be flexibly deployed. It is suitable for equipment groups of various sizes and layouts. The monitoring substation equipment is dispersed in multiple locations, and their respective operating data is collected through edge computing nodes. The data is then aggregated to the cloud for centralized analysis. This architecture not only improves the speed and real-time performance of data processing, but also enhances the reliability and scalability of the system. It can dynamically adjust resource allocation according to actual needs. Through distributed deployment, users can achieve unified monitoring on devices in different geographical locations, optimize the equipment management process, reduce maintenance costs, and improve the overall flexibility and responsiveness of the system, thereby better supporting various application scenarios.

[0054] 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. An equipment wear diagnosis and control system based on lubrication status monitoring technology, characterized in that: It includes a monitoring main station, multiple monitoring substations, sensors, and a cloud data analysis and display platform. The monitoring main station is used to aggregate and analyze the data from each monitoring substation, and the monitoring substation is responsible for collecting equipment lubrication status and vibration data in real time, and comparing it with historical lubrication status data to generate a lubrication status health coefficient.

2. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The monitoring main station adopts a high-performance microcontroller MCU as the control core, is equipped with a large-capacity flash memory and a high-speed data conversion chip to ensure the efficiency of data processing and storage, and is configured with a wireless communication module for wireless communication with the monitoring substation and the cloud.

3. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The monitoring substation is installed in the lubrication system of each industrial equipment and is equipped with a variety of sensors, including vibration sensors, viscosity sensors, and cleanliness sensors, which collect key parameters such as temperature, pressure, flow, cleanliness, acid value and moisture content of the lubricating oil in real time.

4. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The lubrication health coefficient is obtained in the following manner: The monitoring substation collects the key parameters of the lubricating oil in real time through the sensor, and then compares the real-time collected data with the historical lubrication status data. In order to ensure the accuracy and consistency of the data, the real-time collected data is preprocessed, including denoising and normalization.

5. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The calculation formula of the lubrication health coefficient is as follows: In the formula, HC is the lubrication health coefficient, ranging from 0 to 1. The closer the value is to 1, the healthier the lubrication state is. C i is the health coefficient of the i-th key parameter, calculated as: Among them, V i is the ith parameter value collected in real time, L i is the lower threshold of the i-th parameter, U i is the upper threshold of the i-th parameter; W i is the weight of the i-th parameter, reflecting the importance of this parameter to the overall lubrication state.

6. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The formula for data preprocessing is: In the formula, X' is the normalized value, x i is the original data value, m is the moving average window size (referring to the number of consecutive data points included in calculating each moving average), X min and X max are the minimum and maximum values ​​of the denoised data set, respectively.

7. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The monitoring master station selects different wear diagnosis control processing strategies according to the threshold value of the lubrication health coefficient, specifically: When L i ≤V i ≤U i When the device is operating normally, no action is required; When V i <L i or V i >U i When the parameter value exceeds the normal range, there is a potential problem with the equipment and an early warning needs to be issued; When V i Exceeds the set safety threshold: such as V i <L i -Δ or V i >U i +Δ, it is considered as equipment failure and needs to be shut down for maintenance immediately.

8. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The cloud data analysis and display platform uses a browser and a mobile application APP to allow users to remotely browse the operating status of the device.

9. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1 is characterized in that: The system has a distributed architecture, can be flexibly deployed, and is suitable for equipment groups of various sizes and layouts.

10. The equipment wear diagnosis and control system based on lubrication state monitoring technology according to claim 1, characterized in that: The monitoring main station, multiple monitoring substations, sensors, and cloud data analysis and display platform are connected via wireless communication modules to facilitate real-time data transmission.

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