Equipment intelligent diagnosis and maintenance management system

Abstract

The present invention discloses an equipment intelligent diagnosis and maintenance management system, comprising a data acquisition module (10), a data analysis module (20) and a business application module (30), wherein the data acquisition module is used to acquire on-site equipment data and push the acquired equipment data to the data analysis module; the data analysis module is used to analyze and diagnose the equipment status according to the equipment data pushed by the data acquisition module, and push the equipment status diagnosis result to the business application module; the business application module is used to set business projects for different equipment status diagnosis results, and start the business process of implementing related business projects according to the equipment status diagnosis result pushed by the data analysis module. The maintenance management system reduces the manpower cost in equipment monitoring and maintenance work, improves the intelligent level of equipment abnormal condition diagnosis, and makes equipment maintenance management work more accurate, fast and efficient.

Description

Technical Field

[0001] The present invention relates to equipment information management technology, and in particular to an equipment intelligent diagnosis and maintenance management system. Background Art

[0002] For large-scale production factories, daily inspection and maintenance of equipment is a very important task. Only by ensuring the normal and stable operation of the equipment can the stability of factory production be guaranteed. However, regular inspection and maintenance work is also very tedious.

[0003] Take a steel plant as an example. There are a large number of fan equipment in the steel plant that need maintenance and management. The fan equipment is distributed in different locations within the plant. At present, equipment maintenance management personnel are required to regularly go to the site for inspection. Therefore, if all fan equipment are to be inspected, it will consume a lot of manpower. Even so, it may not be possible to inspect all equipment abnormalities. The maintenance method of relying solely on manpower to inspect fan equipment has low work efficiency and can no longer meet the needs of high-paced production in modern factories.

[0004] In order to solve the above problems and improve the efficiency of daily equipment inspection and maintenance, some patent cases have proposed some technical solutions using sensors and networks to achieve the purpose of remote monitoring equipment, such as the Chinese patent (CN107727137A) an online monitoring system for mining ventilators, which discloses: multiple sensors are provided, which are respectively connected to mining ventilators, multiple sensors are connected to the processor via power lines, and the display is connected to the processor to display the signals transmitted from various sensors to the processor; each sensor is connected to a wireless carrier repeater, the wireless carrier repeater is connected to the receiving module wireless data, and the receiving module is connected to the processor. However, in such a solution, only a simple threshold judgment alarm for equipment data is realized, which is an alarm for equipment that has already abnormal, but it cannot realize the early warning of equipment that has not abnormal but is about to abnormal. In addition, there is no comprehensive management function for alarm information in the above solution, which cannot cope with equipment maintenance work during fast-paced and high-efficiency production. For example, during the daily inspection and maintenance of equipment, it is necessary to manually record the equipment status. This work must be done, but it is also very cumbersome. When the equipment has an abnormal condition and needs maintenance, the establishment, implementation, acceptance and other work of the maintenance and other business projects also need to be manually logged in. The process is also very cumbersome and is not conducive to improving work efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent equipment diagnosis and maintenance management system. The maintenance management system of the present invention is provided with a data acquisition module, a data analysis module and a business application module, thereby reducing the manpower cost in equipment monitoring and maintenance work, improving the intelligence level of equipment abnormal condition diagnosis, and making equipment maintenance management work more accurate, fast and efficient.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] An equipment intelligent diagnosis and maintenance management system includes a data acquisition module, a data analysis module and a business application module, wherein the data acquisition module is data connected to the data analysis module, and the data analysis module is data connected to the business application module;

[0008] The data acquisition module is used to collect on-site equipment data and push the collected equipment data to the data analysis module;

[0009] The data analysis module is used to analyze and diagnose the equipment status according to the equipment data pushed by the data acquisition module, and push the equipment status diagnosis result to the business application module;

[0010] The business application module is used to set business items according to different equipment condition diagnosis results, and start the business process of implementing related business items according to the equipment condition diagnosis results pushed by the data analysis module.

[0011] Further, the data acquisition module includes a regional server and a signal receiving unit, the regional server is data connected to the data analysis module, the regional server is data connected to the signal receiving unit, the regional server is data connected to the on-site production computer, and the signal receiving unit is connected to the sensor electrical signal of the on-site equipment;

[0012] The signal receiving unit is used to collect electrical signals from field device sensors and convert the electrical signals into device status data to transmit to the regional server;

[0013] The regional server is used to collect equipment parameter data through on-site production computers;

[0014] The device status data and the device parameter data together constitute the device data;

[0015] The regional server is also used to push the device data to the data analysis module.

[0016] Furthermore, the regional server is provided with an equipment status early warning model, which can perform preliminary analysis on equipment data and issue early warning information for abnormal conditions of equipment;

[0017] The regional server is also used to push the warning information and the equipment data when the warning occurs to the data analysis module.

[0018] Furthermore, the equipment status warning model includes threshold alarm, multiple rule warning, comprehensive warning and warning based on machine learning model.

[0019] Further, the data analysis module includes a data processing submodule and a diagnosis submodule, the data processing submodule is data connected to the diagnosis submodule, the data processing submodule is data connected to the data acquisition module, and the diagnosis submodule is data connected to the business application module;

[0020] The data processing submodule is used to receive the device data pushed by the data acquisition module, and store, analyze and calculate the device data;

[0021] The diagnosis submodule is used to perform equipment fault diagnosis based on the equipment data in the data processing submodule, and push the diagnosis result to the business application module.

[0022] Furthermore, the data analysis module also includes a human-computer interaction submodule, which is data-connected to the diagnosis submodule, and is used to implement the interaction function between the operator and the data processing submodule and the diagnosis submodule, and to display the diagnosis results.

[0023] Furthermore, the interaction functions between the operator and the data processing submodule and the diagnosis submodule implemented by the human-computer interaction submodule include: area management, gateway configuration, device management, view management, early warning model configuration, data analysis, diagnostic report, process tracking, statistical analysis and data output interface.

[0024] Furthermore, the business application module is also used to push relevant information of the business process to the mobile phone of the person associated with the device.

[0025] Furthermore, the business items set in the business application module are editable and modifiable.

[0026] Furthermore, the maintenance management system is used to perform maintenance management on wind turbine equipment.

[0027] The maintenance management system of the present invention is used for the maintenance and management of on-site equipment, especially for the maintenance and management of on-site fan equipment. Compared with the prior art, the maintenance management system of the present invention has the beneficial effect that the maintenance management system of the present invention is divided into a three-layer module structure as a whole. The first layer is a data acquisition module, which can autonomously collect on-site equipment data, so that the equipment inspection work that originally required a lot of manpower to complete can be automatically completed by the machine, thereby greatly reducing the labor cost; the second layer is a data analysis module, which can autonomously analyze the equipment data and diagnose the abnormal equipment status, thereby realizing intelligent equipment abnormality diagnosis, so that maintenance management personnel can be informed of abnormal equipment status in time; the third layer is a business application module, which can start the corresponding business process for various abnormal equipment conditions, so that the equipment maintenance management work is more accurate, fast and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a structural schematic diagram of the equipment intelligent diagnosis and maintenance management system of the present invention;

[0029] Figure 2 for Figure 1 The structural diagram of the data acquisition module;

[0030] Figure 3 for Figure 1 Schematic diagram of the structure of the data analysis module.

[0031] In the figure: 10-data acquisition module, 11-regional server, 12-signal receiving unit, 20-data analysis module, 21-data processing submodule, 22-diagnosis submodule, 23-human-computer interaction submodule, 30-business application module, 4-on-site production computer. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0033] The implementation background of this embodiment is a certain steel plant, and the fan equipment in various areas of the steel plant needs to be uniformly maintained and managed.

[0034] See also Figures 1 to 3 This embodiment is an intelligent equipment diagnosis and maintenance management system. The main function of this maintenance management system is to realize remote centralized monitoring of the equipment status on site, automatically analyze and diagnose the equipment status, and start relevant business processes according to the diagnosis results. In this embodiment, the object equipment monitored and managed by the maintenance management system is the fan equipment. In other embodiments according to the present invention, the maintenance management system of the present invention can also be used to monitor and manage other types of equipment.

[0035] See also Figure 1The maintenance management system of this embodiment includes a data acquisition module 10, a data analysis module 20 and a business application module 30. The data acquisition module 10 is data-connected to the data analysis module 20, and the data analysis module 20 is data-connected to the business application module 30.

[0036] The data acquisition module 10 is used to collect on-site fan equipment data and push the collected fan equipment data to the data analysis module 20; in the present embodiment, there are three data acquisition modules 10, and the three data acquisition modules 10 are respectively for the three areas of the steel mill, blast furnace plant and coke oven plant, and each data acquisition module 10 collects the on-site fan equipment data in its respective area; in other embodiments of the present invention, the specific number of data acquisition modules 10 can be determined according to needs. Generally speaking, one data acquisition module 10 is configured for a relatively independent area.

[0037] The data analysis module 20 is used to analyze and diagnose the equipment status according to the wind turbine equipment data pushed by the data acquisition module 10, and push the equipment status diagnosis result to the business application module 30;

[0038] The business application module 30 is used to set business items according to different wind turbine equipment condition diagnosis results, and start the business process of implementing related business items according to the wind turbine equipment condition diagnosis results pushed by the data analysis module 20.

[0039] In this embodiment, the business application module 30 is constructed by a server with an EQMS system (equipment management system) installed. In the EQMS, corresponding business items are compiled for various equipment inspection and diagnosis results. When the EQMS system receives the wind turbine equipment diagnosis results pushed by the data analysis module 20, the EQMS system will automatically match the corresponding business items and start the business process of the business item. The business items in the EQMS are all editable and modifiable, so that it is convenient to improve the business process at any time, and then gradually improve the equipment maintenance effect. When the business process is started, the EQMS can also push the diagnosis results of the equipment abnormality to the maintenance manager related to the equipment. Specifically, the server on which the EQMS is installed is connected to the mobile communication network, and the EQMS can push the diagnosis results of the equipment abnormality to the maintenance manager's mobile phone through the mobile communication network. The push form includes text messages and mobile phone APP.

[0040] See also Figure 2More specifically, the data acquisition module 10 includes a regional server 11 and a signal receiving unit 12. The regional server 11 is data-connected to the data analysis module 20, the regional server 11 is data-connected to the signal receiving unit 12, and the regional server 11 is data-connected to the on-site production computer 4. The regional server 11 and the on-site production computer 4 are usually connected in an ixcom (Baoxin communication middleware) communication mode, and the signal receiving unit 12 is connected to the sensor electrical signal of the on-site wind turbine equipment. In this embodiment, the signal receiving unit 12 is an electrical cabinet for collecting sensor signals. The data connection between the regional server 11 and the signal receiving unit 12 can be a wired connection, such as an optical cable, or a wireless connection, such as WIFI or a mobile 4G network, etc.

[0041] The signal receiving unit 12 is used to collect electrical signals from on-site fan equipment sensors and convert the electrical signals into fan equipment status data and transmit them to the regional server 11; the sensors include vibration, temperature and other sensors, and the fan equipment status data includes vibration, temperature and other data.

[0042] The regional server 11 is used to collect parameter data of the fan equipment through the on-site production computer 4, including parameter data such as vibration, temperature, rotation speed, air pressure, air flow and current of the fan.

[0043] The fan equipment status data and the fan equipment parameter data together constitute the equipment data.

[0044] The regional server 11 is further configured to push the device data to the data analysis module 20 .

[0045] Generally speaking, the regional server 11 is mainly responsible for all data preprocessing, status warning, data forwarding, short-term data storage, data storage, analysis, and also includes collection software integration, warning, configuration, processing, etc. in the corresponding area.

[0046] For further optimization, the regional server 11 is provided with an equipment status early warning model, which can perform a preliminary analysis on the wind turbine equipment data and issue early warning information for abnormal conditions of the wind turbine equipment.

[0047] The regional server 11 is further used to push the warning information and the wind turbine equipment data when the warning occurs to the data analysis module 20.

[0048] The purpose of setting up the equipment status warning model is to reduce the workload of the data analysis module 20. Generally speaking, the data acquisition module 10 has a high acquisition frequency and a large amount of data processing. In addition, many fan devices always have some minor abnormalities during operation, such as increased vibration, excessive temperature, low wind pressure, etc., and the occurrence of these minor abnormalities will not have much impact on the normal operation of the fan. If all the data of the fan equipment are pushed to the data analysis module 20 and the data analysis module 20 is responsible for the analysis of all the data, this will inevitably cause a huge burden on the data analysis module 20, which will greatly reduce the work efficiency of the data analysis module 20 and will not be conducive to the further expansion of the monitoring and diagnosis projects of the data analysis module 20 in the future. In the regional server 11 of the data acquisition module 10, an equipment status warning model is set. The equipment status warning model can first perform a preliminary analysis and investigation on all wind turbine equipment data. When a major abnormality is found in the equipment, a warning message is issued. Then the regional server 11 pushes the warning message and the equipment data when the warning occurs to the data analysis module 20. That is to say, only when a major abnormality occurs in the wind turbine equipment, the regional server 11 will push the warning message and the equipment data when the warning occurs to the data analysis module 20, thereby reducing the workload of the data analysis module 20 and allowing it to focus only on the diagnosis of abnormal equipment conditions, thereby improving the overall operating efficiency of the maintenance management system.

[0049] The equipment status warning model specifically includes threshold warning, multiple rule warning, comprehensive warning and warning based on machine learning model, and its specific implementation is as follows:

[0050] Threshold alarm: Early warning is issued by setting thresholds for the status data of each device and each monitoring point.

[0051] Multiple rule warnings: Combining expert experience, we design a more comprehensive and complex rule warning system that combines multiple rules and issues warnings.

[0052] Comprehensive early warning: Combine the rules with equipment status data and parameter data, design an early warning model associated with the working conditions, and comprehensively consider the early warning of various operating indicators of the design system.

[0053] Early warning based on machine learning models: Use historically confirmed fault data as cases, use machine learning methods to train algorithm models, detect anomalies in actual production operation data, and issue early warnings.

[0054] In addition, the equipment condition early warning model is editable, that is, as the condition of the wind turbine equipment changes, the early warning rules in the equipment condition early warning model can be modified.

[0055] See also Figure 3More specifically, the data analysis module 20 includes a data processing submodule 21 and a diagnosis submodule 22. The data processing submodule 21 and the diagnosis submodule 22 are data connected, the data processing submodule 21 is data connected to the data acquisition module 10, and the diagnosis submodule 22 is data connected to the business application module 30. In this embodiment, the data analysis module 20 is constructed by one server, and the data processing submodule 21 and the diagnosis submodule 22 are two functional units virtually constructed on the one server. However, in other embodiments, if conditions permit, two servers can also be used to respectively construct the data processing submodule 21 and the diagnosis submodule 22 to improve the operating effect.

[0056] The data processing submodule 21 is used to receive the wind turbine equipment data pushed by the data acquisition module 10, and store, analyze and calculate the wind turbine equipment data;

[0057] The diagnosis submodule 22 is provided with an equipment abnormality diagnosis model. The data processing submodule 21 can perform equipment fault diagnosis on the wind turbine equipment data in the data processing submodule 21 through the equipment abnormality diagnosis model, and push the diagnosis result to the business application module 30 .

[0058] An equipment abnormality diagnosis model is set in the diagnosis submodule 22. Different from the equipment condition warning model, the equipment condition warning model only performs a simple check on the equipment data, such as threshold judgment, so it takes less time and can quickly check a large amount of equipment data. The equipment abnormality diagnosis model usually further diagnoses the equipment data for which the equipment condition warning model has issued a warning. The algorithm used in the diagnosis process is relatively complex and time-consuming, and the diagnosis result of the equipment abnormality is more comprehensive and accurate. The equipment condition warning model and the equipment abnormality diagnosis model cooperate with each other, so a large amount of equipment data can be quickly, efficiently and accurately checked and diagnosed, thereby realizing accurate judgment and positioning of faulty fan equipment.

[0059] The equipment abnormality diagnosis model specifically includes: intelligent diagnosis, collaborative diagnosis, knowledge extraction and other functions. Among them, the intelligent diagnosis includes diagnostic models such as impeller imbalance, unit misalignment, mechanical looseness, and rolling bearing damage; the collaborative diagnosis is a diagnosis based on autonomous diagnosis combined with multi-professional comprehensive analysis and remote assistance from an expert technical team; the knowledge extraction refers to the automatic extraction of quantitative key features from multi-dimensional data information, thereby establishing fault diagnosis rules and models.

[0060] Optimally, the data analysis module 20 also includes a human-computer interaction submodule 23, which is data-connected to the diagnosis submodule 22. The human-computer interaction submodule 23 is used to implement the interaction function between the operator and the data processing submodule 21 and the diagnosis submodule 22, and to display the diagnosis results.

[0061] The interaction functions between the operator and the data processing submodule 21 and the diagnosis submodule 22 implemented by the human-computer interaction submodule 23 include: area management, gateway configuration, device management, view management, early warning model configuration, data analysis, diagnosis report, process tracking, statistical analysis and data output interface.

[0062] The following are the details of each interactive function:

[0063] 1) Regional management: The regional management module is mainly used for the management of equipment areas and standardizes the regional division of equipment. After the data of this project is connected to the system, the regional management plans and manages the access equipment data to facilitate the classification and display of equipment.

[0064] 2) View management: The view is the system's display page configuration management module for access devices. It is designed according to the physical structure of the access device, draws the display picture of the device, and groups the display according to the device access data information, locates the display area, position and other settings.

[0065] 3) Equipment management: Equipment management is the system's maintenance and management module for access equipment information. It manages the basic information of access equipment, including equipment attribute information such as rated power, speed, etc.

[0066] 4) Diagnostic model configuration: According to the type of access equipment and the professional status data detected, the status warning model access that meets the system embedding is configured. Based on the configuration information, the system can automatically perform status warning, fault diagnosis and other automated processing on the collected data.

[0067] 5) Data Analysis: Data analysis provides a data analysis tool set for professional technicians. The module configures corresponding analysis tools based on the different professional data types connected to the device for use by technicians.

[0068] 6) Diagnostic report: The system automatically issues diagnostic report information, and also provides professional technicians with report review and calibration functions, and also provides technicians with a functional module for making diagnostic reports.

[0069] 7) Process tracking: Process tracking can form a tracking list of alarm events occurring in the equipment, clarify the data flow history, and track the data processing process.

[0070] 8) Statistical analysis: Provide users with statistical analysis tools for collected data information, alarm information, diagnostic information, on-site feedback information, etc., so that users can summarize the regional equipment status management situation.

[0071] 9) Data output interface: The data output interface realizes the data communication function with the EQMS (equipment management system), maintenance management system and other external systems. According to the agreed rules, it accepts the data information transmitted by the external system. At the same time, it also pushes various status data to various external systems according to the agreement and triggers the business processing flow of the system.

[0072] When the maintenance management system of this embodiment monitors the on-site fan equipment, the data acquisition module 10 can independently collect the on-site fan equipment data, so that the equipment inspection work that originally required a lot of manpower to complete can be automatically completed by the machine, and basic judgment can be made on the equipment status, thereby greatly reducing the labor cost; the data analysis module 20 can independently analyze the abnormal equipment data of the fan and diagnose the abnormal equipment status, thereby realizing intelligent equipment abnormality diagnosis, so that maintenance management personnel can be aware of the abnormal equipment status in time; the business application module 30 can start the corresponding business process for various abnormal equipment conditions, so as to make the equipment maintenance management work more accurate, fast and efficient.

[0073] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent equipment diagnosis and maintenance management system, characterized by: It comprises a data collection module (10), a data analysis module (20) and a business application module (30), wherein the data collection module (10) and the data analysis module (20) are data connected, and the data analysis module (20) and the business application module (30) are data connected; The data acquisition module (10) is used to collect on-site equipment data and push the collected equipment data to the data analysis module (20); The data analysis module (20) is used to analyze and diagnose the equipment status according to the equipment data pushed by the data acquisition module (10), and push the equipment status diagnosis result to the business application module (30); The business application module (30) is used to set business items according to different equipment condition diagnosis results, and to start the business process of implementing the relevant business items according to the equipment condition diagnosis results pushed by the data analysis module (20); The data acquisition module (10) comprises a regional server (11) and a signal receiving unit (12); the regional server (11) is data connected to the data analysis module (20); the regional server (11) is data connected to the signal receiving unit (12); the regional server (11) is data connected to a computer (4) for on-site production; and the signal receiving unit (12) is electrically connected to a sensor of an on-site device; The signal receiving unit (12) is used to collect electrical signals from field device sensors and convert the electrical signals into device status data for transmission to the regional server (11); The regional server (11) is used to collect equipment parameter data through an on-site production computer (4); The device status data and the device parameter data together constitute the device data; The regional server (11) is also used to push the device data to the data analysis module (20); The regional server (11) is provided with an equipment condition early warning model, which can perform a preliminary analysis on equipment data and issue early warning information in response to abnormal conditions of the equipment; The regional server (11) is also used to push the warning information and the device data when the warning occurs to the data analysis module (20); The equipment status warning model includes threshold warning, multiple rule warning, comprehensive warning and warning based on machine learning model; The data analysis module (20) comprises a data processing submodule (21) and a diagnosis submodule (22), the data processing submodule (21) and the diagnosis submodule (22) are data connected, the data processing submodule (21) and the data acquisition module (10) are data connected, and the diagnosis submodule (22) and the business application module (30) are data connected; The data processing submodule (21) is used to receive the device data pushed by the data acquisition module (10), and to store, analyze and calculate the device data; The diagnosis submodule (22) is used to perform equipment fault diagnosis based on the equipment data in the data processing submodule (21), and push the diagnosis result to the business application module (30); The diagnosis submodule (22) is provided with an equipment abnormality diagnosis model, and the equipment abnormality diagnosis model can further diagnose the equipment data for which the equipment condition warning model has issued a warning.

2. According to claim 1, the equipment intelligent diagnosis and maintenance management system is characterized by: The data analysis module (20) further comprises a human-computer interaction submodule (23), wherein the human-computer interaction submodule (23) is data-connected to the diagnosis submodule (22), and the human-computer interaction submodule (23) is used to implement an interactive function between an operator and the data processing submodule (21) and the diagnosis submodule (22), and to display a diagnosis result.

3. The intelligent equipment diagnosis and maintenance management system according to claim 2 is characterized by: The interactive functions between the operator and the data processing submodule (21) and the diagnosis submodule (22) implemented by the human-computer interaction submodule (23) include: area management, gateway configuration, device management, view management, early warning model configuration, data analysis, diagnostic report, process tracking, statistical analysis and data output interface.

4. The intelligent equipment diagnosis and maintenance management system according to claim 1, characterized in that: The business application module (30) is also used to push relevant information of the business process to a mobile phone of a person associated with the device.

5. The equipment intelligent diagnosis and maintenance management system according to claim 1 is characterized by: The business items set in the business application module (30) are editable and modifiable.

6. The equipment intelligent diagnosis and maintenance management system according to claim 1 is characterized by: The maintenance management system is used to perform maintenance management on wind turbine equipment.

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