CN113281046B - Paper machine bearing monitoring device and method based on big data - Google Patents
Paper machine bearing monitoring device and method based on big data Download PDFInfo
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- CN113281046B CN113281046B CN202110584449.7A CN202110584449A CN113281046B CN 113281046 B CN113281046 B CN 113281046B CN 202110584449 A CN202110584449 A CN 202110584449A CN 113281046 B CN113281046 B CN 113281046B
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000012806 monitoring device Methods 0.000 title claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 33
- 238000004891 communication Methods 0.000 claims abstract description 20
- 238000005096 rolling process Methods 0.000 claims abstract description 18
- 238000004519 manufacturing process Methods 0.000 claims abstract description 11
- 238000005516 engineering process Methods 0.000 claims abstract description 10
- 238000012423 maintenance Methods 0.000 claims description 44
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000003490 calendering Methods 0.000 claims description 3
- 238000001035 drying Methods 0.000 claims description 3
- 238000004804 winding Methods 0.000 claims description 3
- 238000003825 pressing Methods 0.000 claims description 2
- 230000006378 damage Effects 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000000465 moulding Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- NJPPVKZQTLUDBO-UHFFFAOYSA-N novaluron Chemical compound C1=C(Cl)C(OC(F)(F)C(OC(F)(F)F)F)=CC=C1NC(=O)NC(=O)C1=C(F)C=CC=C1F NJPPVKZQTLUDBO-UHFFFAOYSA-N 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
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- General Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Rolling Contact Bearings (AREA)
Abstract
The utility model provides a paper machine bearing monitoring devices and method based on big data, includes data acquisition module and the bearing monitoring module that sets up on paper machine inner bearing, data acquisition module is used for gathering the working signal of different model bearings, data acquisition module and bearing monitoring module link to each other with processing module, and bearing information is entered the module to processing module's input connection, and alarm module is connected to processing module's output, processing module connects bearing inquiry module, communication module and cloud server respectively, the database is connected to the cloud server. The invention is convenient for staff to repair the fault bearing in time, solves the problem of unplanned production and shutdown caused by the faults and damages of the rolling bearing of the traditional paper machine, analyzes the bearing quality and the technical level of the maintainer by utilizing the big data technology monitoring data, and is beneficial to improving the quality of the paper machine bearing produced by bearing manufacturers and improving the production efficiency.
Description
Technical Field
The invention relates to the technical field of big data and bearing detection, in particular to a paper machine bearing monitoring device and method based on big data.
Background
The paper machine has a complex structure, belongs to large mechanical equipment running continuously for 24 hours, and the rolling bearing of the paper machine can bear external load with complex change in the actual running process, so that the rolling bearing is easy to damage, and according to incomplete statistics, about 30% of faults of the paper machine are caused by the faults of the rolling bearing. These bearing failures and injuries are likely to cause excessive or insufficient maintenance, unplanned downtime problems, impact on plant production cycle and progress, and reduce paper machine productivity. However, the prior art only detects faults of a single designated bearing, and the prior art has no real-time monitoring and analyzing of working states of different bearings by using a big data technology.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a paper machine bearing monitoring device and method based on big data, which are suitable for each bearing production, paper machine production and paper manufacturer, can realize real-time monitoring and fault detection of the paper machine bearing, facilitate the maintenance of the fault bearing by staff in time, solve the problem of unplanned production stopping and shutdown caused by the faults and damages of the traditional paper machine rolling bearing, analyze the bearing quality and the technical level of maintenance staff by utilizing the big data technology monitoring data, and are beneficial to improving the paper machine bearing quality produced by the bearing manufacturer and improving the production efficiency.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the utility model provides a paper machine bearing monitoring devices based on big data, includes data acquisition module 12 and the bearing monitoring module 30 that set up on paper machine inner bearing 11, data acquisition module 12 is used for gathering the working signal of different model bearings, data acquisition module 12 and bearing monitoring module 30 link to each other with processing module 60, and processing module 60's input is connected bearing information and is entered module 20, and alarm module 50 is connected to processing module 60's output, processing module 60 connects bearing inquiry module 40, communication module 50 and cloud server 82 respectively, database 81 is connected to cloud server 82.
The bearing information input module 20 is used for inputting model batch, manufacturer, assembly and maintenance record information of the bearing to be monitored;
the bearing monitoring module 30 is used for monitoring the working process of the bearing in the whole life cycle and detecting whether faults occur;
the alarm module 50 is used for sending alarm information to the processing module 60 when the device detects that the bearing is about to fail or has failed;
the processing module 60 receives the alarm information, reminds workshop staff and uploads the alarm information to the cloud server 82 through the communication module 70, the database 81 is used for recording bearing model batches, bearing monitoring data, alarm conditions, assembly and maintenance log information, and the cloud server 82 is used for storing each database 81;
the bearing query module 40 is used for querying basic information, working state, monitoring data and analysis results of each bearing;
the processing module 60 is used for performing corresponding processing according to tasks of different modules of the staff.
The bearing 11 is a paper machine rolling bearing, and the paper machine rolling bearing comprises a paper machine headbox 101, a forming part 102, a pressing part 103, a drying part 104, a calendaring part 105, a paper winding part 106 and other bearings to be tested in the paper production process.
All the sensors provided in the molding part 102 are waterproof sensors.
The data acquisition module 12 comprises a data acquisition device 121 arranged on the bearing pedestal 115, wherein the data acquisition device 121 is used for acquiring working signals of the bearing 11, and the data acquisition device 121 comprises a vibration sensor, a photoelectric encoder, a temperature sensor and a data acquisition card;
the vibration sensor is an acceleration sensor and is used for measuring vibration signals of the bearing, and the model is RVT-120;
the photoelectric encoder is used for measuring an instantaneous rotating speed signal of the bearing, and the model is MK80;
the temperature sensor is used for measuring the working temperature of the bearing, and the model is DS18B20;
the data acquisition card is used for acquiring various sensor data and transmitting the data to the processing module 60, and the model is NI9234.
The bearing monitoring module 30 is used for monitoring the working process of the bearing 11 in the whole life cycle, judging whether the bearing 11 has faults according to the data acquired by the data acquisition module 12, wherein the monitored bearing faults comprise four types of faults of an inner ring 111, an outer ring 114, a rolling body 112 and a retainer 113;
the bearing 11 includes an outer ring 114 on the outer side and an inner ring 111 on the inner side, rolling elements 112 are provided between the outer ring 114 and the inner ring 111, and a cage 113 is provided on the rolling elements 112.
The communication module 70 comprises a base station 70 arranged in a staff duty room and communication nodes below a bearing workbench of the paper machine, and the number of the communication nodes is determined according to the scale of the bearing 11.
The application method of the paper machine bearing monitoring device based on big data comprises the following steps:
the device has a startup self-starting function in use, in the initialization process, information such as the model, batch, manufacturer, assembly record and maintenance personnel of the bearing to be detected is recorded by a worker through the bearing information recording module 20, and the information can be stored in the database 81 and uploaded to the cloud server 82 after the worker checks the information; during the working process of the paper machine, the data acquisition module 12 acquires working data of the bearing 11 in real time, the data are transmitted to the bearing monitoring module 30 through the communication module 70 after the data acquisition is successful, and the bearing monitoring module 30 detects whether the bearing 11 fails or not;
when the bearing 11 fails, the alarm module 50 sends alarm information to the processing module 60, and the alarm lamp is turned on and the buzzer sounds, so that a worker is reminded of timely maintaining or replacing the failed bearing, and the monitoring condition is stored in the database 81;
in the working process of the paper machine, the cloud server 82 can analyze the quality of the monitoring bearings and the technical level of maintenance staff by analyzing the data information stored in each database 81 in real time through a big data technology, and analyze the quality bearing model batch, manufacturer and the technical level of maintenance staff recommended to use, and staff can inquire the basic information, working state, monitoring data and analysis result information of each bearing through the bearing inquiry module 40 so as to know and feed back the quality of the bearings and the technology of the maintenance staff in time.
The invention has the beneficial effects that:
according to the invention, through the arrangement of the data acquisition module and the bearing monitoring module, the working data of the bearing can be acquired in real time, whether the bearing breaks down or not and the type of the broken down can be detected, through the arrangement of the cloud server, the bearing data information can be synchronized in real time, the quality of the bearing can be analyzed, whether the bearing is recommended to purchase or not can be analyzed, through the arrangement of the bearing inquiry module, the working state of the bearing and the recommended condition of the system can be inquired at any time, the real-time monitoring and the fault detection of the paper machine bearing can be realized, meanwhile, the type of the high-quality bearing and the corresponding manufacturer and batch can be analyzed, and whether the technology of a maintainer is qualified can be analyzed, so that the quality of the paper machine bearing can be improved, and the production efficiency can be improved.
Drawings
Fig. 1 is a functional block diagram of the present invention.
Fig. 2 is a schematic diagram of the overall structure of the present invention.
FIG. 3 is a schematic diagram of the location of a data acquisition module.
Fig. 4 is a schematic of the workflow of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a paper machine bearing monitoring device and method based on big data is characterized in that: the system comprises a bearing 11, a data acquisition module 12, a bearing information input module 20, a bearing monitoring module 30, a bearing query module 40, an alarm module 50, a processing module 60, a communication module 70, a database 81 and a cloud server 82. The bearing 11 is a rolling bearing of a paper machine, the data acquisition module 12 is used for acquiring working signals of bearings of different types, the bearing information input module 20 is used for inputting information such as model batches, manufacturers, assembly and maintenance records of the bearings to be monitored, the bearing monitoring module 30 is used for monitoring the working process of the bearings in the whole life cycle and detecting whether faults occur, the alarm module 50 is used for sending alarm information to the processing module 60 when the device detects that the bearings are about to fail or have failed, the processing module 60 receives the alarm information and reminds workshop staff and uploads the alarm information to the cloud server 82 through the communication module 70, the database 81 is used for recording the information such as the bearing model batches, the bearing monitoring data, alarm conditions, assembly and maintenance logs and the like, the cloud server 82 is used for storing the information such as the database 81, the bearing working states monitored synchronously in real time and analyzing the quality bearing model batches, the manufacturers and the maintenance staff technical level according to the monitoring results, and the bearing query module 40 is used for inquiring basic information, the working states, the monitoring data and the analysis results of the bearings, and the processing module 60 is used for carrying out corresponding processing according to tasks of different modules of the staff.
Referring to fig. 1 and 2, the bearing 11 is a rolling bearing of a paper machine, and includes a paper machine headbox 101, a forming section 102, a press section 103, a drying section 104, a calendering section 105, a paper winding section 106, and other bearings to be tested in the paper production process.
Referring to fig. 2 and 3, the data acquisition module 12 includes a data acquisition device 121 mounted on the bearing seat 115 for acquiring working signals of the bearing 11, including a vibration sensor, a photoelectric encoder, a temperature sensor and a data acquisition card. The vibration sensor is an acceleration sensor and is used for measuring vibration signals of the bearing, and the model of the vibration sensor is RVT-120; the adopted photoelectric encoder is used for measuring an instantaneous rotating speed signal of the bearing, and the model is MK80; the adopted temperature sensor is used for measuring the working temperature of the bearing, and the model is DS18B20; the adopted data acquisition card is used for acquiring various sensor data and transmitting the data to the processing module 60, and the model is NI9234; the molding unit 102 selects all the corresponding waterproof sensors.
The bearing information input module 20 is used for inputting information such as the type, batch, manufacturer, assembly and maintenance record, assembly personnel name, maintenance personnel name and the like of the bearing 11 to be monitored, and the staff confirms that the information is accurate and then uploads the information to the cloud server 82 and stores the information in the database 81.
Referring to fig. 2 and 3, the bearing monitoring module 30 is configured to monitor the working process of the bearing 11 in the whole life cycle, and determine whether the bearing 11 has a fault according to the data collected by the data collecting module 12, where the monitored bearing faults include an inner ring 111 fault, an outer ring 114 fault, a rolling element 112 fault, and a cage 113 fault.
The bearing query module 40 is used for a worker to query basic information, working state, monitoring data, time, frequency and type of fault occurrence, and analyzed bearing quality results and maintenance personnel technical level of each monitoring bearing 11.
Referring to fig. 2, the communication module 70 includes communication nodes (701-706) disposed under the base station 70 and the papermachine's bearing table of the operator's duty room, and the number of communication nodes (701-706) is determined according to the scale of the bearing 11.
Referring to fig. 2, the alarm module 50 includes an alarm lamp and a buzzer, and is configured to send alarm information to the processing module 60 when the device detects that the bearing 11 will fail or has failed. The processing module 60, upon receiving the alarm message, alerts the shop staff to repair or replace the faulty bearing and upload it to the cloud server 82 via the communication module 70.
The database 81 comprises a bearing model database, a bearing monitoring signal database, a bearing fault condition database, a basic condition for recording the bearing, an alarm condition, a maintenance log and other information. The cloud server 82 is used for storing the working state of the bearing monitored by the database 81 and the real-time synchronization device, avoiding the occurrence of unplanned situations caused by untimely updating of data information, analyzing high-quality bearing models and corresponding manufacturers and batches according to the monitoring results, and simultaneously analyzing whether the technology of maintenance personnel is qualified or not.
A specific workflow diagram of the present system is shown in fig. 4.
The device has a startup self-starting function in use, in the initialization process, basic information such as the model, batch, manufacturer, assembly and maintenance record, assembly personnel name, maintenance personnel name and the like of the bearing to be detected is recorded by a worker through the bearing information recording module 20, and the worker can store in the database 81 and upload to the cloud server 82 after checking without errors. In the working process of the paper machine, the data acquisition module 12 acquires working data of the bearing 11 in real time, the data are transmitted to the bearing monitoring module 30 through the communication module 70 after the data acquisition is successful, and the bearing monitoring module 30 detects the acquired data and judges whether the bearing 11 fails or not. When the bearing 11 fails, the alarm module 50 sends alarm information to the processing module 60, and the alarm lamp is turned on and the buzzer sounds, thereby reminding a worker to repair or replace the failed bearing in time, and storing the monitoring situation in the database 81. During the paper machine working process, the cloud server 82 can analyze the quality of the monitoring bearing and the technical level of maintenance personnel by analyzing the data information stored in each database 81 in real time through a big data technology, and mainly analyze the following three conditions:
1) If the manufacturers of the bearings 11 to be tested are the same, the models are the same, the batches are the same, and when maintenance staff is different, the maintenance staff A maintains the fault bearings to a normal working state in the maintenance process, and the maintenance staff B does not maintain the fault bearings to the normal working state in the maintenance process, the technical deficiency of the maintenance staff B is described, the technical level of the maintenance staff A is higher than that of the maintenance staff B, and the maintenance staff B needs to promote the personal maintenance technical level so as to meet the daily working requirements;
2) If the manufacturers of the bearings 11 to be tested are the same, the models are the same, and the maintenance staff are the same, when the batches are different, the maintenance staff maintains the fault bearing A to a normal working state in the maintenance process, but does not maintain the fault bearing B to the normal working state, the bearing quality of the bearing B batch is low, the bearing quality of the bearing A batch is better than that of the bearing B batch, and the batch A bearings of the manufacturers and the models are recommended to be purchased;
3) If the types of the bearings 11 to be detected are the same, the maintenance staff are the same, and the manufacturers are different, the maintenance staff maintains the fault bearing A to a normal working state in the maintenance process, but does not maintain the fault bearing B to the normal working state, the bearing quality of the bearing B manufacturer is low, the bearing quality of the bearing A manufacturer is superior to that of the bearing B manufacturer, and the purchase of the bearing of the A manufacturer with the type is recommended;
therefore, the technical level of high-quality bearing model batches, manufacturers and maintenance personnel which are recommended to use is analyzed, and the staff can inquire the basic information, working state, monitoring data, analysis result and other information of the bearing through the bearing inquiry module 40 so as to timely know and feed back the quality of the bearing and the technical level of the maintenance personnel.
Claims (5)
1. Paper machine bearing monitoring devices based on big data, its characterized in that: the system comprises a data acquisition module (12) and a bearing monitoring module (30) which are arranged on a bearing (11) in a paper machine, wherein the data acquisition module (12) is used for acquiring working signals of bearings of different types, the data acquisition module (12) and the bearing monitoring module (30) are connected with a processing module (60), the input end of the processing module (60) is connected with a bearing information input module (20), the output end of the processing module (60) is connected with an alarm module (50), the processing module (60) is respectively connected with a bearing query module (40), a communication module (50) and a cloud server (82), and the cloud server (82) is connected with a database (81);
the bearing information input module (20) is used for inputting model batch, manufacturer, assembly and maintenance record information of the bearing to be monitored;
the bearing monitoring module (30) is used for monitoring the working process of the bearing in the whole life cycle and detecting whether faults occur or not;
the alarm module (50) is used for sending alarm information to the processing module (60) when the device detects that the bearing is about to fail or has failed;
the processing module (60) reminds workshop staff after receiving the alarm information and uploads the alarm information to the cloud server (82) through the communication module (70), the database (81) is used for recording bearing model batches, bearing monitoring data, alarm conditions, assembly and maintenance log information, and the cloud server (82) is used for storing all the databases (81);
the bearing query module (40) is used for querying basic information, working state, monitoring data and analysis results of each bearing;
the processing module (60) is used for carrying out corresponding processing according to tasks of different modules of the staff;
in the working process of the paper machine, the cloud server (82) can analyze the quality of the bearing and the technical level of maintenance personnel by analyzing the data information stored in each database (81) in real time through a big data technology, so that the quality batch of the high-quality bearing recommended to be used, the technical level of manufacturers and maintenance personnel are analyzed, and the staff can inquire the basic information, the working state, the monitoring data and the analysis result information of each bearing through the bearing inquiry module (40) to timely know and feed back the quality of the bearing and the technology of the maintenance personnel;
the data acquisition module (12) comprises a data acquisition device (121) arranged on the bearing seat (115), the data acquisition device (121) is used for acquiring working signals of the bearing (11), and the data acquisition device (121) comprises a vibration sensor, a photoelectric encoder, a temperature sensor and a data acquisition card;
the vibration sensor is an acceleration sensor and is used for measuring vibration signals of the bearing, and the model is RVT-120;
the photoelectric encoder is used for measuring an instantaneous rotating speed signal of the bearing, and the model is MK80;
the temperature sensor is used for measuring the working temperature of the bearing, and the model is DS18B20;
the data acquisition card is used for acquiring various sensor data and transmitting the data to the processing module 60, and the model is NI9234;
the bearing monitoring module (30) is used for monitoring the working process of the bearing (11) in the whole life cycle, judging whether the bearing (11) has faults according to the data acquired by the data acquisition module (12), wherein the monitored bearing faults comprise an inner ring (111) fault, an outer ring (114) fault, a rolling body (112) fault and a retainer (113) fault;
the bearing (11) comprises an outer ring (114) at the outer side and an inner ring (111) at the inner side, rolling bodies (112) are arranged between the outer ring (114) and the inner ring (111), and a retainer (113) is arranged on the rolling bodies (112).
2. The paper machine bearing monitoring device based on big data according to claim 1, wherein the bearing (11) is a paper machine rolling bearing, and the paper machine rolling bearing comprises a paper machine headbox (101), a forming part (102), a pressing part (103), a drying part (104), a calendaring part (105), a paper winding part (106) and other bearings to be tested in paper production processes.
3. The paper machine bearing monitoring device based on big data according to claim 2, wherein the sensors provided in the forming section (102) all select the corresponding waterproof type sensors.
4. The paper machine bearing monitoring device based on big data according to claim 1, characterized in that the communication module (70) comprises communication nodes arranged under the base station (70) of the staff duty room and the paper machine bearing workbench, and the number of the communication nodes is determined according to the scale of the bearing (11).
5. A method of using a paper machine bearing monitoring device based on big data according to any of claims 1-4,
the method is characterized by comprising the following steps of:
the device has a startup self-starting function in use, in the initialization process, information such as the model, batch, manufacturer, assembly record and maintenance personnel of the bearing to be detected is recorded by a staff through a bearing information recording module (20), and the information can be stored in a database (81) and uploaded to a cloud server (82) after the staff checks; in the working process of the paper machine, a data acquisition module (12) acquires working data of a bearing (11) in real time, the data is transmitted to a bearing monitoring module (30) through a communication module (70) after the data acquisition is successful, and the bearing monitoring module (30) detects whether the bearing (11) fails or not;
when the bearing (11) breaks down, the alarm module (50) sends alarm information to the processing module (60), and the alarm lamp is on and the buzzer sounds, so that workers are reminded of timely maintaining or replacing the broken bearing, and monitoring conditions are stored in the database (81).
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