CN113743529B - Deep learning and early warning method and device for operation data of mass industrial equipment - Google Patents
Deep learning and early warning method and device for operation data of mass industrial equipment Download PDFInfo
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- 238000013135 deep learning Methods 0.000 title claims abstract description 35
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- 238000003745 diagnosis Methods 0.000 claims abstract description 16
- 230000005540 biological transmission Effects 0.000 claims description 17
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Abstract
The invention provides a deep learning and early warning method and device for operation data of mass industrial equipment, and relates to the technical field of industrial equipment. The deep learning and early warning method and device comprises a shell, wherein an outer clamping plate is fixedly connected to one side, opposite to the shell, of the bottom of a fixing plate, one end of a second hydraulic rod penetrates through the shell and is fixedly connected with an inner clamping plate, a data acquisition module is fixedly connected to the outer wall of the middle position of the bottom of the shell, and the deep learning and early warning system comprises a data model module, a data processing module, a data output module, an autonomous learning module and a diagnosis learning module. The method comprises the steps of analyzing data through massive equipment operation data, operating a data model according to different conditions and scenes, diagnosing and processing the operation conditions of equipment, and performing alarm processing in advance according to the established data model by a system before the equipment is abnormal in operation, so that faults of industrial equipment are reduced, and the method is worthy of being widely popularized.
Description
Technical Field
The invention relates to the technical field of industrial equipment, in particular to a deep learning and early warning method and device for operation data of mass industrial equipment.
Background
The industry is the social substance production department of exploiting, collecting and processing various raw materials of natural resources, industry (industry) is the manufacturing industry, industry is the product of the development of social division, through several development stages of hand industry and machine industry, industry is the component part of the second industry, and is divided into light industry and heavy industry, industry is the only department of producing modern labor means, it determines the speed, scale and level of national economy modernization, plays dominant role in national economy of all countries in the modern world, industrial equipment refers to industrial production equipment and various machine tools such as lathe, milling machine, grinding machine, planer and the like.
At present, in the operation process of industrial equipment, simple real-time error alarm can be carried out only through the self-contained central control of the equipment, deep learning and early warning can not be carried out through historical mass operation data, and the traditional operation monitoring device is inconvenient to operate during installation.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a deep learning and early warning method and device for massive industrial equipment operation data, and solves the problems that simple real-time error warning can be carried out only through self-contained central control of the equipment in the operation process of the industrial equipment, and the deep learning and early warning can not be carried out through historical massive operation data.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the deep learning and early warning device for the operation data of the mass industrial equipment comprises a shell, a bidirectional hydraulic cylinder is fixedly connected to the inner wall of the middle position of the top of the shell, first hydraulic rods are arranged at two sides of the bidirectional hydraulic cylinder, one ends of the first hydraulic rods penetrate through the shell and are fixedly connected with fixing plates, outer clamping plates are fixedly connected to the opposite sides of the bottom of the fixing plates and are uniformly distributed on the adjacent sides of the outer clamping plates and the shell, second fixing teeth are uniformly distributed on the inner wall of the middle position of the two sides of the bottom of the shell, hydraulic cylinders are uniformly and fixedly connected to the inner clamping plates at the output ends of the hydraulic cylinders, second hydraulic rods penetrate through the shell and are fixedly connected with inner clamping plates, the inner clamping plates are far away from the first fixing teeth uniformly distributed on one sides of the shell, and the shell is made of titanium alloy.
Preferably, the outer wall of the middle position of the bottom of the shell is fixedly connected with a data acquisition module, the inner wall of the middle position of the bottom of the shell is fixedly connected with a data transmission module, the middle position inside the shell is fixedly connected with a deep learning and early warning system, and the deep learning and early warning system comprises a data model module, a data processing module, a data output module, an autonomous learning module and a diagnosis learning module;
the data acquisition module comprises one or more of a vibration sensor, a pressure sensor, a liquid level sensor and a temperature sensor.
Preferably, the outer wall of the middle position of the bottom of the shell is fixedly connected with a data acquisition module, the inner wall of the middle position of the bottom of the shell is fixedly connected with a data transmission module, the middle position inside the shell is fixedly connected with a deep learning and early warning system, and the deep learning and early warning system comprises a data model module, a data processing module, a data output module, an autonomous learning module and a diagnosis learning module;
the data acquisition module comprises one or more of a vibration sensor, a pressure sensor, a liquid level sensor and a temperature sensor.
Preferably, the data model module comprises an equipment information database, a fault information database and an operation optimization information database, wherein the equipment information database is used for storing various data when the equipment operates, the fault information database is used for storing various data when the equipment breaks down, and the operation optimization information database is used for storing information of the equipment in an optimal state.
Preferably, the workflow of the deep learning and early warning system comprises the following steps:
s1, detecting uploaded data in real time
Firstly, the shell is installed and fixed in the boiler equipment, and various data of the boiler equipment during working are detected through the data acquisition module;
s2, data judgment
Comparing the latest recorded data with the data in the fault information database through the data processing module, judging whether the data in the current equipment working state is consistent with the fault information data, judging whether the data in the current equipment working state is consistent with the information data before the fault, if the comparison result shows that the data is inconsistent with the information data before the fault, the equipment normally operates, then comparing the data with the data in the operation optimization information database through the data processing module, judging whether the data in the current equipment working state is compared with the data information in the equipment optimal state, and judging whether the equipment works in the optimal state;
s3, deep learning method
And the autonomous learning module stores the latest recorded data into the equipment information database.
Preferably, in the step S2 of data determination, when the data of the working state of the device is consistent with the fault information data, the diagnosis learning module alarms the fault information in advance through the data output module.
Working principle: the industrial equipment generates a large amount of operation monitoring data through the data acquisition module in the operation process, a large amount of important operation information and knowledge are contained in the proliferated data, the fault information database, the equipment information database and the operation optimization information database are provided with instructive significance for the problems of fault early warning, equipment diagnosis, operation optimization, operation reliability improvement and the like, the shell is placed at the inner top of the boiler, then the two-way hydraulic cylinder works to drive the first hydraulic rod to extend, the first hydraulic rod drives the fixing plate and the outer clamping plate to move, the two outer clamping plates are clamped on the outer walls of the two sides of the boiler, then the hydraulic cylinder works to drive the second hydraulic rod to extend, the inner clamping plate is driven to move through the second hydraulic rod, the two inner clamping plates are clamped on the inner walls of the two sides of the boiler, and the first fixing teeth on the inner clamping plates are matched with the second fixing teeth on the outer clamping plates to use, so that the shell can be firmly fixed at the inner top of the boiler.
(III) beneficial effects
The invention provides a deep learning and early warning method and device for operation data of mass industrial equipment. The beneficial effects are as follows:
1. the invention collects data through the data collection module, compares the latest recorded data with the data in the fault information database through the data processing module, judges whether the data in the working state of the current equipment is consistent with the fault information data, judges whether the data in the working state of the current equipment is consistent with the information data before the fault, normally operates the equipment when the comparison result is inconsistent, then compares the data with the data in the operation optimization information database, judges whether the equipment works in the optimal state or not, analyzes the data through massive industrial equipment operation data and operates a data model according to different conditions and scenes, diagnoses and processes the operation condition of the industrial equipment, and the system can perform self-learning according to the massive industrial equipment operation data.
2. According to the invention, the shell is placed at the inner top of the boiler, then the two-way hydraulic cylinder works to drive the first hydraulic rod to extend, the first hydraulic rod drives the fixed plate and the outer clamping plates to move, the two outer clamping plates are clamped on the outer walls of the two sides of the boiler, then the hydraulic cylinder works to drive the second hydraulic rod to extend, the second hydraulic rod drives the inner clamping plates to move, the two inner clamping plates are clamped on the inner walls of the two sides of the boiler, and the first fixed teeth on the inner clamping plates are matched with the second fixed teeth on the outer clamping plates to use, so that the shell can be firmly fixed at the inner top of the boiler, and the two-way hydraulic cylinder is suitable for boilers with different diameters, has higher applicability and is worth popularizing.
Drawings
FIG. 1 is a perspective view of the present invention;
FIG. 2 is a front view of the present invention;
FIG. 3 is an exploded view of the present invention;
FIG. 4 is a block diagram of a deep learning and early warning system of the present invention;
FIG. 5 is a block diagram of a data model module according to the present invention.
Wherein, 1, the shell; 2. a data acquisition module; 3. a fixing plate; 4. an outer clamping plate; 5. an inner clamping plate; 6. a first fixed tooth; 7. a second fixed tooth; 8. a bidirectional hydraulic cylinder; 9. a first hydraulic lever; 10. deep learning and early warning systems; 11. a data transmission module; 12. a hydraulic cylinder; 13. a second hydraulic lever; 14. a data model module; 15. a data processing module; 16. a data output module; 17. an autonomous learning module; 18. a diagnostic learning module; 19. a device information database; 20. a fault information database; 21. and running an optimization information database.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1-3, the embodiment of the invention provides a deep learning and early warning device for operation data of mass industrial equipment, which comprises a housing 1, wherein a bidirectional hydraulic cylinder 8 is fixedly connected to the inner wall of the middle position at the top of the housing 1, first hydraulic rods 9 are respectively arranged at the output ends at two sides of the bidirectional hydraulic cylinder 8, one end of each first hydraulic rod 9 penetrates through the housing 1 and is fixedly connected with a fixed plate 3, an outer clamping plate 4 is respectively fixedly connected to one side of the bottom of the fixed plate 3 opposite to the housing 1, second fixed teeth 7 which are uniformly distributed are respectively fixedly connected to one side of the outer clamping plate 4 adjacent to the housing 1, hydraulic cylinders 12 are respectively fixedly connected to the inner walls of the middle positions at two sides of the bottom of the housing 1, second hydraulic rods 13 are respectively arranged at the output ends of the hydraulic cylinders 12, one end of each second hydraulic rod 13 respectively penetrates through the housing 1 and is fixedly connected with an inner clamping plate 5, the first fixed teeth 6 of equal fixedly connected with of shell 1 one side is kept away from to interior splint 5, through putting shell 1 at the boiler in top, then two-way pneumatic cylinder 8 work drives first hydraulic stem 9 and elongates, drive fixed plate 3 and external splint 4 through first hydraulic stem 9 and remove, press from both sides on the both sides outer wall of boiler through two external splint 4, then hydraulic cylinder 12 work drives the extension of second hydraulic stem 13, it removes to drive interior splint 5 through second hydraulic stem 13, press from both sides on the both sides inner wall of boiler through two interior splint 5, first fixed teeth 6 on the interior splint 5 and the cooperation of the second fixed teeth 7 on external splint 4 are used, can firmly fix shell 1 at the boiler in top, and be applicable to in the boiler of different diameters, the suitability is stronger, the material of shell 1 is the titanium alloy, the titanium alloy material has intensity is higher and high temperature resistant characteristics.
The outer wall of the middle position of the bottom of the shell 1 is fixedly connected with a data acquisition module 2, the inner wall of the middle position of the bottom of the shell 1 is fixedly connected with a data transmission module 11, the middle position inside the shell 1 is fixedly connected with a deep learning and early warning system 10, and the deep learning and early warning system 10 comprises a data model module 14, a data processing module 15, a data output module 16, an autonomous learning module 17 and a diagnosis learning module 18;
the data acquisition module 2 comprises one or more of a vibration sensor, a pressure sensor, a liquid level sensor and a temperature sensor.
Embodiment two:
as shown in fig. 4-5, the embodiment of the invention provides a deep learning and early warning method for running data of a mass industrial device, the data model module 14 and the autonomous learning module 17 are connected with each other through the data transmission module 11, the diagnosis learning module 18 and the data model module 14 are connected with each other through the data transmission module 11, the data output module 16 and the autonomous learning module 17 and the diagnosis learning module 18 are respectively connected with each other through the data transmission module 11, the data processing module 15 is respectively connected with the data model module 14, the data output module 16, the autonomous learning module 17 and the diagnosis learning module 18 through the data transmission module 11, and the transmission mode of the data transmission module 11 is a 5G network.
The data model module 14 includes a device information database 19, a fault information database 20, and an operation optimization information database 21, where the device information database 19 is used to store various data when the device operates, the fault information database 20 stores various data when the device fails, and the operation optimization information database 21 stores information of the device in an optimal state.
The workflow of the deep learning and early warning system 10 includes the steps of:
s1, detecting uploaded data in real time
Firstly, the shell 1 is installed and fixed in the boiler equipment, and various data of the boiler equipment during operation are detected through the data acquisition module 2;
s2, data judgment
Comparing the latest recorded data with the data in the fault information database 20 through the data processing module 15, judging whether the data in the current equipment working state is consistent with the fault information data, judging whether the data in the current equipment working state is consistent with the information data before the fault, if the comparison result shows inconsistent, the equipment normally operates, then comparing the data with the data in the operation optimization information database 21 through the data processing module 15, and judging whether the equipment works in the optimal state or not by comparing the data in the current equipment working state with the data information in the optimal state;
s3, deep learning method
The autonomous learning module 17 saves the latest recorded data into the device information database 19.
In the step S2, when the data of the working state of the equipment is consistent with the data of the fault information, the diagnosis learning module 18 alarms the fault information in advance through the data output module 16, analyzes the data through massive industrial equipment operation data, operates a data model according to different conditions and scenes, diagnoses and processes the operation condition of the industrial equipment, the system performs self-learning according to the massive industrial equipment operation data, the system performs model building through the massive industrial equipment operation data, and the system performs alarm processing in advance according to the established data model when the equipment is abnormal in operation, and performs relevant alarm and notifies relevant personnel to process, so that the influence of the fault of the industrial equipment on a production line and the occurrence of some dangerous megaaccidents of enterprises are reduced.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. The deep learning and early warning device for the operation data of the mass industrial equipment comprises a shell (1), and is characterized in that: the two-way hydraulic cylinder (8) is fixedly connected to the inner wall of the middle position of the top of the shell (1), first hydraulic rods (9) are respectively arranged at the output ends of two sides of the two-way hydraulic cylinder (8), one ends of the first hydraulic rods (9) penetrate through the shell (1) and are fixedly connected with a fixing plate (3), an outer clamping plate (4) is respectively fixedly connected to one side, opposite to the shell (1), of the bottom of the fixing plate (3), second fixing teeth (7) which are uniformly distributed are respectively fixedly connected to one side, adjacent to the shell (1), of the outer clamping plate (4), the novel hydraulic cylinder is characterized in that hydraulic cylinders (12) are fixedly connected to the inner walls of the middle positions of the two sides of the bottom of the shell (1), second hydraulic rods (13) are arranged at the output ends of the hydraulic cylinders (12), one ends of the second hydraulic rods (13) penetrate through the shell (1) and are fixedly connected with inner clamping plates (5), the inner clamping plates (5) are far away from first fixing teeth (6) which are uniformly distributed on one side of the shell (1), and the shell (1) is made of titanium alloy.
2. The deep learning and early warning device for operation data of mass industrial equipment according to claim 1, wherein: the intelligent monitoring device comprises a shell (1), wherein a data acquisition module (2) is fixedly connected to the outer wall of the middle position of the bottom of the shell (1), a data transmission module (11) is fixedly connected to the inner wall of the middle position of the bottom of the shell (1), a deep learning and early warning system (10) is fixedly connected to the middle position inside the shell (1), and the deep learning and early warning system (10) comprises a data model module (14), a data processing module (15), a data output module (16), an autonomous learning module (17) and a diagnosis learning module (18);
the data acquisition module (2) comprises one or more of a vibration sensor, a pressure sensor, a liquid level sensor and a temperature sensor.
3. The deep learning and early warning device for operation data of mass industrial equipment according to claim 2, wherein: the diagnosis learning system is characterized in that the data model module (14) is connected with the autonomous learning module (17) through a data transmission module (11), the diagnosis learning module (18) is connected with the data model module (14) through the data transmission module (11), the data output module (16) is connected with the autonomous learning module (17) and the diagnosis learning module (18) through the data transmission module (11), the data processing module (15) is connected with the data model module (14), the data output module (16), the autonomous learning module (17) and the diagnosis learning module (18) through the data transmission module (11), and the transmission mode of the data transmission module (11) is a 5G network.
4. The deep learning and early warning device for operation data of mass industrial equipment according to claim 2, wherein: the data model module (14) comprises an equipment information database (19), a fault information database (20) and an operation optimization information database (21), wherein the equipment information database (19) is used for storing various data when equipment operates, the fault information database (20) is used for storing various data when the equipment breaks down, and the operation optimization information database (21) is used for storing information of the equipment in an optimal state.
5. The method for pre-warning the deep learning and pre-warning device for the operation data of the mass industrial equipment according to claim 2, wherein the method is characterized by comprising the following steps: the workflow of the deep learning and early warning system (10) comprises the following steps:
s1, detecting uploaded data in real time
Firstly, the shell (1) is installed and fixed in the boiler equipment, and various data of the boiler equipment during operation are detected through the data acquisition module (2);
s2, data judgment
Comparing the latest recorded data with the data in the fault information database (20) through the data processing module (15), judging whether the data in the current equipment working state is consistent with the fault information data, judging whether the data in the current equipment working state is consistent with the information data before the fault, if the comparison result is inconsistent, the equipment normally operates, then comparing the data with the data in the operation optimization information database (21) through the data processing module (15), and judging whether the equipment works in the optimal state or not by comparing the data in the current equipment working state with the data information in the optimal state of the equipment;
s3, deep learning method
The autonomous learning module (17) stores the latest recorded data into the device information database (19).
6. The method for pre-warning the deep learning and pre-warning device for the operation data of the mass industrial equipment according to claim 5, wherein the method is characterized by comprising the following steps: in the step S2 data judgment, when the data of the working state of the equipment is consistent with the fault information data, the diagnosis learning module (18) alarms the fault information in advance through the data output module (16).
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007052756A (en) * | 2005-08-16 | 2007-03-01 | Movell Software:Kk | Learning type diagnostic database applied to trouble diagnosis in wireless device |
| CN102023100A (en) * | 2010-04-19 | 2011-04-20 | 东莞市罗尔机电科技有限公司 | Equipment failure early-warning system and method |
| WO2019245728A1 (en) * | 2018-06-22 | 2019-12-26 | Siemens Industry, Inc. | Deep-learning-based fault detection in building automation systems |
| CN111426699A (en) * | 2020-04-20 | 2020-07-17 | 上海发电设备成套设计研究院有限责任公司 | Boiler heating surface appearance defect online detection and identification device and method |
| CN212081205U (en) * | 2020-05-20 | 2020-12-04 | 祁志鹏 | Alarm device for boiler pressure detection |
| CN112664921A (en) * | 2020-12-01 | 2021-04-16 | 华电电力科学研究院有限公司 | Boiler hearth pressure thermal control regulator for power plant and use method thereof |
| CN213577444U (en) * | 2020-11-04 | 2021-06-29 | 厦门市特种设备检验检测院(厦门市特种设备应急处置中心) | Real-time creep quantity monitoring device for main steam pipeline of power station boiler |
-
2021
- 2021-09-15 CN CN202111082041.6A patent/CN113743529B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007052756A (en) * | 2005-08-16 | 2007-03-01 | Movell Software:Kk | Learning type diagnostic database applied to trouble diagnosis in wireless device |
| CN102023100A (en) * | 2010-04-19 | 2011-04-20 | 东莞市罗尔机电科技有限公司 | Equipment failure early-warning system and method |
| WO2019245728A1 (en) * | 2018-06-22 | 2019-12-26 | Siemens Industry, Inc. | Deep-learning-based fault detection in building automation systems |
| CN111426699A (en) * | 2020-04-20 | 2020-07-17 | 上海发电设备成套设计研究院有限责任公司 | Boiler heating surface appearance defect online detection and identification device and method |
| CN212081205U (en) * | 2020-05-20 | 2020-12-04 | 祁志鹏 | Alarm device for boiler pressure detection |
| CN213577444U (en) * | 2020-11-04 | 2021-06-29 | 厦门市特种设备检验检测院(厦门市特种设备应急处置中心) | Real-time creep quantity monitoring device for main steam pipeline of power station boiler |
| CN112664921A (en) * | 2020-12-01 | 2021-04-16 | 华电电力科学研究院有限公司 | Boiler hearth pressure thermal control regulator for power plant and use method thereof |
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