CN110794799A - Big data system with fault diagnosis function applied to industrial production - Google Patents
Big data system with fault diagnosis function applied to industrial production Download PDFInfo
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- CN110794799A CN110794799A CN201911193324.0A CN201911193324A CN110794799A CN 110794799 A CN110794799 A CN 110794799A CN 201911193324 A CN201911193324 A CN 201911193324A CN 110794799 A CN110794799 A CN 110794799A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a big data system with a fault diagnosis function applied to industrial production, which relates to the technical field of intelligent manufacturing, and comprises an Internet of things device, a data acquisition module and a data processing module, wherein the Internet of things device is used for acquiring working condition information of production equipment; the big data equipment carries out modeling and control strategy formation according to the working condition information, forms a control step and an early warning signal after fault diagnosis is carried out according to the established model and the control strategy, and controls the production equipment through the control step; meanwhile, temperature field data obtained by monitoring of the infrared temperature measurement equipment is transmitted to big data equipment, and the big data equipment sends out corresponding temperature early warning signals according to the temperature measurement data; and the early warning equipment performs early warning according to the early warning signal or the temperature early warning signal. The modeling and control of industrial production through the big data system are realized, the monitoring and maintenance of production are convenient through the big data system with the fault diagnosis function applied to the industrial production, meanwhile, prompt can be provided through fault diagnosis, further damage of equipment is reduced, and resource loss is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a big data system with a fault diagnosis function, which is applied to industrial production.
Background
The data set size of the data grows at an unimaginable rate, presenting a significant challenge to data processing. First, the development of information technology makes the generation and consumption of data easier. For example, a 72 hour long video per minute is uploaded to the Youtube server. This large capacity nature of big data makes it difficult for data to be collected and integrated from distributed sites in a scalable manner. Second, after data collection, how to store and manage these massive heterogeneous data with minimal hardware and software cost is a very challenging problem. Third, due to the characteristics of heterogeneity, scalability, real-time, complexity, and privacy of big data, big data analysis must efficiently mine data at different levels (modeling, visualization, prediction, and optimization) to improve decision efficiency. These challenges urgently require a revolution through the various levels of data management systems (from architectural to specific mechanisms). But if large data can be managed effectively, it can bring huge revolution to many fields such as scientific and environmental modeling, health care and energy conservation. Research reports of McKinsey, an international policy consulting company, show that the potential value of global personal location information reaches 7000 hundred million, and the cost of product development and integration can be reduced by more than half.
With the rapid development of science, technology and engineering, in recent 20 years, a large amount of data (more properly described, perhaps "infinite" data, for example, in applications such as optical observation and monitoring, data is continuously coming, forming a "data disaster") has been generated in many fields (such as optical observation, optical monitoring, health care, sensors, user data, internet and financial companies, and supply chain systems), and the concept of big data has attracted attention again. Compared with the traditional data, in addition to the appearance characteristics such as large capacity, the big data also has other unique characteristics, for example, the big data is generally unstructured and needs to be analyzed in real time, so the development of the big data needs a completely new architecture for processing the acquisition, transmission, storage and analysis of large-scale data, however, the existing big data system applied to industrial production is not provided with a fault diagnosis function, and in industrial production, the occurrence of faults is inevitable, and therefore, a big data system with a fault diagnosis function applied to industrial production is urgently needed.
Disclosure of Invention
The invention aims to provide a big data system with a fault diagnosis function applied to industrial production, thereby overcoming the defect that the existing big data system with the fault diagnosis function applied to the industrial production does not exist.
In order to achieve the above object, the present invention provides a big data system with a fault diagnosis function applied to industrial production, comprising:
production equipment which is equipment required by industrial production;
the Internet of things equipment is connected with the production equipment and is used for acquiring the working condition information of the production equipment;
the big data equipment is respectively connected with the Internet of things equipment and the production equipment, and is used for modeling and forming a control strategy according to the working condition information acquired by the Internet of things equipment, forming a control step and an early warning signal after fault diagnosis is carried out according to the established model and the control strategy, and controlling the production equipment through the control step;
the infrared temperature measurement equipment is connected with the big data equipment, is used for monitoring temperature field data in the monitoring range of the infrared temperature measurement equipment and transmitting the temperature field data to the big data equipment, and the big data equipment sends out corresponding temperature early warning signals according to the collected temperature measurement data; and
and the early warning equipment is connected with the big data equipment and carries out early warning according to the early warning signal or the temperature early warning signal.
Further, the production apparatus includes: the equipment on the production line, the warehouse equipment, the intelligent trolley, the tooling equipment and the control equipment corresponding to the equipment on the production line, the warehouse equipment and the intelligent trolley.
Furthermore, the internet of things equipment comprises a data acquisition unit for acquiring various data of the current production, the signal output end of the data acquisition unit is connected with the master microcontroller through the microcontroller, and the master microcontroller sends signals to the big data equipment.
Further, the big data device includes:
the real-time data transmission module is connected with the physical network equipment and used for receiving data acquired by the Internet of things equipment;
the big data server is connected with the real-time data transmission module and is used for carrying out data processing on historical data acquired by the real-time data transmission module to form a control strategy;
the model database is connected with the real-time data transmission module and is used for modeling the historical data collected by the real-time data transmission module and correcting the model according to the real-time data collected by the real-time data transmission module; and
the fault diagnosis equipment is respectively connected with the big data server and the model database and is used for carrying out fault diagnosis according to the model corrected by the big data server and fault data in the control strategy generated by the big data server to form a fault diagnosis result;
and the master controller is respectively connected with the fault diagnosis equipment and the production equipment, is used for combining the model modified by the big data server and the control strategy generated by the big data server, forming a control step and an early warning signal according to the fault diagnosis result, simultaneously storing the control step and the early warning signal into the big data server, and controlling the production equipment according to the control step and sending the control step and the early warning signal to the early warning equipment for early warning.
Further, the operating condition information includes: basic equipment information, running condition information, actual service life information and replacement frequency information of the production equipment.
Further, the warning signal includes: the early warning signals of the equipment on the production line, the warehouse equipment, the intelligent trolley, the tooling equipment and the control equipment corresponding to the equipment on the production line, the warehouse equipment and the intelligent trolley.
Further, the fault diagnosis device is one or a combination of more than two of a digital signal processing device, an embedded device and a computer, and is selected according to industrial production.
Further, the fault diagnosis includes the steps of:
s1, classifying according to the type of the production equipment according to the working condition information;
s2, normalizing the working condition information of one type of production equipment;
s3, setting the output of the neural network as the fault type and fault degree of the uploading equipment, and training the normalized data through the neural network to obtain the trained neural network;
s4, repeating S2-S3 to obtain trained neural networks corresponding to all production equipment;
and S5, classifying the real-time collected working condition information through S1, and performing fault diagnosis through the correspondingly trained neural network in the corresponding type S4 to obtain a diagnosis result.
Further, the fault diagnosis equipment used for fault diagnosis is set according to production requirements.
Further, the fault diagnosis includes the steps of:
a1, classifying according to the type of the production equipment, and drawing a data curve when the corresponding production equipment operates normally;
a2, classifying the historical working condition information according to the type of the production equipment, and drawing a corresponding historical data curve;
a3, comparing the historical data curve with the corresponding data curve in normal operation, judging the contact ratio, and if the historical data curve is basically normal with the corresponding data curve in normal operation, the production equipment corresponding to the historical data curve works normally; otherwise, the fault occurs;
a4, overhauling the corresponding equipment according to the fault, marking the fault type on the corresponding real-time data curve, and forming a data curve with fault analysis to replace the data curve in A1;
a5, repeating A1-A4 to obtain a data curve with fault analysis;
a6, classifying the real-time working condition information according to the type of production equipment, and drawing a corresponding real-time data curve;
a7, comparing the real-time data curve with the corresponding data curve with fault analysis, judging the contact ratio, and when the real-time data curve and the corresponding data curve with fault analysis are basically normal, the production equipment corresponding to the real-time data curve works normally; otherwise, a real-time diagnosis result is obtained.
Compared with the prior art, the invention has the following beneficial effects:
the big data system with the fault diagnosis function, which is applied to industrial production, adopts the equipment of the Internet of things to acquire the working condition information of the production equipment; the big data equipment carries out modeling and control strategy forming according to the working condition information collected by the Internet of things equipment, forms a control step and an early warning signal after fault diagnosis is carried out according to the established model and the control strategy, and controls the production equipment through the control step; meanwhile, the infrared temperature measurement equipment is connected with the big data equipment, temperature field data in the monitoring range of the infrared temperature measurement equipment are monitored and transmitted to the big data equipment, and the big data equipment sends out corresponding temperature early warning signals according to the collected temperature measurement data; and the early warning equipment performs early warning according to the early warning signal or the temperature early warning signal. The modeling and control of industrial production through the big data system are realized, the monitoring and maintenance of production are convenient through the big data system with the fault diagnosis function applied to the industrial production, meanwhile, prompt can be provided through fault diagnosis, further damage of equipment is reduced, and resource loss is reduced.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a big data system with a fault diagnosis function applied to industrial production.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the big data system with fault diagnosis function applied to industrial production provided by the present invention includes: production equipment, thing networking device, big data equipment, infrared temperature measurement equipment and early warning equipment.
The production equipment is equipment required by industrial production;
the Internet of things equipment is connected with the production equipment and is used for acquiring working condition information of the production equipment; the working condition information comprises: basic equipment information, running condition information, actual service life information and replacement frequency information of the production equipment;
the big data equipment is respectively connected with the Internet of things equipment and the production equipment, and is used for modeling and forming a control strategy according to the working condition information acquired by the Internet of things equipment, forming a control step and an early warning signal after fault diagnosis is carried out according to the established model and the control strategy, and controlling the production equipment through the control step;
the infrared temperature measurement equipment is connected with the big data equipment, is used for monitoring temperature field data in the monitoring range of the infrared temperature measurement equipment and transmitting the temperature field data to the big data equipment, and the big data equipment sends out corresponding temperature early warning signals according to the collected temperature measurement data; and
and the early warning equipment is connected with the big data equipment and performs early warning according to the early warning signal or the temperature early warning signal.
The production equipment comprises: the equipment on the production line, the warehouse equipment, the intelligent trolley, the tooling equipment and the control equipment corresponding to the equipment on the production line, the warehouse equipment and the intelligent trolley.
The information of the equipment on the production line comprises a task order number, the number of products and various production process information.
The information of the warehouse equipment comprises the codes, the positions, the corresponding equipment states and the like of the equipment.
The tool equipment information comprises tool codes, tool loading, tool service life, tool quantity types and the like.
Continuing to refer to fig. 1, the internet of things device includes a data collector for collecting various data of the current production, a signal output end of the data collector is connected with the master microcontroller through the microcontroller, and a signal is sent to the big data device through the master microcontroller.
The big data device includes:
the real-time data transmission module is connected with the physical network equipment and used for receiving data acquired by the Internet of things equipment;
the big data server is connected with the real-time data transmission module and is used for processing the historical data acquired by the real-time data transmission module to form a control strategy;
the big data server is connected with the real-time data transmission module and used for modeling the historical data collected by the real-time data transmission module and correcting the model according to the real-time data collected by the real-time data transmission module; and
the fault diagnosis equipment is respectively connected with the big data server and the model database and is used for carrying out fault diagnosis according to the model corrected by the big data server and fault data in the control strategy generated by the big data server to form a fault diagnosis result;
and the master controller is respectively connected with the fault diagnosis equipment and the production equipment, is used for combining the model modified by the big data server and the control strategy generated by the big data server, forming a control step and an early warning signal according to a fault diagnosis result, simultaneously storing the control step and the early warning signal to the big data server, and controlling the production equipment according to the control step and sending the control step and the early warning signal to the early warning equipment for early warning.
The early warning signal includes: the early warning signals of the equipment on the production line, the warehouse equipment, the intelligent trolley, the tooling equipment and the control equipment corresponding to the equipment on the production line, the warehouse equipment and the intelligent trolley.
The fault diagnosis equipment is one or the combination of more than two of a digital signal processing device, an embedded device and a computer, and is selected according to industrial production.
The fault diagnosis of the fault diagnosis equipment comprises two methods, and can be correspondingly adopted or simultaneously adopted according to production requirements. The fault diagnosis equipment used for fault diagnosis is set according to production requirements, can be one, and can also be set with corresponding number according to the type of the production equipment.
The first fault diagnosis method comprises the following steps:
s1, classifying according to the type of the production equipment according to the working condition information;
s2, normalizing the working condition information of one type of production equipment;
s3, setting the output of the neural network as the fault type and fault degree of the uploading equipment, and training the normalized data through the neural network to obtain the trained neural network;
s4, repeating S2-S3 to obtain trained neural networks corresponding to all production equipment;
and S5, classifying the real-time collected working condition information through S1, and performing fault diagnosis through the correspondingly trained neural network in the corresponding type S4 to obtain a diagnosis result.
The second fault diagnosis method includes the steps of:
a1, classifying according to the type of the production equipment, and drawing a data curve when the corresponding production equipment operates normally;
a2, classifying the historical working condition information according to the type of the production equipment, and drawing a corresponding historical data curve;
a3, comparing the historical data curve with the corresponding data curve in normal operation, judging the contact ratio, and if the historical data curve is basically normal with the corresponding data curve in normal operation, the production equipment corresponding to the historical data curve works normally; otherwise, the fault occurs;
a4, overhauling the corresponding equipment according to the fault, marking the fault type on the corresponding real-time data curve, and forming a data curve with fault analysis to replace the data curve in A1;
a5, repeating A1-A4 to obtain a data curve with fault analysis;
a6, classifying the real-time working condition information according to the type of production equipment, and drawing a corresponding real-time data curve;
a7, comparing the real-time data curve with the corresponding data curve with fault analysis, judging the contact ratio, and when the real-time data curve and the corresponding data curve with fault analysis are basically normal, the production equipment corresponding to the real-time data curve works normally; otherwise, a real-time diagnosis result is obtained.
The working principle of the big data system with fault diagnosis function applied to industrial production of the invention is explained in detail so that the person skilled in the art can understand the invention more:
the Internet of things equipment acquires the working condition information of the production equipment through a corresponding data acquisition unit, transmits the working condition information to the master microcontroller, and transmits a signal to a real-time data transmission module of the big data equipment through the master microcontroller; the real-time data transmission module transmits the received real-time data to the big data server and the big data server respectively; the big data server performs data processing on the real-time data to form a control strategy; the big data server corrects the established model according to the real-time data; the fault diagnosis equipment carries out fault diagnosis according to the model corrected by the big data server and fault data in a control strategy generated by the big data server to form a fault diagnosis result; the master controller combines the model corrected by the big data server and the control strategy generated by the big data server, forms a control step and an early warning signal according to the fault diagnosis result, simultaneously stores the control step and the early warning signal to the big data server, controls the production equipment according to the control step and sends the control step and the early warning signal to the early warning equipment for early warning; meanwhile, the infrared temperature measurement equipment monitors temperature field data in the monitoring range of the infrared temperature measurement equipment and transmits the temperature field data to the big data equipment, and the big data equipment sends out corresponding temperature early warning signals according to the collected temperature measurement data; and the early warning equipment performs early warning according to the early warning signal or the temperature early warning signal. The method for fault diagnosis by the fault diagnosis device can be selected according to the requirement.
When the simulation is built, the big data equipment and the big data server in the big data equipment are built at first, and the entity factory is output and controlled according to the simulation big data server. Likewise, replacing the big data server with a digital twin server also enables a digital twin system by replacing the big data device with a digital twin device.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (10)
1. Big data system that has failure diagnosis function for industrial production, its characterized in that: the method comprises the following steps:
production equipment which is equipment required by industrial production;
the Internet of things equipment is connected with the production equipment and is used for acquiring the working condition information of the production equipment;
the big data equipment is respectively connected with the Internet of things equipment and the production equipment, and is used for modeling and forming a control strategy according to the working condition information acquired by the Internet of things equipment, forming a control step and an early warning signal after fault diagnosis is carried out according to the established model and the control strategy, and controlling the production equipment through the control step;
the infrared temperature measurement equipment is connected with the big data equipment, is used for monitoring temperature field data in the monitoring range of the infrared temperature measurement equipment and transmitting the temperature field data to the big data equipment, and the big data equipment sends out corresponding temperature early warning signals according to the collected temperature measurement data; and
and the early warning equipment is connected with the big data equipment and carries out early warning according to the early warning signal or the temperature early warning signal.
2. The big data system with fault diagnosis function applied to industrial production according to claim 1, wherein: the production apparatus includes: the equipment on the production line, the warehouse equipment, the intelligent trolley, the tooling equipment and the control equipment corresponding to the equipment on the production line, the warehouse equipment and the intelligent trolley.
3. The big data system with fault diagnosis function applied to industrial production according to claim 1, wherein: the Internet of things equipment comprises a data collector for collecting various data produced at present, wherein a signal output end of the data collector is connected with a main microcontroller through the microcontroller, and the main microcontroller sends signals to the big data equipment.
4. The big data system with fault diagnosis function applied to industrial production according to claim 1, wherein: the big data device includes:
the real-time data transmission module is connected with the physical network equipment and used for receiving data acquired by the Internet of things equipment;
the big data server is connected with the real-time data transmission module and is used for carrying out data processing on historical data acquired by the real-time data transmission module to form a control strategy;
the model database is connected with the real-time data transmission module and is used for modeling the historical data collected by the real-time data transmission module and correcting the model according to the real-time data collected by the real-time data transmission module; and
the fault diagnosis equipment is respectively connected with the big data server and the model database and is used for carrying out fault diagnosis according to the model corrected by the big data server and fault data in the control strategy generated by the big data server to form a fault diagnosis result;
and the master controller is respectively connected with the fault diagnosis equipment and the production equipment, is used for combining the model modified by the big data server and the control strategy generated by the big data server, forming a control step and an early warning signal according to the fault diagnosis result, simultaneously storing the control step and the early warning signal into the big data server, and controlling the production equipment according to the control step and sending the control step and the early warning signal to the early warning equipment for early warning.
5. The big data system with fault diagnosis function applied to industrial production according to claim 1, wherein: the working condition information comprises: basic equipment information, running condition information, actual service life information and replacement frequency information of the production equipment.
6. The big data system with fault diagnosis function applied to industrial production according to claim 2, wherein: the early warning signal includes: the early warning signals of the equipment on the production line, the warehouse equipment, the intelligent trolley, the tooling equipment and the control equipment corresponding to the equipment on the production line, the warehouse equipment and the intelligent trolley.
7. The big data system with fault diagnosis function applied to industrial production according to claim 4, wherein: the fault diagnosis equipment is one or the combination of more than two of a digital signal processing device, an embedded device and a computer, and is selected according to industrial production.
8. The big data system with fault diagnosis function applied to industrial production according to claim 1, wherein: the fault diagnosis comprises the following steps:
s1, classifying according to the type of the production equipment according to the working condition information;
s2, normalizing the working condition information of one type of production equipment;
s3, setting the output of the neural network as the fault type and fault degree of the uploading equipment, and training the normalized data through the neural network to obtain the trained neural network;
s4, repeating S2-S3 to obtain trained neural networks corresponding to all production equipment;
and S5, classifying the real-time collected working condition information through S1, and performing fault diagnosis through the correspondingly trained neural network in the corresponding type S4 to obtain a diagnosis result.
9. The big data system with fault diagnosis function applied to industrial production according to claim 8, wherein: and the fault diagnosis equipment adopted by the fault diagnosis is set according to the production requirement.
10. The big data system with fault diagnosis function applied to industrial production according to claim 1, wherein: the fault diagnosis comprises the following steps:
a1, classifying according to the type of the production equipment, and drawing a data curve when the corresponding production equipment operates normally;
a2, classifying the historical working condition information according to the type of the production equipment, and drawing a corresponding historical data curve;
a3, comparing the historical data curve with the corresponding data curve in normal operation, judging the contact ratio, and if the historical data curve is basically normal with the corresponding data curve in normal operation, the production equipment corresponding to the historical data curve works normally; otherwise, the fault occurs;
a4, overhauling the corresponding equipment according to the fault, marking the fault type on the corresponding real-time data curve, and forming a data curve with fault analysis to replace the data curve in A1;
a5, repeating A1-A4 to obtain a data curve with fault analysis;
a6, classifying the real-time working condition information according to the type of production equipment, and drawing a corresponding real-time data curve;
a7, comparing the real-time data curve with the corresponding data curve with fault analysis, judging the contact ratio, and when the real-time data curve and the corresponding data curve with fault analysis are basically normal, the production equipment corresponding to the real-time data curve works normally; otherwise, a real-time diagnosis result is obtained.
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| CN113205248A (en) * | 2021-04-27 | 2021-08-03 | 西安热工研究院有限公司 | Regulating valve fault early warning system and method based on big data medium parameter diagnosis |
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