CN115034408A - Internet of things maintenance prediction system for key equipment in seamless steel tube production - Google Patents

Internet of things maintenance prediction system for key equipment in seamless steel tube production Download PDF

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Publication number
CN115034408A
CN115034408A CN202210551823.8A CN202210551823A CN115034408A CN 115034408 A CN115034408 A CN 115034408A CN 202210551823 A CN202210551823 A CN 202210551823A CN 115034408 A CN115034408 A CN 115034408A
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equipment
internet
seamless steel
steel tube
analyzing
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高万峰
王胜永
刘金刚
李凤魁
李万明
吴纪峰
栾子君
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Xinpengyuan Intelligent Equipment Group Co ltd
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Xinpengyuan Intelligent Equipment Group Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
    • 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
    • Y02P90/80—Management or planning

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Abstract

The invention relates to a seamless steel tube production key equipment Internet of things maintenance prediction system, which comprises the following steps: s1: constructing a perception layer; s2: constructing a network layer; s3: constructing an application layer; s4: analyzing the signal; s5: carrying out intelligent diagnosis on the equipment; s6: predictive maintenance of the equipment. The internet of things maintenance prediction system for the key equipment for seamless steel tube production timely, correctly and effectively judges various abnormal states in the operation process of the equipment, prevents and eliminates faults, reduces the hazard of the faults, guides the operation of the equipment as necessary, ensures the safety, stability and economy of the operation, determines reasonable troubleshooting opportunity and items, reasonably prolongs the service life of the equipment and reduces maintenance cost, monitors and analyzes the vibration, noise, current, temperature and oil quality of the equipment by a sensor, intelligently monitors the equipment, judges the development trend of the equipment, and diagnoses fault occurrence parts and reasons of the faults.

Description

Internet of things maintenance prediction system for key equipment in seamless steel tube production
Technical Field
The invention relates to the technical field of seamless steel pipe maintenance, in particular to an Internet of things maintenance prediction system for key equipment in seamless steel pipe production.
Background
Seamless steel pipes are steel pipes which are punched by a whole round steel and have no welding seams on the surface, and are called seamless steel pipes. The seamless steel pipe may be hot-rolled, cold-drawn, extruded, pipe-jacked, etc., depending on the production method.
In the process of production and processing of the seamless steel tube in the production line, a large number of large mechanical equipment are needed, and the operation condition of the mechanical equipment for producing the seamless steel tube also draws attention of people due to the huge economic value brought by the machinery. However, in the prior art, because the seamless steel tube mechanical equipment has a complex structure and high working strength, some faults often occur during operation, so that the production progress of the seamless steel tube is influenced, the production efficiency of enterprises is greatly influenced, and huge benefits and social benefits are lost.
Therefore, aiming at the problems, the maintenance prediction system for the internet of things of key equipment for seamless steel tube production is provided.
Disclosure of Invention
The invention aims to provide a seamless steel tube production key equipment Internet of things maintenance prediction system, which is characterized by comprising the following steps:
s1: a perception layer is constructed, a sensor is arranged on a monitored object, a data acquisition system acquires equipment running state and process information, and the running state and the process information are sent to a data center of a network layer through a wired and wireless sensor network;
s2: a network layer is constructed, and high-speed data transmission, storage, data analysis, scheduling, release and safety protection of data information are completed by utilizing a database, a cloud platform distributed application system and a network equipment system;
s3: constructing an application layer, wherein the application layer utilizes a big data machine learning technology to train and learn a large amount of accumulated fault cases and maintenance record data to obtain an intelligent diagnosis model and an intelligent maintenance decision model;
s4: analyzing the signals, and analyzing a spectrum array, a frequency spectrum, a time domain, a wavelet packet and a wavelet through data transmitted by a sensor of a sensing layer and an acquisition data system;
s5: carrying out intelligent diagnosis on the equipment, and carrying out intelligent diagnosis on the equipment through an application layer intelligent diagnosis model and an intelligent maintenance decision model;
s6: and (4) performing predictive maintenance on the equipment through the equipment running state and the process information acquired by the sensing layer.
Preferably, in step S1, the sensor performs online detection, acquisition, analysis on the device test state parameters, and uploads the parameters in time.
Preferably, in the step S1, the apparatus includes a pipe blank and pipe blank heating apparatus, a piercing apparatus, a pipe blank rolling apparatus, a steel pipe reheating apparatus, a sizing and reducing apparatus, and a steel pipe cooling and finishing apparatus.
Preferably, in step S2, the network layer network is configured by a 4G private network, a wireless intranet, a wired intranet, and a low-power communication network, and realizes high-speed data transmission.
Preferably, in step S5, the device is detected online by the sensor according to the fault information, and a corresponding self-diagnostic status report is uploaded.
Preferably, in step S6, the device information obtained by the data acquisition system includes the service life of the mechanical component and the continuous safe working time of the machine, and the vibration, noise, current, temperature and oil quality of the device are monitored and analyzed by the sensor, so that the device is intelligently monitored, the future development trend of the device is judged, and the occurrence position and the cause of the fault are diagnosed.
Compared with the prior art, the invention has the beneficial effects that: this seamless steel pipe production key equipment thing networking maintenance prediction system:
1. the method can timely, correctly and effectively judge various abnormal states in the operation process of the equipment, prevent and eliminate the fault, or reduce the hazard of the fault to the minimum degree, and simultaneously carry out necessary guidance on the operation of the equipment to ensure the safety, stability and economy of the operation.
2. The reasonable fault maintenance time and items are determined, so that the equipment is ensured to be safe and not to have serious equipment faults when in operation with diseases, the equipment is ensured to be actually problematic when in shutdown inspection, the service life of the equipment is reasonably prolonged, and the maintenance cost is reduced.
3. The vibration, noise, current, temperature and oil quality of the equipment are monitored and analyzed by the sensor, so that the equipment is intelligently monitored, the development trend of the equipment is judged, and the position of the fault and the reason of the fault are diagnosed.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described, 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.
The invention provides a technical scheme that: the Internet of things maintenance prediction system for key equipment in seamless steel tube production is characterized by comprising the following steps:
s1: a perception layer is constructed, sensors are arranged on a monitored object, a data acquisition system acquires equipment running states and process information, and the running states and the process information are sent to a data center of a network layer through wired and wireless sensor networks;
s2: a network layer is constructed, and high-speed data transmission, storage, data analysis, scheduling, release and safety protection of data information are completed by utilizing a database, a cloud platform distributed application system and a network equipment system;
s3: constructing an application layer, wherein the application layer utilizes a big data machine learning technology to train and learn a large amount of accumulated fault cases and maintenance record data to obtain an intelligent diagnosis model and an intelligent maintenance decision model;
s4: analyzing signals, analyzing a spectrum array, analyzing a spectrum and a time domain, a wavelet packet and a wavelet through a sensor of a sensing layer and data transmitted by a data acquisition system, wherein the spectrum array is used for analyzing the spectrum array of a measuring point, displaying a specified frequency band and full-frequency energy, predicting the results and analyzing the variation trend of the results, finally maintaining according to the diagnosis result, analyzing the spectrum and the time domain, analyzing the frequency spectrum and the time domain by using analysis methods such as a histogram and the like, and analyzing the wavelet packet and the wavelet packet by adopting operations such as multi-page display, range change and the like by each analysis method, so that all received signals can be directly decomposed into independent frequency bands, and the characteristics among the signals can be found out, which is an effective means for fault detection;
s5: carrying out intelligent diagnosis on the equipment, and carrying out intelligent diagnosis on the equipment through an application layer intelligent diagnosis model and an intelligent maintenance decision model;
s6: and (4) predicting and maintaining the equipment, wherein the equipment operation state and the process information are acquired through the sensing layer.
Further, in step S1, the sensor performs on-line detection, acquisition, and analysis on the device, and uploads the device test state parameters in time, so as to know the operation condition of the device, and accurately analyze and determine possible faults of the device, thereby effectively improving the maintenance efficiency of the device.
The most core part of the intelligent diagnosis and predictive maintenance system for mechanical equipment is the powerful database contained therein. The realization is to the random increase and decrease of equipment, in addition when carrying out the arrangement of measurement station, mainly sets up the picture to direct measurement station setting of carrying on. And dynamically observing data to be tested and arranged measuring points, and realizing bidirectional communication management. Generally speaking, the data report predicted by the system is various and can be output or stored as a text file. In addition, the system adopts the most open internet technology for the management system, thereby greatly improving the convenience for the operation, design and development of the system.
Further, in the step S1, the apparatus includes a pipe blank and pipe blank heating apparatus, a piercing apparatus, a pipe blank rolling apparatus, a steel pipe reheating apparatus, a diameter fixing and reducing apparatus, and a steel pipe cooling and finishing apparatus.
In step S2, the network layer network is composed of a 4G private network, a wireless intranet, a wired intranet, and a low-power communication network, and high-speed data transmission is realized.
Further, in step S5, the device is detected online by the sensor according to the fault information, and a corresponding self-diagnostic status report is uploaded.
The system mainly adopts databases including a monitoring database, a report database and a structural parameter database, wherein the monitoring database mainly refers to all information for performing maintenance prediction on equipment in the mechanical equipment, and further includes the main arrangement mode of the mechanical equipment in the database and the display condition of the mechanical equipment in a tree structure. The report database mainly refers to output of a standard format of predicted reports and data, fault report analysis, equipment maintenance record reports, equipment test analysis reports and the like. The structural parameter database mainly refers to an area where structural parameters of equipment elements are stored, and generally includes a monitoring component library and a transmission route library.
Further, in step S6, the device information obtained by the data collection system includes the service life of the mechanical component and the continuous safe working time of the machine, and the vibration, noise, current, temperature and oil quality of the device are monitored and analyzed by the sensor, so that the device is intelligently monitored, the future development trend of the device is judged, and the occurrence position and the cause of the fault are diagnosed.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing embodiments, or equivalents may be substituted for elements thereof.

Claims (6)

1. The internet of things maintenance prediction system for key equipment in seamless steel tube production is characterized by comprising the following steps:
s1: a perception layer is constructed, a sensor is arranged on a monitored object, a data acquisition system acquires equipment running state and process information, and the running state and the process information are sent to a data center of a network layer through a wired and wireless sensor network;
s2: a network layer is constructed, and high-speed data transmission, storage, data analysis, scheduling, release and safety protection of data information are completed by utilizing a database, a cloud platform distributed application system and a network equipment system;
s3: constructing an application layer, wherein the application layer utilizes a big data machine learning technology to train and learn a large amount of accumulated fault cases and maintenance record data to obtain an intelligent diagnosis model and an intelligent maintenance decision model;
s4: analyzing signals, analyzing a spectrum array, analyzing a spectrum and a time domain, a wavelet packet and a wavelet through a sensor of a sensing layer and data transmitted by a data acquisition system, wherein the spectrum array is used for analyzing the spectrum array of a measuring point, displaying a specified frequency band and full-frequency energy, predicting the results and analyzing the variation trend of the results, finally maintaining according to the diagnosis result, analyzing the spectrum and the time domain, analyzing the frequency spectrum and the time domain by using analysis methods such as a histogram and the like, and analyzing the wavelet packet and the wavelet packet by adopting operations such as multi-page display, range change and the like by each analysis method, so that all received signals can be directly decomposed into independent frequency bands, and the characteristics among the signals can be found out, which is an effective means for fault detection;
s5: carrying out intelligent diagnosis on the equipment, and carrying out intelligent diagnosis on the equipment through an application layer intelligent diagnosis model and an intelligent maintenance decision model;
s6: and (4) performing predictive maintenance on the equipment through the equipment running state and the process information acquired by the sensing layer.
2. The internet of things maintenance prediction system for key equipment in seamless steel tube production according to the claim is characterized in that: in step S1, the sensor performs online detection, acquisition, analysis and timely uploading of the device test state parameters to the device, so as to know the operation condition of the device and accurately analyze and judge the possible faults of the device, thereby effectively improving the maintenance efficiency of the device.
3. The internet of things maintenance prediction system for key equipment in seamless steel tube production according to the claim is characterized in that: in the step S1, the apparatus includes a pipe blank and pipe blank heating apparatus, a piercing apparatus, a pipe blank rolling apparatus, a steel pipe reheating apparatus, a warp and reducing apparatus, and a steel pipe cooling and finishing apparatus.
4. The internet of things maintenance prediction system for key equipment in seamless steel tube production according to the claim is characterized in that: in step S2, the network layer network is composed of a 4G private network, a wireless intranet, a wired intranet, and a low-power communication network, and high-speed data transmission 2 is realized.
5. The internet of things maintenance prediction system for key equipment in seamless steel tube production according to the claim is characterized in that: in step S5, the device is detected online by the sensor according to the fault information, and a corresponding self-diagnostic status report is uploaded.
6. The Internet of things maintenance prediction system for key equipment in seamless steel tube production as claimed in claim, characterized in that: in step S6, the device information obtained by the data acquisition system includes the service life of the mechanical component and the continuous safe working time of the machine, and the vibration, noise, current, temperature and oil quality of the device are monitored and analyzed by the sensor, so that the device is intelligently monitored, the future development trend of the device is judged, and the occurrence position and the cause of the fault are diagnosed.
CN202210551823.8A 2022-05-21 2022-05-21 Internet of things maintenance prediction system for key equipment in seamless steel tube production Pending CN115034408A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210551823.8A CN115034408A (en) 2022-05-21 2022-05-21 Internet of things maintenance prediction system for key equipment in seamless steel tube production

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210551823.8A CN115034408A (en) 2022-05-21 2022-05-21 Internet of things maintenance prediction system for key equipment in seamless steel tube production

Publications (1)

Publication Number Publication Date
CN115034408A true CN115034408A (en) 2022-09-09

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