CN116698227A - Online monitoring and early warning method and system for temperature of extrusion roll shafts of pickling process section - Google Patents
Online monitoring and early warning method and system for temperature of extrusion roll shafts of pickling process section Download PDFInfo
- Publication number
- CN116698227A CN116698227A CN202310862766.XA CN202310862766A CN116698227A CN 116698227 A CN116698227 A CN 116698227A CN 202310862766 A CN202310862766 A CN 202310862766A CN 116698227 A CN116698227 A CN 116698227A
- Authority
- CN
- China
- Prior art keywords
- temperature
- bearing
- early warning
- data
- training
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000012544 monitoring process Methods 0.000 title claims abstract description 43
- 230000008569 process Effects 0.000 title claims description 29
- 238000005554 pickling Methods 0.000 title claims description 26
- 238000001125 extrusion Methods 0.000 title description 2
- 238000012549 training Methods 0.000 claims abstract description 40
- 239000002253 acid Substances 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000012423 maintenance Methods 0.000 claims abstract description 8
- 238000005406 washing Methods 0.000 claims abstract description 8
- 229910000831 Steel Inorganic materials 0.000 claims description 17
- 239000010959 steel Substances 0.000 claims description 17
- 238000012795 verification Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 238000010200 validation analysis Methods 0.000 claims description 5
- 230000006855 networking Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 2
- 238000005097 cold rolling Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 7
- 238000003860 storage Methods 0.000 description 7
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
- 238000004140 cleaning Methods 0.000 description 4
- 238000004590 computer program Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012800 visualization Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 239000000853 adhesive Substances 0.000 description 2
- 230000001070 adhesive effect Effects 0.000 description 2
- 229910052742 iron Inorganic materials 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000003466 welding Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000008237 rinsing water Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
-
- 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
-
- 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]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention belongs to the technical field of cold rolling treatment lines, and particularly provides an on-line monitoring and early warning method and system for the temperature of a squeezing roller of an acid washing process section, wherein the method comprises the following steps: acquiring comprehensive data of the temperature and vibration of the bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet; processing the comprehensive data to form sample data and training the sample data to obtain a bearing temperature prediction model; inputting real-time data of bearing seat temperature and vibration into the bearing temperature prediction model, and outputting predicted bearing temperature in a certain future time window; and obtaining future early warning suggestions according to the early warning grade and the predicted bearing temperature. The wireless intelligent gateway is communicated with the wireless Wen Zhen sensor to acquire vibration and temperature data, so that the wiring difficulty is reduced, and the wireless intelligent gateway is easy to reform and implement; the bearing temperature in a certain time window is predicted by a trained bearing temperature prediction model, so that the bearing temperature can be predicted in advance, data support is provided for the operation and maintenance of the wringing roller equipment, and accidents are avoided.
Description
Technical Field
The disclosure relates to the technical field of cold rolling treatment lines, in particular to an on-line monitoring and early warning method and system for the temperature of a squeezing roller of an acid washing process section.
Background
The pickling process section mainly comprises a pickling tank and a rinsing tank, wherein acid liquor in the pickling tank and iron scales on the surface of the strip steel are subjected to chemical reaction, the iron scales are removed, and the rinsing tank is used for cleaning residual pickling on the surface of the strip steel which is discharged from the pickling tank.
The pickling tank and the rinsing tank are made of PPH or steel lining rubber, are inflammable, a squeezing roller is arranged between an inlet and an outlet of the pickling tank and each pickling tank, so that the acid liquor amount carried out by the strip steel is minimized, and the squeezing roller is arranged between each two sections of the rinsing tank, so that the rinsing water amount carried out by the strip steel is minimized.
In the running process of the pickling process section, as the wringing roller runs abnormally, the temperatures of the wringing roller bearing and the bearing seat are continuously increased, and finally the pickling tank body and the rinsing tank body are ignited, so that the pickling tank and the rinsing tank are seriously damaged, the unit is stopped, and the damage is serious.
Disclosure of Invention
The disclosure aims at solving at least one of the technical problems existing in the prior art, and provides an on-line monitoring and early warning method and system for the temperature of a squeezing roller of an acid washing process section.
In a first aspect, the present disclosure provides an on-line monitoring and early warning method for a temperature of a wiping roller of an acid cleaning process section, including:
acquiring comprehensive data of the temperature and vibration of the bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet;
processing the comprehensive data to form sample data and training the sample data to obtain a bearing temperature prediction model;
inputting real-time data of bearing seat temperature and vibration into the bearing temperature prediction model, and outputting predicted bearing temperature in a certain future time window;
and obtaining future early warning suggestions according to the early warning grade and the predicted bearing temperature.
Preferably, the processing the integrated data to form sample data and training the sample data to obtain a bearing temperature prediction model specifically includes:
processing the comprehensive data of the temperature and vibration of the bearing seat to form sample data;
based on the sample data, deepAR, informer, LSTNet, MLP, NBEATS, NHiTS, RNN, SCINet, TCN, TFT or transducer models are selected for training and verification until the bearing temperature prediction model predicts that the deviation is controlled within an allowable range.
Preferably, the temperature of the bearing seat is a target to be predicted, the strip steel brand, the strip steel width, the strip steel thickness, the strip steel speed and the vibration of the bearing seat are covariates, and the data are divided into training data, verification data and a test data set.
Preferably, the ratio of the training data, the validation data and the test data set is 7:2:1.
Preferably, the selecting the LSTNet model for training and verification based on the sample data specifically includes:
firstly, model networking is carried out by using PaddleTS, and the time sequence length of model input, the time sequence length of model output, a loss function, an optimization algorithm, optimizer parameters and the maximum number of rounds of training are predefined;
model training and validation of sample data using lstm. Fit (train_dataset, val_dataset), where train_dataset is the training dataset and val_dataset is the validation dataset;
in the training process and after the training is finished, MAE (Mean Absolute Error) and MSE (Mean Squared Error) are adopted to evaluate the model prediction effect, and the bearing temperature prediction model is obtained when the effect reaches a preset value.
Preferably, lstm is used to save the trained bearing temperature prediction model after the training process is completed.
Preferably, the acquiring comprehensive data of the bearing seat temperature and vibration from the wireless Wen Zhenchuan sensor through the ethernet specifically includes: continuously acquiring comprehensive data of the temperature and vibration of the bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet; and storing the sample data to a bearing temperature monitoring and early warning server and visually displaying the sample data on a display of the bearing temperature monitoring and early warning server.
Preferably, the early warning level includes: recommended care to run, recommended necessary checks at appropriate times, recommended maintenance within a recently planned outage, recommended maintenance action to be taken at the first time.
In a second aspect, the present disclosure provides an on-line monitoring and early warning system for a temperature of a wiping roller bearing of an acid cleaning process section, the system being usable for implementing an on-line monitoring and early warning method for a temperature of a wiping roller bearing of an acid cleaning process section, the system comprising:
the data acquisition module is configured to acquire comprehensive data of the temperature and vibration of the bearing seat from the wireless Wen Zhenchuan sensor through the Ethernet;
the model training module is configured to process the comprehensive data to form sample data and train the sample data to obtain a bearing temperature prediction model;
the prediction module is configured to input real-time data of bearing seat temperature and vibration into the bearing temperature prediction model and output predicted bearing temperature in a certain future time window;
and the early warning module is configured to obtain future early warning suggestions according to the early warning grade and the predicted bearing temperature.
In a third aspect, the present disclosure provides an electronic device comprising:
one or more processors;
a memory for storing one or more programs;
and when the one or more programs are executed by the one or more processors, the one or more processors realize an on-line monitoring and early warning method for the temperature of the squeezing roller bearing of the pickling process section.
Drawings
FIG. 1 is a flow chart of an on-line monitoring and early warning method for the temperature of a squeezing roller of an acid washing process section provided by an embodiment of the disclosure;
FIG. 2 is a diagram of an on-line monitoring and early warning system for the temperature of a wiping roller bearing in an acid pickling process section provided by an embodiment of the disclosure;
FIG. 3 is a training flow chart of a bearing temperature prediction model provided by an embodiment of the present disclosure;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present disclosure, the present disclosure will be described in further detail with reference to the accompanying drawings and detailed description.
Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, are not intended to be inclusive of any order, quantity, or importance, but rather are used to distinguish between different components. Also, the terms "a," "an," or "the" and similar terms are not intended to be limiting in number, but rather are intended to mean that there is at least one. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used only for the relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Like elements are designated with like reference numerals throughout the various figures. For purposes of clarity, the various features of the drawings are not drawn to scale. Furthermore, some well-known portions may not be shown in the drawings.
Numerous specific details of the present disclosure, such as construction, materials, dimensions, processing techniques and technologies, are set forth in the following description in order to provide a more thorough understanding of the present disclosure. However, as will be understood by those skilled in the art, the present disclosure may be practiced without these specific details.
As shown in fig. 1 to 3, the embodiment of the disclosure provides an on-line monitoring and early warning method for the temperature of a squeezing roller of an acid washing process section, which comprises the following steps:
s1, acquiring comprehensive data of temperature and vibration of a bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet;
s2, processing the comprehensive data to form sample data and training the sample data to obtain a bearing temperature prediction model;
s3, inputting real-time data of bearing seat temperature and vibration into the bearing temperature prediction model, and outputting predicted bearing temperature in a certain future time window;
and S4, obtaining future early warning suggestions according to the early warning grade and the predicted bearing temperature.
It should be noted that the order of the above steps may be arbitrarily disturbed and is not limited by this disclosure.
When the method is realized, an on-line monitoring and early warning system for the temperature of the wringing roller bearing in the pickling process section needs to be built, and the on-line monitoring and early warning system comprises a wringing roller bearing seat C1, a wireless Wen Zhen sensor C2, a wireless intelligent gateway C3, an Ethernet C4, a bearing temperature monitoring early warning server C5 and a temperature monitoring early warning module C6. The wireless Wen Zhenchuan sensor C2 is installed on the wringing roller bearing seat in a connection mode such as an adhesive/welding base or a transfer bolt, the wireless intelligent gateway C3 collects temperature and vibration data of the wireless Wen Zhen sensor C2 through wireless communication, the data are transmitted to a bearing temperature monitoring and early warning server through the Ethernet C4, and the temperature monitoring and early warning module C6 monitors the temperature of the wringing roller bearing and performs early warning.
In the production process, the temperature monitoring and early warning module C6 is operated, and the temperature monitoring and early warning module C6 mainly comprises a data acquisition visualization module and a bearing temperature prediction training module. The data acquisition visualization module continuously acquires comprehensive data of bearing seat temperature and vibration from the wireless Wen Zhen sensor C2 through the Ethernet C4. Storing sample data to a hard disk of a bearing temperature monitoring and early warning server C5 and visually displaying the sample data on a display of the bearing temperature monitoring and early warning server C5, wherein visually displayed information comprises: the number of the squeeze roller bearing, real-time vibration temperature parameters and running statistical information. The flow of the bearing temperature prediction training module is shown in fig. 3, the comprehensive data of the bearing seat temperature and vibration are processed to form sample data, wherein the bearing seat temperature is a target to be predicted, the strip steel brand, the strip steel width, the strip steel thickness, the strip steel speed and the bearing seat vibration are covariates, the data are divided into training data, verification data and test data, and a DeepAR, informer, LSTNet, MLP, NBEATS, NHiTS, RNN, SCINet, TCN, TFT or transducer model is selected for training and verification based on the sample data until the prediction deviation of the bearing temperature prediction model is controlled within an allowable range.
Taking LSTNet as an example:
firstly, model networking is carried out by using PaddleTS (time sequence modeling Python library based on hundred-degree deep learning framework PaddlePaddle), mainly predefining parameters such as in_channel_len (time sequence length of model input), out_channel_len (time sequence length of model output), loss_fn (loss function), optimizer_fn (optimization algorithm), optimizer_params (optimizer parameters) and max_epochs (maximum number of training), after the parameters of the model are predefine, model training and verification are carried out by using a data set, wherein the train_dataset is a training data set, the val_dataset is a verification data set, and after the training process and training are completed, hydrogen concentration prediction model prediction effects are estimated by adopting MAE (Mean Absolute Error) and MSE (Mean Squared Error), and the smaller MAE and MSE are the better. After training, a bearing temperature prediction model is obtained, the trained model is saved by using lstm (lstm. Save) ("lstm"), and the model can be subsequently called by a bearing temperature online early warning module for predicting the bearing temperature in a certain future time window.
The temperature monitoring and early warning module collects real-time bearing seat vibration and temperature data on the one hand, and alarms when the bearing seat vibration and temperature data exceed a threshold value, and on the other hand, a trained bearing temperature prediction model is called to predict the bearing seat temperature in a certain time window, and early warning grades are divided into 4 grades, and the temperature monitoring and early warning module comprises: the recommended care is to be run, the recommended necessary checks are to be made at the appropriate time, the recommended maintenance is to be performed within a recently planned downtime, and the maintenance measures are to be taken at the first time. Thereby avoiding the tank body ignition accident caused by abnormal temperature of the squeeze roller bearing.
The data acquisition visualization module continuously performs data acquisition, acquires bearing seat vibration and temperature data of different brands, different widths, different thicknesses, different speeds and different time periods, visually displays on a bearing temperature monitoring and early warning server C5 display, and the display information comprises: the number of the squeeze roller bearing, real-time vibration temperature parameters and running statistical information.
And continuously monitoring the temperature of the wringing roller bearing and carrying out early warning under the condition of full-speed operation of the production line, so as to avoid the tank body ignition accident caused by abnormal temperature of the wringing roller bearing.
The embodiment of the disclosure also provides an on-line monitoring and early warning system for the temperature of the extruding roller bearing of the pickling process section, wherein the system can be used for realizing an on-line monitoring and early warning method for the temperature of the extruding roller bearing of the pickling process section, and the system comprises the following components:
the data acquisition module is configured to acquire comprehensive data of the temperature and vibration of the bearing seat from the wireless Wen Zhenchuan sensor through the Ethernet;
the model training module is configured to process the comprehensive data to form sample data and train the sample data to obtain a bearing temperature prediction model;
the prediction module is configured to input real-time data of bearing seat temperature and vibration into the bearing temperature prediction model and output predicted bearing temperature in a certain future time window;
and the early warning module is configured to obtain future early warning suggestions according to the early warning grade and the predicted bearing temperature.
The beneficial effects are that:
the temperature vibration sensor is used for collecting vibration and temperature data of the bearing seat, and the Wen Zhenchuan sensor is arranged on the wringing roller bearing seat in a connection mode such as an adhesive/welding base or a transfer bolt, so that the installation is convenient;
the wireless intelligent gateway is communicated with the wireless Wen Zhen sensor to acquire vibration and temperature data, so that the wiring difficulty is reduced, and the wireless intelligent gateway is easy to reform and implement;
the bearing temperature in a certain time window is predicted by a trained bearing temperature prediction model, so that the bearing temperature can be predicted in advance, data support is provided for the operation and maintenance of the wringing roller equipment, and accidents are avoided.
Fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the present invention. As shown in fig. 4, an embodiment of the present invention provides an electronic device 1300, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320, wherein the processor 1320 executes the computer program 1311 to implement the following steps: s1, acquiring comprehensive data of temperature and vibration of a bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet;
s2, processing the comprehensive data to form sample data and training the sample data to obtain a bearing temperature prediction model;
s3, inputting real-time data of bearing seat temperature and vibration into the bearing temperature prediction model, and outputting predicted bearing temperature in a certain future time window;
and S4, obtaining future early warning suggestions according to the early warning grade and the predicted bearing temperature.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (10)
1. The on-line monitoring and early warning method for the temperature of the extruding roller bearing in the pickling process section is characterized by comprising the following steps:
acquiring comprehensive data of the temperature and vibration of the bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet;
processing the comprehensive data to form sample data and training the sample data to obtain a bearing temperature prediction model;
inputting real-time data of bearing seat temperature and vibration into the bearing temperature prediction model, and outputting predicted bearing temperature in a certain future time window;
and obtaining future early warning suggestions according to the early warning grade and the predicted bearing temperature.
2. The online monitoring and early warning method for the temperature of the squeeze roller bearing in the pickling process section according to claim 1, wherein the method is characterized in that the comprehensive data are processed to form sample data and trained to obtain a bearing temperature prediction model, and specifically comprises the following steps:
processing the comprehensive data of the temperature and vibration of the bearing seat to form sample data;
based on the sample data, deepAR, informer, LSTNet, MLP, NBEATS, NHiTS, RNN, SCINet, TCN, TFT or transducer models are selected for training and verification until the bearing temperature prediction model predicts that the deviation is controlled within an allowable range.
3. The online monitoring and early warning method for the temperature of the squeeze roller bearing of the pickling process section according to claim 2, wherein the temperature of the bearing seat is a target to be predicted, the brand of strip steel, the width of strip steel, the thickness of strip steel, the speed of strip steel and the vibration of the bearing seat are covariates, and the data are divided into training data, verification data and a test data set.
4. The online monitoring and early warning method for the temperature of the wiping roller bearing of the pickling process section according to claim 3, wherein the ratio of the training data, the verification data and the test data set is 7:2:1.
5. The online monitoring and early warning method for the temperature of the wiping roller bearing of the pickling process section according to claim 2, wherein the method is characterized by selecting an LSTNet model for training and verification based on sample data, and specifically comprises the following steps:
firstly, model networking is carried out by using PaddleTS, and the time sequence length of model input, the time sequence length of model output, a loss function, an optimization algorithm, optimizer parameters and the maximum number of rounds of training are predefined;
model training and validation of sample data using lstm. Fit (train_dataset, val_dataset), where train_dataset is the training dataset and val_dataset is the validation dataset;
in the training process and after the training is finished, MAE (Mean Absolute Error) and MSE (Mean Squared Error) are adopted to evaluate the model prediction effect, and the bearing temperature prediction model is obtained when the effect reaches a preset value.
6. The online monitoring and early warning method for the temperature of the wiping roller bearing in the pickling process section according to claim 5, wherein after the training process is completed, a lstm is used for storing a trained bearing temperature prediction model.
7. The online monitoring and early warning method for the temperature of the extruding roller bearing of the pickling process section according to claim 1, wherein the comprehensive data of the temperature and vibration of the bearing seat is obtained from a wireless Wen Zhenchuan sensor through an Ethernet, and specifically comprises the following steps: continuously acquiring comprehensive data of the temperature and vibration of the bearing seat from a wireless Wen Zhenchuan sensor through an Ethernet; and storing the sample data to a bearing temperature monitoring and early warning server and visually displaying the sample data on a display of the bearing temperature monitoring and early warning server.
8. The method for online monitoring and early warning of the temperature of a wiping roller bearing in an acid washing process section according to claim 1, wherein the early warning level comprises: recommended care to run, recommended necessary checks at appropriate times, recommended maintenance within a recently planned outage, recommended maintenance action to be taken at the first time.
9. An on-line monitoring and early warning system for the temperature of a wiping roller bearing of an acid washing process section, which is characterized in that the system can be used for realizing the on-line monitoring and early warning method for the temperature of the wiping roller bearing of the acid washing process section according to any one of claims 1 to 8, and the system comprises:
the data acquisition module is configured to acquire comprehensive data of the temperature and vibration of the bearing seat from the wireless Wen Zhenchuan sensor through the Ethernet;
the model training module is configured to process the comprehensive data to form sample data and train the sample data to obtain a bearing temperature prediction model;
the prediction module is configured to input real-time data of bearing seat temperature and vibration into the bearing temperature prediction model and output predicted bearing temperature in a certain future time window;
and the early warning module is configured to obtain future early warning suggestions according to the early warning grade and the predicted bearing temperature.
10. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the on-line monitoring and early warning method for the temperature of the wringing roller bearing of the pickling process segment according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310862766.XA CN116698227A (en) | 2023-07-13 | 2023-07-13 | Online monitoring and early warning method and system for temperature of extrusion roll shafts of pickling process section |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310862766.XA CN116698227A (en) | 2023-07-13 | 2023-07-13 | Online monitoring and early warning method and system for temperature of extrusion roll shafts of pickling process section |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116698227A true CN116698227A (en) | 2023-09-05 |
Family
ID=87825900
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310862766.XA Pending CN116698227A (en) | 2023-07-13 | 2023-07-13 | Online monitoring and early warning method and system for temperature of extrusion roll shafts of pickling process section |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116698227A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117990221A (en) * | 2024-04-07 | 2024-05-07 | 镇江西门子母线有限公司 | Automatic bus temperature measurement inspection method and system based on RFID |
-
2023
- 2023-07-13 CN CN202310862766.XA patent/CN116698227A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117990221A (en) * | 2024-04-07 | 2024-05-07 | 镇江西门子母线有限公司 | Automatic bus temperature measurement inspection method and system based on RFID |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ahmad et al. | A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models | |
| Wang et al. | Remaining useful life prediction and optimal maintenance time determination for a single unit using isotonic regression and gamma process model | |
| CN116698227A (en) | Online monitoring and early warning method and system for temperature of extrusion roll shafts of pickling process section | |
| US10521490B2 (en) | Equipment maintenance management system and equipment maintenance management method | |
| Di Maio et al. | Combining relevance vector machines and exponential regression for bearing residual life estimation | |
| Yuan et al. | Predictive maintenance of shield tunnels | |
| Ahmad et al. | A review of condition-based maintenance decision-making | |
| Ding et al. | Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model | |
| CN116953164A (en) | On-line monitoring and early warning method and system for hydrogen concentration in pickling process section | |
| CN110469561B (en) | Device, method and system for monitoring state of hydraulic system | |
| CN113252544A (en) | Corrosion monitoring system and method for oil refining device | |
| Wen et al. | Multiple-change-point modeling and exact Bayesian inference of degradation signal for prognostic improvement | |
| WO2023165006A1 (en) | Predictive maintenance method and apparatus for industrial equipment based on health status index, and electronic device | |
| CN109766593B (en) | Method and device for evaluating buckling risk of strip steel in furnace | |
| Son et al. | Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks | |
| US20240085274A1 (en) | Hybrid bearing fault prognosis with fault detection and multiple model fusion | |
| Wang et al. | A mixed-effects model of two-phase degradation process for reliability assessment and RUL prediction | |
| CN115409066A (en) | Method and device for detecting abnormality of time series data, and computer storage medium | |
| CN114066274A (en) | Equipment performance early warning method and device | |
| Chang et al. | Temporal convolution-based sorting feature repeat-explore network combining with multi-band information for remaining useful life estimation of equipment | |
| CN210829975U (en) | Hydraulic system state monitoring system based on edge calculation and machine learning | |
| Yang et al. | Process monitoring using causal graphical models, with application to clogging detection in steel continuous casting | |
| Pei et al. | A novel machinery RUL prediction method based on exponential model and cross-domain health indicator considering first-to-end prediction time | |
| Sohlberg | Hybrid grey box modelling of a pickling process | |
| Jiang et al. | A new monitoring method for the blocking time of the taphole of blast furnace using molten iron flow images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |