CN111157622A - Graphite electrode defect detection system - Google Patents
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Abstract
The invention relates to a graphite electrode defect detection system, which comprises: the system comprises a cloud server, a first processor module, a second processor module, a knocking mechanism and a sound detection module which are connected with the second processor module, and a transmission module which is electrically connected with the first processor module; the second processor module is suitable for controlling the knocking mechanism to knock the graphite electrode; the sound detection module is suitable for detecting sound generated by knocking a graphite electrode by a knocking mechanism and sending a sound signal to the second processor module so as to be forwarded to the first processor module by the second processor module; the first processor module is adapted to send a sound signal to the cloud server through the transmission module; the cloud server is suitable for judging the defect degree of the graphite electrode according to the sound signal, so that the defect degree of the graphite electrode is automatically detected, the defects of strong subjectivity and high omission ratio caused by manual detection and other methods are avoided, and meanwhile, the labor cost is reduced.
Description
Technical Field
The invention belongs to the technical field of graphite electrode detection, and particularly relates to a graphite electrode defect detection system.
Background
The graphite electrode inevitably has the conditions of cavities, cracks and the like in the production process, which directly influences the use of the graphite electrode. In the smelting process, if the electrode is broken or damaged, the broken or damaged part falls into the smelted metal, so that the carbon content in the smelted metal is changed, and finally, the whole furnace raw material can be scrapped, and great loss can be caused.
The traditional graphite electrode detection is a manual detection or mechanical cutting-off sampling detection method, so that the detection efficiency is low, the detection precision is low, the detection is easy to miss, and the large-scale electrode detection requirement cannot be met.
Therefore, a new graphite electrode defect detection system needs to be designed based on the above technical problems.
Disclosure of Invention
The invention aims to provide a graphite electrode defect detection system.
In order to solve the above technical problem, the present invention provides a graphite electrode defect detection system, including:
the system comprises a cloud server, a first processor module, a second processor module, a knocking mechanism and a sound detection module which are connected with the second processor module, and a transmission module which is electrically connected with the first processor module;
the second processor module is suitable for controlling the knocking mechanism to knock the graphite electrode;
the sound detection module is suitable for detecting sound generated by knocking a graphite electrode by a knocking mechanism and sending sound signal data to the second processor module so as to be forwarded to the first processor module by the second processor module;
the first processor module is adapted to send sound signal data to the cloud server through the transmission module;
the cloud server is suitable for judging the defect degree of the graphite electrode according to the sound signal data.
Further, the striking mechanism includes: the pulse control circuit, the electromagnetic valve, the air pump and the air hammer;
the second processor module is suitable for the pulse control circuit to output pulse signals to control the electromagnetic valve to be conducted, so that gas provided by the gas pump enters the air hammer through the high-pressure gas pipe, and the graphite electrode is knocked by the air hammer.
Further, the sound detection module includes: a sound sensor, a conditioning circuit and an AD converter;
the sound sensor is suitable for detecting the sound generated by knocking the graphite electrode by the knocking mechanism;
the sound sensor is suitable for converting a sound signal into an electric signal and sending the electric signal to the conditioning circuit;
the conditioning circuit is suitable for amplifying and filtering an electric signal and sending the electric signal to the AD converter; the AD converter is adapted to perform analog-to-digital conversion on the amplified and filtered signal and send to a second processor module for forwarding to the first processor module via the second processor module.
Further, the cloud server is adapted to determine the degree of graphite electrode defect from the acoustic signal data, i.e., the degree of graphite electrode defect
The cloud server is suitable for acquiring the power spectral density of each frequency band according to the sound signal data through FFT (fast Fourier transform);
the frequency band includes: 500-1500Hz, 1500-2500Hz and 2500-3500 Hz.
Further, the cloud server is adapted to calculate the power ratio of the relevant frequency band from the power spectral density, i.e.
Wherein, S (f)i) Is a frequency of fiA value of the time power spectral density; s (f)i+1) Is a frequency of fi+1A value of the time power spectral density; n isjThe number of j frequency bands is divided equally according to frequency intervals; f. ofjhThe highest frequency of the jth frequency band; f. ofjlIs the lowest frequency of the jth frequency band; Δ f is fiTo fi+1The frequency interval of (a); ejIs the power of the jth frequency band; x is the number of(j)Is the ratio of the power of the jth frequency band to the sum of the powers of all frequency bands, i.e. the jth frequency bandThe power ratio of the frequency band, j, is 1,2, 3.
Further, the cloud server is adapted to establish respective vectors, i.e. power ratios, according to the relevant frequency bands
The cloud server establishes a data vector and a weight coefficient vector;
the data vector is: x ═ x(1),x(2),x(3))；
Wherein x is(1)Power ratio of 500-1500Hz frequency band; x is the number of(2)Power ratio in the 1500-; x is the number of(3)Power ratio in 2500-;
the weight coefficient vector is: w ═ w (w)(1),w(2),w(3))；
Wherein, w(1)Power ratio coefficient of 500-1500Hz frequency band; w is a(2)Power ratio coefficient of 1500-; w is a(3)The power ratio coefficient is 2500-.
Further, the cloud server is adapted to construct an optimization model from the respective vectors and to obtain a solution to the optimization model, i.e. of the optimization model
The cloud server is adapted to build an optimization model:
s.t.yi(wgxi+b)≥1-ξi；
ξi≥0i＝1,2,......,N；
wherein C is a penalty coefficient; x is the number ofiIs the ith training data vector; y isiIs xiWhen y is a class markiWhen the number is-1, it means that the graphite electrode is seriously defective, and when y isi1 indicates that the graphite electrode is not damaged, N is the number of training data, ξ is the relaxation variable, ξiRelaxation variables for the ith training data; b is an offset;
the solution of the optimization model is then: w is a*And b*；
Wherein, w*Is the solution of the coefficient vector; b*Is a solution to the bias;
the ith element which is a solution to the dual problem in the Lagrangian multiplier vector;
for all graphite electrodes to be severely defective yiData x of-1iCalculating w*xiTake all of w*xiMaximum value of (d);
to have no damage to all graphite electrodesiData x of 1iCalculating w*xiTake all of w*xiIs measured.
Further, the cloud server is adapted to construct a graphite electrode defect detection model according to the solution of the optimization model, and judge the defect degree of the graphite electrode according to the graphite electrode defect detection model, namely
The cloud server is suitable for constructing a graphite electrode defect detection model:
wherein, mugThe state mean value of the graphite electrode when the defect is serious; mu.sbThe state mean value of the lossless graphite electrode is obtained; m is the number of lossless graphite electrode states; q is a graphite electrode defect index, and q is an integer greater than 0; x is the number ofcIs the data vector of the graphite electrode currently detected.
Further, the cloud server is further adapted to obtain a graphite electrode defect index according to the graphite electrode defect detection model, and judge whether the graphite electrode is defective or not according to the graphite electrode defect index, that is, the cloud server is adapted to judge whether the graphite electrode is defective or not according to the graphite electrode defect index
The smaller the graphite electrode defect index q is, the smaller the graphite electrode defect is, and the larger the graphite electrode defect index q is, the larger the graphite electrode defect is.
The cloud server, the first processor module, the second processor module, the knocking mechanism and the sound detection module which are connected with the second processor module, and the transmission module which is electrically connected with the first processor module are adopted; the second processor module is suitable for controlling the knocking mechanism to knock the graphite electrode; the sound detection module is suitable for detecting sound generated by knocking a graphite electrode by a knocking mechanism and sending a sound signal to the second processor module so as to be forwarded to the first processor module by the second processor module; the first processor module is adapted to send a sound signal to the cloud server through the transmission module; the cloud server is suitable for judging the defect degree of the graphite electrode according to the sound signal, so that the defect degree of the graphite electrode is automatically detected, the defects of strong subjectivity and high omission factor caused by manual detection and other methods are avoided, and meanwhile, the labor cost is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic block diagram of a graphite electrode defect detection system in accordance with the present invention;
fig. 2 is a schematic block diagram of a cloud server of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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.
Example 1
Fig. 1 is a schematic block diagram of a graphite electrode defect detection system according to the present invention.
As shown in fig. 1, this embodiment 1 provides a graphite electrode defect detection system, which includes: the system comprises a cloud server, a first processor module, a second processor module, a knocking mechanism and a sound detection module which are connected with the second processor module, and a transmission module which is electrically connected with the first processor module; the first processor module may be, but is not limited to being, an ARM processor; the second processor module may be, but is not limited to being, a DSP; the transmission module may be, but is not limited to being, Wi-Fi; the second processor module is suitable for controlling the knocking mechanism to knock the graphite electrode; the sound detection module is suitable for detecting sound generated by knocking a graphite electrode by a knocking mechanism and sending sound signal data to the second processor module so as to be forwarded to the first processor module by the second processor module; the first processor module is adapted to send sound signal data to the cloud server through the transmission module; the cloud server is suitable for judging the defect degree of the graphite electrode according to the sound signal data, so that the defect degree of the graphite electrode is automatically detected, the defects of strong subjectivity and high omission ratio caused by manual detection and other methods are avoided, and meanwhile, the labor cost is reduced.
As shown in fig. 2, in the present embodiment, the cloud server includes a memory, a processor, and a communication module. The memory, the processor and the communication module are electrically connected with each other directly or indirectly to realize the data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
Wherein the memory is used for storing programs or data. The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an erasable Read-Only Memory (EPROM), an electrically erasable Read-Only Memory (EEPROM), and the like.
The processor is used to read/write data or programs stored in the memory and perform corresponding functions.
The communication module is used for establishing communication connection between the cloud server and other communication terminals through the network and receiving and transmitting data through the network.
It should be understood that the architecture shown in fig. 2 is merely a schematic of an architecture for a cloud server that may also include more or fewer components than shown in fig. 2, or have a different configuration than shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
In this embodiment, the striking mechanism includes: the pulse control circuit, the electromagnetic valve, the air pump and the air hammer; the second processor module is suitable for the pulse control circuit to output pulse signals to control the electromagnetic valve to be conducted, so that gas provided by the gas pump enters the air hammer through the high-pressure gas pipe, and the graphite electrode is knocked by the air hammer.
In this embodiment, the sound detection module includes: a sound sensor, a conditioning circuit and an AD converter; the sound sensor is suitable for detecting the sound generated by knocking the graphite electrode by the knocking mechanism; the sound sensor is suitable for converting a sound signal into an electric signal and sending the electric signal to the conditioning circuit; the conditioning circuit is suitable for amplifying and filtering an electric signal and sending the electric signal to the AD converter; the AD converter is adapted to perform analog-to-digital conversion on the amplified and filtered signal and send to a second processor module for forwarding to the first processor module via the second processor module.
In this embodiment, the second processor module may perform FFT on the current sound signal data and process the current sound signal data to obtain the power spectral density of the relevant frequency band, so as to detect the graphite electrode defect on line, and directly obtain the graphite electrode defect index through the second processor module.
In this embodiment, the cloud server is adapted to determine the defect degree of the graphite electrode according to the sound signal data, that is, the cloud server is adapted to obtain the power spectral density of each frequency band according to the sound signal data through FFT conversion; the frequency band includes: 500-1500Hz, 1500-2500Hz and 2500-3500 Hz.
In this embodiment, the cloud server is adapted to calculate the power ratio of the relevant frequency band from the power spectral density, i.e.
Calculating the power ratio x of the relevant frequency band by the following formula(j)：
Wherein, S (f)i) Is a frequency of fiA value of the time power spectral density; s (f)i+1) Is a frequency of fi+1A value of the time power spectral density; n isjThe number of j frequency bands is divided equally according to frequency intervals; f. ofjhThe highest frequency of the jth frequency band; f. ofjlIs the lowest frequency of the jth frequency band; Δ f is fiTo fi+1The frequency interval of (a); ejIs the power of the jth frequency band; x is the number of(j)The j is the ratio of the power of the jth frequency band to the sum of the powers of all frequency bands, i.e., the power ratio of the jth frequency band, and is 1,2, and 3.
The highest and lowest frequencies for each band are shown in the following table:
| j | fjl(Hz) | fjh(Hz) |
| 1 | 500 | 1500 |
| 2 | 1500 | 2500 |
| 3 | 2500 | 3500 |
in this embodiment, the cloud server is adapted to establish a corresponding vector according to the power ratio of the relevant frequency band, i.e. the cloud server is adapted to establish the corresponding vector according to the power ratio of the relevant frequency band
The cloud server establishes a data vector and a weight coefficient vector (according to the power ratio of the relevant frequency band, a weight coefficient of the relation between the power ratio of the relevant frequency band and the working state of the equipment can be obtained under the condition of corresponding historical data);
the data vector is: x ═ x(1),x(2),x(3))；
Wherein x is(1)Power ratio of 500-1500Hz frequency band; x is the number of(2)Power ratio in the 1500-; x is the number of(3)Power ratio in 2500-;
the weight coefficient vector is: w ═ w (w)(1),w(2),w(3))；
Wherein, w(1)Power ratio coefficient of 500-1500Hz frequency band; w is a(2)Power ratio coefficient of 1500-; w is a(3)The power ratio coefficient is 2500-.
In an embodiment, the cloud server is adapted to build the optimization model from the respective vectors and to obtain a solution for the optimization model, i.e. of the optimization model
The cloud server is adapted to build an optimization model (find the classification hyperplane with the largest geometrical separation, the problem can be expressed as a constrained optimization problem):
s.t.yi(wgxi+b)≥1-ξi；
ξi≥0i＝1,2,......,N；
wherein, C is a punishment coefficient (the best effect is obtained when C is 0.55 after repeated parameter adjustment); x is the number ofiIs the ith training data vector; y isiIs xiWhen y is a class markiWhen the number is-1, it means that the graphite electrode is seriously defective, and when y isi1 indicates that the graphite electrode is not damaged, N is the number of training data, ξ is the relaxation variable, ξiRelaxation variables for the ith training data; b is an offset;
the solution of the optimization model is then: w is a*And b*；
By converting the original problem into the dual problem, and using the KKT condition, the optimal solution of the dual problem is solved, and the following can be obtained:
wherein, w*Is the solution of the coefficient vector; b*Is a solution to the bias;
the ith element which is a solution to the dual problem in the Lagrangian multiplier vector;
for all graphite electrodes to be severely defective yiData x of-1iCalculating w*xiTake all of w*xiMaximum value of (d);
to have no damage to all graphite electrodesiData x of 1iCalculating w*xiTake all of w*xiIs measured.
In this embodiment, the cloud server is adapted to construct a graphite electrode defect detection model according to the solution of the optimization model, and determine the degree of defect of the graphite electrode, i.e. the degree of defect of the graphite electrode, according to the graphite electrode defect detection model
The cloud server is suitable for constructing a graphite electrode defect detection model:
wherein k is ∈ [1, m ]]；μgThe state mean value of the graphite electrode when the defect is serious; mu.sbThe state mean value of the lossless graphite electrode can be calibrated by experts; m is the number of lossless graphite electrode states, which can be calibrated by experts; q is a graphite electrode defect index, and q is an integer greater than 0; x is the number ofcThe data vector of the graphite electrode to be detected is obtained; INT is a floor function.
In this embodiment, the cloud server is further adapted to obtain a graphite electrode defect index according to the graphite electrode defect detection model, and determine whether the graphite electrode is defective according to the graphite electrode defect index, that is, a smaller graphite electrode defect index q indicates a smaller graphite electrode defect, a larger graphite electrode defect index q indicates a larger graphite electrode defect, and the graphite electrode defect degree can be accurately known through the graphite electrode defect index, so that a worker can determine whether the graphite electrode is qualified.
Example 2
In addition to embodiment 1, in embodiment 2, the graphite electrode defect detection system further includes: the human-computer interaction module and the alarm module are connected with the first processor module; the human-computer interaction module can be but is not limited to a touch screen; the alarm module can be but is not limited to a buzzer; the human-computer interaction module can display the graphite electrode defect index detected by the second processor module, or the cloud server can display the graphite electrode defect index detected by the cloud server; and when the graphite electrode defect index is larger than a preset value, the alarm module gives an alarm.
In this embodiment, the graphite electrode defect detecting system further includes: a storage module connected to the second processor module; the storage module can be but is not limited to an SD card; the storage module is suitable for storing the graphite electrode defect index detected by the second processor module on line.
In summary, the cloud server, the first processor module, the second processor module, the knocking mechanism and the sound detection module which are connected with the second processor module, and the transmission module which is electrically connected with the first processor module are used; the second processor module is suitable for controlling the knocking mechanism to knock the graphite electrode; the sound detection module is suitable for detecting sound generated by knocking a graphite electrode by a knocking mechanism and sending a sound signal to the second processor module so as to be forwarded to the first processor module by the second processor module; the first processor module is adapted to send a sound signal to the cloud server through the transmission module; the cloud server is suitable for judging the defect degree of the graphite electrode according to the sound signal, so that the defect degree of the graphite electrode is automatically detected, the defects of strong subjectivity and high omission factor caused by manual detection and other methods are avoided, and meanwhile, the labor cost is reduced.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (9)
1. A graphite electrode defect detection system, comprising:
the system comprises a cloud server, a first processor module, a second processor module, a knocking mechanism and a sound detection module which are connected with the second processor module, and a transmission module which is electrically connected with the first processor module;
the second processor module is suitable for controlling the knocking mechanism to knock the graphite electrode;
the sound detection module is suitable for detecting sound generated by knocking a graphite electrode by a knocking mechanism and sending sound signal data to the second processor module so as to be forwarded to the first processor module by the second processor module;
the first processor module is adapted to send sound signal data to the cloud server through the transmission module;
the cloud server is suitable for judging the defect degree of the graphite electrode according to the sound signal data.
2. The graphite electrode defect detection system of claim 1,
the striking mechanism includes: the pulse control circuit, the electromagnetic valve, the air pump and the air hammer;
the second processor module is suitable for the pulse control circuit to output pulse signals to control the electromagnetic valve to be conducted, so that gas provided by the gas pump enters the air hammer through the high-pressure gas pipe, and the graphite electrode is knocked by the air hammer.
3. The graphite electrode defect detection system of claim 2,
the sound detection module includes: a sound sensor, a conditioning circuit and an AD converter;
the sound sensor is suitable for detecting the sound generated by knocking the graphite electrode by the knocking mechanism;
the sound sensor is suitable for converting a sound signal into an electric signal and sending the electric signal to the conditioning circuit;
the conditioning circuit is suitable for amplifying and filtering an electric signal and sending the electric signal to the AD converter; the AD converter is adapted to perform analog-to-digital conversion on the amplified and filtered signal and send to a second processor module for forwarding to the first processor module via the second processor module.
4. The graphite electrode defect detection system of claim 3,
the cloud server is adapted to determine a degree of graphite electrode defect from the acoustic signal data, i.e.
The cloud server is suitable for acquiring the power spectral density of each frequency band according to the sound signal data through FFT (fast Fourier transform);
the frequency band includes: 500-1500Hz, 1500-2500Hz and 2500-3500 Hz.
5. The graphite electrode defect detection system of claim 4,
the cloud server is adapted to calculate the power ratio of the relevant frequency bands from the power spectral density, i.e.
Wherein, S (f)i) Is a frequency of fiA value of the time power spectral density; s (f)i+1) Is a frequency of fi+1A value of the time power spectral density; n isjThe number of j frequency bands is divided equally according to frequency intervals; f. ofjhThe highest frequency of the jth frequency band; f. ofjlIs the lowest frequency of the jth frequency band; Δ f is fiTo fi+1The frequency interval of (a); ejIs the power of the jth frequency band; x is the number of(j)The j is the ratio of the power of the jth frequency band to the sum of the powers of all frequency bands, i.e., the power ratio of the jth frequency band, and is 1,2, and 3.
6. The graphite electrode defect detection system of claim 5,
the cloud server is adapted to establish respective vectors, i.e. according to the power ratios of the relevant frequency bands
The cloud server establishes a data vector and a weight coefficient vector;
the data vector is: x ═ x(1),x(2),x(3))；
Wherein x is(1)Power ratio of 500-1500Hz frequency band; x is the number of(2)Power ratio in the 1500-; x is the number of(3)Power ratio in 2500-;
the weight valueThe coefficient vector is: w ═ w (w)(1),w(2),w(3))；
Wherein, w(1)Power ratio coefficient of 500-1500Hz frequency band; w is a(2)Power ratio coefficient of 1500-; w is a(3)The power ratio coefficient is 2500-.
7. The graphite electrode defect detection system of claim 6,
the cloud server is adapted to construct an optimization model from the corresponding vectors and to obtain a solution to the optimization model, i.e. of
The cloud server is adapted to build an optimization model:
s.t. yi(wgxi+b)≥1-ξi；
ξi≥0i＝1,2,......,N；
wherein C is a penalty coefficient; x is the number ofiIs the ith training data vector; y isiIs xiWhen y is a class markiWhen the number is-1, it means that the graphite electrode is seriously defective, and when y isi1 indicates that the graphite electrode is not damaged, N is the number of training data, ξ is the relaxation variable, ξiRelaxation variables for the ith training data; b is an offset;
the solution of the optimization model is then: w is a*And b*；
Wherein, w*Is the solution of the coefficient vector; b*Is a solution to the bias;
the ith element which is a solution to the dual problem in the Lagrangian multiplier vector;
for all graphite electrodes to be severely defective yiData x of-1iCalculating w*xiTake all of w*xiMaximum value of (d);
to have no damage to all graphite electrodesiData x of 1iCalculating w*xiTake all of w*xiIs measured.
8. The graphite electrode defect detection system of claim 7,
the cloud server is suitable for constructing a graphite electrode defect detection model according to the solution of the optimization model and judging the defect degree of the graphite electrode according to the graphite electrode defect detection model, namely
The cloud server is suitable for constructing a graphite electrode defect detection model:
wherein, mugThe state mean value of the graphite electrode when the defect is serious; mu.sbThe state mean value of the lossless graphite electrode is obtained; m is the number of lossless graphite electrode states; q is a graphite electrode defect index, and q is an integer greater than 0; x is the number ofcIs the data vector of the graphite electrode currently detected.
9. The graphite electrode defect detection system of claim 8,
the cloud server is also suitable for acquiring a graphite electrode defect index according to the graphite electrode defect detection model and judging whether the graphite electrode is defective or not according to the graphite electrode defect index, namely
The smaller the graphite electrode defect index q is, the smaller the graphite electrode defect is, and the larger the graphite electrode defect index q is, the larger the graphite electrode defect is.
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Cited By (4)
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| CN111965256A (en) * | 2020-08-21 | 2020-11-20 | 合肥炭素有限责任公司 | Graphite electrode defect detection device and method |
| CN112032568A (en) * | 2020-08-14 | 2020-12-04 | 常州机电职业技术学院 | Buried gas pipeline leakage risk prediction algorithm and prediction method |
| CN112032567A (en) * | 2020-08-14 | 2020-12-04 | 常州机电职业技术学院 | Buried gas pipeline leakage risk prediction system |
| CN112067043A (en) * | 2020-08-14 | 2020-12-11 | 常州机电职业技术学院 | Defective degree detecting system of timber structure ancient building |
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| CN106908309A (en) * | 2017-01-25 | 2017-06-30 | 吉林炭素有限公司 | A kind of lossless detection method of graphite electrode and its joint rupture strength |
| CN108982671A (en) * | 2018-09-06 | 2018-12-11 | 大同宇林德石墨设备股份有限公司 | Physical data detection line and detection method after a kind of processing of graphite electrode |
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| CN106908309A (en) * | 2017-01-25 | 2017-06-30 | 吉林炭素有限公司 | A kind of lossless detection method of graphite electrode and its joint rupture strength |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112032568A (en) * | 2020-08-14 | 2020-12-04 | 常州机电职业技术学院 | Buried gas pipeline leakage risk prediction algorithm and prediction method |
| CN112032567A (en) * | 2020-08-14 | 2020-12-04 | 常州机电职业技术学院 | Buried gas pipeline leakage risk prediction system |
| CN112067043A (en) * | 2020-08-14 | 2020-12-11 | 常州机电职业技术学院 | Defective degree detecting system of timber structure ancient building |
| CN111965256A (en) * | 2020-08-21 | 2020-11-20 | 合肥炭素有限责任公司 | Graphite electrode defect detection device and method |
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