CN110925022A - Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method - Google Patents
Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method Download PDFInfo
- Publication number
- CN110925022A CN110925022A CN201911278447.4A CN201911278447A CN110925022A CN 110925022 A CN110925022 A CN 110925022A CN 201911278447 A CN201911278447 A CN 201911278447A CN 110925022 A CN110925022 A CN 110925022A
- Authority
- CN
- China
- Prior art keywords
- early warning
- acoustic emission
- trend
- state
- judging
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 title claims abstract description 21
- 239000003245 coal Substances 0.000 description 9
- 239000011435 rock Substances 0.000 description 7
- 238000009826 distribution Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000035945 sensitivity Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000005422 blasting Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F7/00—Methods or devices for drawing- off gases with or without subsequent use of the gas for any purpose
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses an acoustic emission monitoring and early warning method, wherein an acoustic emission signal generator is arranged at a monitoring position, and real and effective acoustic emission characteristic parameters are extracted; according to the operation shift of mine operation; the acoustic emission characteristic parameters of a plurality of normal shift identification periods are used as samples, the average value of the samples is calculated to be N, and N is used as an early warning threshold value of a normal state; selecting a to-be-detected identification period as X, extracting an acoustic emission characteristic parameter of the X as M, and if M is larger than N, judging that the current state identification result is the state early warning needing to be started; equally dividing the X into four time periods, extracting acoustic emission characteristic parameters a, b, c and d of the four time periods, judging the trend change condition of the four time periods according to a formula, and determining whether a trend early warning signal needs to be started according to a trend judgment result. The early warning accuracy is improved by judging the trend data and the state data.
Description
Technical Field
The invention relates to the field of mine production, in particular to a trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method.
Background
The loss and influence of coal rock gas dynamic disasters on coal mine enterprises are huge, and especially disasters such as coal and gas outburst and rock burst are the most typical, so that the safety production of mines is seriously threatened. At present, coal rock gas dynamic disaster prediction is mainly realized by arranging drill holes and measuring related parameters, but the rationality of drill hole distribution positions, the construction quality of the drill holes and the sensitivity of outburst prediction indexes under different coal seam gas occurrence conditions have large influence on prediction results, belong to a non-continuous point prediction method, and cannot realize real-time prediction and early warning. The acoustic emission prediction technology is a non-contact prediction technology and has the characteristics of real-time, dynamic and continuous prediction and the like, but the acoustic emission monitoring data volume is large, how to rapidly and accurately carry out statistical analysis on the data in a large amount of data and extract available effective information so as to realize an online comprehensive early warning method for coal rock gas dynamic disasters is very important, and meanwhile, the data are rapidly and accurately used, and the effective information extraction is also a necessary condition for intelligent mine and digital mine construction.
Therefore, a novel trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method is needed to overcome the above defects and realize early warning of gas dynamic disasters.
Disclosure of Invention
In view of the above, the invention aims to provide a comprehensive acoustic emission monitoring and early warning method for coal rock gas dynamic disasters of a production mine, which can realize the function of predicting and early warning the coal rock gas dynamic disasters during working on a working face.
1. A gas dynamic disaster acoustic emission monitoring and early warning method based on trend and state comprises the following steps;
a: installing an acoustic emission signal generator at a monitoring position, continuously collecting acoustic emission signals emitted by the acoustic emission signal generator, and extracting real and effective acoustic emission characteristic parameters;
b: defining an identification period of comprehensive early warning as 8 hours according to the operation shift of mine operation;
c: the acoustic emission characteristic parameters of a plurality of normal shift identification periods are used as samples, the average value of the samples is calculated to be N, and N is used as an early warning threshold value of a normal state;
d: selecting a to-be-detected judging and identifying period as X, extracting an acoustic emission characteristic parameter of the X as M, taking N in the step C as a comparison parameter, and if M is larger than N, judging that the current state judging and identifying result is the state early warning needing to be started;
e: equally dividing X in the step D into four time periods, extracting acoustic emission characteristic parameters a, b, c and D of the four time periods, judging the trend change condition of the four time periods according to the following formula, confirming whether a trend early warning signal needs to be started or not in a trend identification result,
in the above expression, the Num · GTZero (m, n, k) function represents the number greater than 0 of the values m, n, k, and max () is a function for finding the maximum value;
f: and D, according to the state identification result in the step D and the trend identification result in the step E, if the state identification result and the trend identification result simultaneously need to start the early warning signal, the comprehensive early warning identification period finally outputs a start early warning signal, otherwise, the start early warning signal is not output.
Further, the average value N in step C is calculated as follows:
in the formula, NiAnd j represents the number of the normal condition operation shifts in the historical data, and j is required to be more than or equal to 20.
Further, the effective acoustic emission characteristic parameters extracted in the step A have removed various interference factors.
The invention has the beneficial effects that:
according to the gas dynamic disaster acoustic emission monitoring and early warning method based on the trend and the state, the intelligent early warning function under the condition of big data is realized, the data analysis workload is simplified, and the disaster judgment accuracy and judgment efficiency are improved.
Drawings
The invention is further described below with reference to the following figures and examples:
fig. 1 is a flow chart of the comprehensive early warning of the present invention.
Detailed Description
FIG. 1 is a flow chart of the comprehensive early warning of the present invention; as shown in the figure; a gas dynamic disaster acoustic emission monitoring and early warning method based on trend and state comprises the following steps;
a: installing an acoustic emission signal generator at a monitoring position, continuously collecting acoustic emission signals emitted by the acoustic emission signal generator, and extracting real and effective acoustic emission characteristic parameters; effective and real acoustic emission characteristic parameter index data are obtained by comprehensively analyzing and extracting factors such as mine operation procedures and operation conditions, signal frequency distribution, wavelength and the like, wherein the main frequency of the acoustic emission real signal of the coal rock mass is 50-500 Hz, the waveform is symmetrical, the main frequency of the acoustic emission signal generated by artificial knocking is not more than 50Hz, and the waveform is asymmetrical. The acoustic emission characteristic parameters have a plurality of indexes, the characteristic parameter indexes selected by the acoustic emission monitoring and early warning comprehensive method combining the trend and the state are determined according to the sensitivity of the characteristic parameter indexes to the operation condition, and the indexes with high sensitivity are selected as the characteristic parameters of the acoustic emission comprehensive early warning.
B: defining an identification period of comprehensive early warning as 8 hours according to the operation shift of mine operation; the early warning identification period is 8h mainly according to the time of the actual shift of the mine (8: 00-16: 00 in the early shift, 16: 00-24: 00 in the middle shift and 0:00-8:00 in the late shift), and corresponding to the time, for example, the early warning information is given according to the acoustic emission signal of the early shift before the middle shift starts to guide the middle shift operation, which is beneficial to the timely release and transmission of the early warning information.
C: the acoustic emission characteristic parameters of a plurality of normal shift identification periods are used as samples, the average value of the samples is calculated to be N, and N is used as an early warning threshold value of a normal state;
d: selecting a to-be-detected judging and identifying period as X, extracting an acoustic emission characteristic parameter of the X as M, taking N in the step C as a comparison parameter, and if M is larger than N, judging that the current state judging and identifying result is the state early warning needing to be started;
e: equally dividing X in the step D into four time periods, extracting acoustic emission characteristic parameters of the four time periods as a, b, c and D respectively, judging the trend change condition of the four time periods according to the following formula, confirming whether a trend judgment result needs to start a trend early warning signal, counting the number of acoustic emission effective characteristic parameters in a period to be judged in terms of 2 hours as a time period, obtaining characteristic parameter statistical values as a, b, c and D corresponding to 4 '2 hours' in the period to be judged, and determining whether to output the early warning signal according to the numerical trend change condition of the a, b, c and D
In the above expression, the Num · GTZero (m, n, k) function represents the number greater than 0 of the values m, n, k, and max () is a function for finding the maximum value;
f: and D, according to the state identification result in the step D and the trend identification result in the step E, if the state identification result and the trend identification result simultaneously need to start the early warning signal, the comprehensive early warning identification period finally outputs a start early warning signal, otherwise, the start early warning signal is not output. And only when the early warning is required to be started for both the trend output and the state output, the early warning is finally started, and the comprehensive judgment is carried out through two conditions, so that the accuracy of the result is improved.
In this embodiment, the average value N in step C is calculated as follows:
in the formula, NiAnd j represents the number of the normal condition operation shifts in the historical data, and j is required to be more than or equal to 20.
In this embodiment, the effective acoustic emission characteristic parameters extracted in step a have removed various interference factors. Extracting effective characteristic parameters mainly by combining the working procedures of a mine, removing acoustic emission characteristic parameters generated by manual operation, such as signals generated by the working procedures of drilling, blasting, water injection and the like, by comparing the working time with the signal receiving time corresponding to the working condition of an acoustic emission monitoring place; and simultaneously, other interference signals are filtered according to factors such as frequency distribution, wavelength and the like of the acoustic emission signals.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (3)
1. A gas dynamic disaster acoustic emission monitoring and early warning method based on trend and state is characterized by comprising the following steps;
a: installing an acoustic emission signal generator at a monitoring position, continuously collecting acoustic emission signals emitted by the acoustic emission signal generator, and extracting real and effective acoustic emission characteristic parameters;
b: defining an identification period of comprehensive early warning as 8 hours according to the operation shift of mine operation;
c: the acoustic emission characteristic parameters of a plurality of normal shift identification periods are used as samples, the average value of the samples is calculated to be N, and N is used as an early warning threshold value of a normal state;
d: selecting a to-be-detected judging and identifying period as X, extracting an acoustic emission characteristic parameter of the X as M, taking N in the step C as a comparison parameter, and if M is larger than N, judging that the current state judging and identifying result is the state early warning needing to be started;
e: equally dividing X in the step D into four time periods, extracting acoustic emission characteristic parameters a, b, c and D of the four time periods, judging the trend change condition of the four time periods according to the following formula, confirming whether a trend early warning signal needs to be started or not in a trend identification result,
in the above expression, the Num · GTZero (m, n, k) function represents the number greater than 0 of the values m, n, k, and max () is a function for finding the maximum value;
f: and D, according to the state identification result in the step D and the trend identification result in the step E, if the state identification result and the trend identification result simultaneously need to start the early warning signal, the comprehensive early warning identification period finally outputs a start early warning signal, otherwise, the start early warning signal is not output.
2. The trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method according to claim 1, wherein the trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method comprises the following steps: the average value N in the step C is calculated by the following method:
in the formula, NiAnd j represents the number of the normal condition operation shifts in the historical data, and j is required to be more than or equal to 20.
3. The trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method according to claim 1, wherein the trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method comprises the following steps: the effective acoustic emission characteristic parameters extracted in the step A have removed various interference factors.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911278447.4A CN110925022B (en) | 2019-12-12 | 2019-12-12 | Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911278447.4A CN110925022B (en) | 2019-12-12 | 2019-12-12 | Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110925022A true CN110925022A (en) | 2020-03-27 |
| CN110925022B CN110925022B (en) | 2021-07-20 |
Family
ID=69860372
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911278447.4A Active CN110925022B (en) | 2019-12-12 | 2019-12-12 | Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110925022B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111965696A (en) * | 2020-08-17 | 2020-11-20 | 中煤科工集团重庆研究院有限公司 | Dynamic disaster prediction method based on elastic wave multi-target analysis |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201078243Y (en) * | 2007-09-29 | 2008-06-25 | 煤炭科学研究总院重庆分院 | Mine dynamic force disaster monitoring system based on acoustic emission |
| CN104454009A (en) * | 2014-12-03 | 2015-03-25 | 中国中煤能源集团有限公司 | Gas forecasting and early-warning system and method based on sensing network |
| CN104500139A (en) * | 2014-11-13 | 2015-04-08 | 四川大学 | Mine disaster prevention and control system based on acoustic emission technique and implementation method thereof |
| CN104500141A (en) * | 2014-12-19 | 2015-04-08 | 中煤科工集团重庆研究院有限公司 | Outburst warning method based on monitoring dynamic characteristics of mine pressure |
| CN106295214A (en) * | 2016-08-18 | 2017-01-04 | 西安科技大学 | A kind of Mine Methane method for early warning |
| CN108876010A (en) * | 2018-05-23 | 2018-11-23 | 中国矿业大学 | The selection of underground coal mine electromagnetic radiation intensity time series data and trend forecasting method |
-
2019
- 2019-12-12 CN CN201911278447.4A patent/CN110925022B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201078243Y (en) * | 2007-09-29 | 2008-06-25 | 煤炭科学研究总院重庆分院 | Mine dynamic force disaster monitoring system based on acoustic emission |
| CN104500139A (en) * | 2014-11-13 | 2015-04-08 | 四川大学 | Mine disaster prevention and control system based on acoustic emission technique and implementation method thereof |
| CN104454009A (en) * | 2014-12-03 | 2015-03-25 | 中国中煤能源集团有限公司 | Gas forecasting and early-warning system and method based on sensing network |
| CN104500141A (en) * | 2014-12-19 | 2015-04-08 | 中煤科工集团重庆研究院有限公司 | Outburst warning method based on monitoring dynamic characteristics of mine pressure |
| CN106295214A (en) * | 2016-08-18 | 2017-01-04 | 西安科技大学 | A kind of Mine Methane method for early warning |
| CN108876010A (en) * | 2018-05-23 | 2018-11-23 | 中国矿业大学 | The selection of underground coal mine electromagnetic radiation intensity time series data and trend forecasting method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111965696A (en) * | 2020-08-17 | 2020-11-20 | 中煤科工集团重庆研究院有限公司 | Dynamic disaster prediction method based on elastic wave multi-target analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110925022B (en) | 2021-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102926810B (en) | Forecasting method of coal and gas outburst | |
| CN109711632B (en) | Coal and gas outburst prediction method based on gas emission abnormal sensitive index | |
| CN111197500B (en) | Method and system suitable for real-time judgment of surrounding rock grade of TBM tunneling tunnel | |
| CN114417697A (en) | Neural network-based TBM hob abrasion real-time prediction method and system | |
| CN102789676B (en) | Method for designing industrial alarm on basis of alarm evidence fusion | |
| CN110925022B (en) | Trend and state-based gas dynamic disaster acoustic emission monitoring and early warning method | |
| CN111476420B (en) | Method for optimizing early warning index of microseismic monitoring rock burst | |
| CN115358494B (en) | Danger early warning method for subway shield underpass construction | |
| CN115628930B (en) | Method for predicting underground cutting working condition of heading machine based on RBF neural network | |
| CN104500141B (en) | Prominent method for early warning based on ore pressure monitor behavioral characteristics | |
| CN106933097B (en) | Chemical process fault diagnosis method based on multi-layer optimization PCC-SDG | |
| CN116658246A (en) | Coal mine water damage monitoring and early warning system and method | |
| CN111832604A (en) | Acoustic emission signal noise filtering and danger comprehensive judgment method based on multi-system fusion | |
| CN117668730A (en) | Load abnormality detection method for underground coal mine scraper based on deep learning algorithm | |
| CN116822352A (en) | GRNN neural network-based underground waterproof sealing wall working condition prediction method and system | |
| CN111241474A (en) | Landslide disaster comprehensive early warning analysis method | |
| CN115492639A (en) | Coal roadway driving working face coal and gas outburst early warning method | |
| CN117471308A (en) | Fan fault diagnosis method based on big data multi-feature technology mining | |
| CN114169656A (en) | Drilling stuck risk early warning method and system based on adjacent well historical data | |
| CN117271980B (en) | Drilling data processing method and device, electronic equipment and storage medium | |
| CN112443319B (en) | Kick monitoring method | |
| LU102212B1 (en) | Method for removing P-wave first arrival time of interference signals in rockburst signals | |
| EA022596B1 (en) | Method of monitoring and determination of dangerous state of rocks during underground mining | |
| CN111764891B (en) | Drilling impact danger degree judging method and vibration collecting device adopted by same | |
| Yang et al. | Design of an Intelligent Monitoring System for Operating States in a Coal Mine Drilling Process |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |