CN115235676A - Automatic alarm method for pressure abnormity of mine hydraulic support - Google Patents
Automatic alarm method for pressure abnormity of mine hydraulic support Download PDFInfo
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Abstract
The invention relates to the technical field of data identification processing, in particular to an automatic alarm method for pressure abnormity of a mine hydraulic support. The method comprises the following steps: collecting a pressure value of each supporting point on the mine hydraulic support to obtain a quality evaluation index of the hydraulic support; acquiring a quality evaluation index sequence of each hydraulic support within a period of time, and acquiring tracking indexes according to the quality evaluation index sequence to obtain the hydraulic support which is preliminarily judged to be in a normal state; acquiring a horizontal inclination angle of each hydraulic support in a normal state in multiple time periods, fitting all the horizontal inclination angles to obtain a Gaussian model so as to obtain probability distribution of each hydraulic support, and obtaining abnormal pressure evaluation according to the probability distribution and quality evaluation indexes; acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormity sequence, and inputting the abnormity sequence into a neural network to predict to obtain an abnormity alarm time; the detection result is ensured to be more accurate and timely.
Description
Technical Field
The invention relates to the technical field of data identification processing, in particular to an automatic alarm method for pressure abnormity of a mine hydraulic support.
Background
The hydraulic support is widely applied to supporting the upper stratum pressure in mine operation, and mining face mine pressure acts on the hydraulic support in an external load mode; the problem of abnormal pressure usually occurs when the hydraulic support for the mine is used along with the lapse of the use time, so that the maintenance of the support is very important when the hydraulic support is used.
At present, the hydraulic support is usually maintained by early warning when the hydraulic support is in a problem during working and then is overhauled and maintained by workers.
Disclosure of Invention
In order to solve the technical problem, the invention aims to provide an automatic alarm method for pressure abnormity of a mine hydraulic support, which comprises the following steps:
collecting the pressure value of each supporting point on the mine hydraulic support in a plurality of time periods; obtaining a quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period;
acquiring a quality evaluation index sequence of each hydraulic support within a period of time, performing phase space reconstruction on the quality evaluation index sequence to acquire a tracking index, and primarily judging the hydraulic support to be in a normal state when the tracking index is smaller than a preset index;
acquiring a horizontal inclination angle of the hydraulic support in each normal state in multiple time periods, fitting the horizontal inclination angles of all the hydraulic supports in each time period to obtain a Gaussian model, obtaining probability distribution of the hydraulic support based on the Gaussian model, and obtaining abnormal pressure evaluation according to the probability distribution and the quality evaluation index;
acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormal sequence, inputting the abnormal sequence into a trained neural network to obtain a predicted abnormal sequence, and obtaining an abnormal alarm time based on the predicted abnormal sequence.
Preferably, the method for obtaining the quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period comprises the following steps:
acquiring a pressure sequence corresponding to the pressure value of the supporting point in the current time period and a pressure sequence corresponding to the pressure value in the adjacent previous time period;
calculating a correlation coefficient between the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period; splicing the pressure sequence in the current time period with the pressure sequence in the adjacent previous time period to obtain a spliced sequence, calculating the standard deviation of the spliced sequence, and obtaining the working state corresponding to the supporting point based on the correlation coefficient and the standard deviation;
and the minimum value of the working state corresponding to all the supporting points in the hydraulic support is the quality evaluation index of the hydraulic support.
Preferably, the method for obtaining the tracking index by performing phase space reconstruction on the quality evaluation index sequence includes:
performing phase space reconstruction on the quality evaluation index sequence, and predicting a phase point value of a next time of different phase spaces based on the phase space reconstruction;
calculating the difference between the predicted phase point value and the true value of the phase point in the quality evaluation index sequence and recording the difference as a phase difference; the phase points are elements in the quality evaluation index sequence; and calculating a standard deviation according to the phase differences corresponding to all the phase points in the quality evaluation index sequence to obtain a tracking index corresponding to the quality evaluation index sequence.
Preferably, the method for obtaining the probability distribution of the hydraulic support based on the gaussian model comprises:
substituting each horizontal inclination angle into the Gaussian model, obtaining probability values corresponding to all the horizontal inclination angles acquired every second by looking up a table, and counting the number of the probability values corresponding to all the horizontal inclination angles in a convergence domain; and calculating the ratio of the number in the convergence domain to the total number of all horizontal tilt angles to be a probability distribution, wherein the probability distribution is the probability distribution of each hydraulic support.
Preferably, the method for obtaining the pressure anomaly evaluation according to the probability distribution and the quality evaluation index includes:
and calculating the product of the probability distribution and the quality evaluation index, wherein the product is the pressure anomaly evaluation.
Preferably, the method for calculating the correlation coefficient between the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period is a pearson correlation coefficient method.
Preferably, the neural network is a time convolution network.
The invention has the following beneficial effects: analyzing the working state corresponding to each supporting point on the hydraulic support by the pressure value corresponding to each supporting point, further reflecting the quality evaluation index of the hydraulic support according to the minimum value of the working state in all the supporting points, and carrying out phase space reconstruction by combining the quality evaluation indexes of the hydraulic support in multiple days so as to increase the accuracy of analysis, thereby judging whether the quality problem occurs in the hydraulic support by the obtained tracking index and carrying out maintenance in time; when the hydraulic supports are not found to be abnormal based on the tracking indexes, acquiring probability distribution corresponding to each hydraulic support by combining horizontal inclined included angles corresponding to all the hydraulic supports, wherein the probability distribution is used for reflecting whether the hydraulic supports incline or not; acquiring pressure abnormity evaluation corresponding to the hydraulic support according to the product of the probability distribution and the quality evaluation index; the abnormal sequence is obtained according to the pressure abnormal evaluation in the historical time period, the time needing alarming is obtained by predicting according to the abnormal sequence, the detection accuracy is high, the abnormal condition is found more timely, and the occurrence of accidents is avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only 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 flowchart of an automatic alarm method for pressure abnormality of a mine hydraulic support according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following detailed description, with reference to the accompanying drawings and preferred embodiments, describes a method for automatically alarming pressure abnormality of a hydraulic support for a mine according to the present invention, and the specific implementation manner, structure, features and effects thereof. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
This application is applicable to the analysis to hydraulic support behavior, through the corresponding pressure value of the last bracing point of every hydraulic support and this hydraulic support horizontal inclination combine the analysis, judge the unusual evaluation of pressure that hydraulic support corresponds every day, the unusual evaluation of pressure that corresponds based on this hydraulic support in the historical period predicts, the unusual moment of work of hydraulic support is located and in time reminds the staff to pay attention to the maintenance through the result mark of prediction, the unusual promptness of discovery hydraulic support has been improved, the emergence of accident has been avoided.
The following specifically describes a specific scheme of the automatic alarm method for the pressure abnormality of the mine hydraulic support provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of an automatic alarm method for mine hydraulic support pressure abnormality according to an embodiment of the present invention is shown, the method includes the following steps:
s100, collecting a pressure value of each supporting point on the mine hydraulic support in multiple time periods; and obtaining a quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the adjacent previous time period.
The hydraulic support of the mine is commonly supported through a plurality of hydraulic supports, each supporting point is used for supporting surrounding rocks at the top, and the hydraulic support is subjected to load sharing by a plurality of hydraulic supports, so that whether the supporting state of the hydraulic support is good or not can be preliminarily judged according to the pressure condition at each supporting point.
Specifically, a pressure sensor is installed at each supporting point in the hydraulic support and used for acquiring a pressure value at the supporting point, in order to improve the accuracy of analysis, in the embodiment of the invention, a time period of one day is set, the pressure value corresponding to each supporting point in the hydraulic support is acquired every day, and the acquisition frequency is 1 second; from this, pressure values per 1 second per 1 day per support point can be obtained.
Considering that the rock burst caused by stratum change can cause abnormal data that the pressure of the supporting point on the hydraulic support is too large, the data with the too large pressure often appears instantly and has no universality, therefore, the high-pass filtering processing is carried out on the pressure value of the supporting point obtained in each day, namely the pressure value of the supporting point obtained in each day forms a complete pressure sequence, and the pressure sequence processed by the high-pass filter is obtained by screening out the larger value in the complete pressure sequence based on the high-pass filter, so that the subsequent analysis based on the pressure sequence is more accurate.
Further, acquiring the pressure change of each supporting point within two days, namely reflecting the working state of the supporting point according to the difference between the pressure sequence of the supporting point in the current day and the pressure sequence of the supporting point in the previous day; considering that the pressure sequences respectively corresponding to the two days are sequences processed by a high-pass filter, the lengths of the pressure sequences are possibly inconsistent; therefore, in the embodiment of the invention, the pressure sequence with fewer elements is subjected to element supplement, namely, the element supplement is carried out at the position of the point screened by the high-pass filter, the element value is the average value of the element values at two adjacent sides of the point, and by analogy, the pressure sequence with fewer elements is subjected to element supplement and then is consistent with the pressure sequence with more elements in length.
Obtaining a correlation coefficient between the pressure sequence of the current day supporting point and the pressure sequence of the previous day supporting point, wherein the method for calculating the correlation coefficient adopts a Pearson correlation coefficient; because the difference of the pressure value born by each supporting point under the normal environment is very small, the pressure sequence of the supporting point on the same day is spliced with the pressure sequence of the supporting point on the previous day to obtain a spliced sequence, the standard deviation corresponding to the spliced sequence is calculated, the working state corresponding to each supporting point is obtained according to the correlation coefficient and the standard deviation corresponding to each supporting point, and the calculation method of the working state comprises the following steps:
wherein the content of the first and second substances,
representing the working state of the supporting point;
denotes the first
The pressure sequence corresponding to the daily supporting point is the pressure sequence corresponding to the daily supporting point;
is shown as
The pressure sequence corresponding to the day supporting point, namely the pressure sequence corresponding to the supporting point of the previous day;
representing the Pearson correlation coefficient calculation;
represents the standard deviation calculation;
represents a natural constant;
representing a splice of the pressure sequence;
representing the correlation coefficient.
By analogy, the working state corresponding to each supporting point on the hydraulic support is obtained, and the minimum value of the working states in all the supporting points is used as the quality evaluation index of the hydraulic support at the moment; the larger the quality evaluation index is, the better the working quality of the hydraulic support is, and the internal quality problem of the hydraulic support does not occur.
And S200, acquiring a quality evaluation index sequence of each hydraulic support within a period of time, performing phase space reconstruction on the quality evaluation index sequence to acquire a tracking index, and primarily judging the hydraulic supports to be in a normal state when the tracking index is smaller than a preset index.
In step S100, the quality evaluation index corresponding to each hydraulic support on the current day is obtained, and in order to identify the abnormality of the hydraulic support more accurately, the quality evaluation index of each hydraulic support for a plurality of consecutive days is obtained, so that a corresponding quality evaluation index sequence is constructed according to the quality evaluation indexes on each day, and then phase space reconstruction is performed on the quality evaluation index sequence; the embedding dimension of the phase space reconstruction is selected by an implementer, and the delay time is calculated according to an autocorrelation function method, wherein the specific method is the prior art and is not described any more.
Predicting a phase point value of a different phase space at the next moment based on the phase space reconstruction, and calculating the difference between the predicted phase point value and the true value of the phase point in the quality evaluation index sequence to be recorded as a phase difference; obtaining the phase difference between the predicted phase point value and the true value of each phase point in the quality evaluation index sequence; calculating a standard deviation according to phase differences corresponding to all phase points in the quality evaluation index sequence to obtain a tracking index corresponding to the quality evaluation index sequence, and when the tracking index is smaller than a preset index, indicating that no actual quality problem occurs in the hydraulic support at the moment, primarily judging the hydraulic support to be in a normal state and enabling the hydraulic support to be normally used; when the index is larger than the preset index, the quality problem of the hydraulic support at the moment is shown, and the hydraulic support is required to be immediately repaired and maintained.
It should be noted that, in the embodiment of the present invention, the phase point in the quality evaluation index sequence is an element in the quality evaluation index sequence.
Step S300, acquiring the horizontal inclination angle of the hydraulic support in each normal state in a plurality of time periods, fitting the horizontal inclination angles of all the hydraulic supports in each time period to obtain a Gaussian model, acquiring the probability distribution of the hydraulic support based on the Gaussian model, and acquiring abnormal pressure evaluation according to the probability distribution and the quality evaluation index.
Specifically, the quality of the hydraulic support is preliminarily evaluated based on the pressure value of each supporting point in the steps S100 and S200; however, considering the complexity of the upper strata, when the upper strata generates rock burst, the hydraulic support with lower pressure is unevenly settled, so that the whole body of the hydraulic support may incline, and the abrasion of the hydraulic support is further increased. Therefore, in the embodiment of the invention, the level meter is arranged at the center of the hydraulic support and used for detecting whether the hydraulic support inclines, one day is taken as a time period, the acquisition frequency is 1 second, and the horizontal inclination angle corresponding to each 1 second of the hydraulic support in one day is acquired.
Acquiring horizontal inclination angles of all hydraulic supports in a day during mine operation; performing Gaussian fitting on the basis of the horizontal inclination angles of all the hydraulic supports acquired in one day to obtain corresponding Gaussian models, wherein the Gaussian fitting method is a conventional known means and is not repeated; acquiring a convergence domain of the Gaussian model, substituting all horizontal inclination angles in the hydraulic support into the Gaussian model, obtaining probability values corresponding to all the horizontal inclination angles acquired per second by looking up a table, and counting the number of the probability values corresponding to all the horizontal inclination angles in the convergence domain; the hydraulic support does not have an inclination phenomenon under normal conditions, so that the obtained convergence domain of the Gaussian model almost comprises all horizontal inclination angle data, when the hydraulic support has the inclination phenomenon, the quantity of the horizontal inclination angle data in the convergence domain of the Gaussian model fitted by the horizontal inclination angle of the hydraulic support has deviation, and the larger the deviation is, the more serious the inclination problem of the hydraulic support is; and recording the ratio of the number of the horizontal inclination angle data in the convergence domain of the fitted Gaussian model to the number of all the horizontal inclination angle data as the probability distribution of the Gaussian model, namely all the hydraulic supports correspond to the probability distribution during mine operation.
Further, calculating a product between the quality evaluation index corresponding to the hydraulic support and the probability distribution of the Gaussian model corresponding to the horizontal inclination angle, and taking the product as the pressure anomaly evaluation corresponding to the hydraulic support in the current day, wherein the smaller the value of the pressure anomaly evaluation is, the more possible problems exist in the hydraulic support at the moment.
And S400, acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormal sequence, inputting the abnormal sequence into a trained neural network to obtain a predicted abnormal sequence, and obtaining an abnormal alarm time based on the predicted abnormal sequence.
In the step S300, the pressure anomaly evaluation of each hydraulic support within one day is obtained, the pressure anomaly evaluation construction sequence of the hydraulic supports for a plurality of consecutive days in the historical time period is obtained to obtain an anomaly sequence, and the anomaly sequence is input into the trained neural network to obtain a predicted anomaly sequence.
The method comprises the steps that a time convolution network TCN which is trained can obtain prediction abnormity evaluation of the hydraulic support every day subsequently, so that a prediction abnormity sequence is formed, when the prediction abnormity evaluation is smaller than a preset threshold value, the problem of the hydraulic support at the moment is shown, the prediction time is marked, early warning is timely sent to relevant workers, and the working condition of the hydraulic support can be better monitored.
In summary, in the embodiment of the present invention, the pressure value of each supporting point on the hydraulic support in each day is collected, and the quality evaluation index corresponding to the hydraulic support is obtained according to the difference between the pressure values corresponding to each supporting point on the current day and the adjacent previous day; acquiring corresponding quality evaluation indexes of the hydraulic support within multiple days to form a quality evaluation index sequence, then performing phase space reconstruction on the quality evaluation index sequence, and primarily judging the working state of the hydraulic support based on a result after the phase space reconstruction; the method further comprises the steps of analyzing based on the horizontal inclination angle on the hydraulic support, fitting through the horizontal inclination angle within one day to obtain a Gaussian model, obtaining probability distribution of the Gaussian model based on fitting, multiplying the probability distribution by a quality evaluation index to obtain pressure anomaly evaluation, inputting a time convolution network according to the pressure anomaly evaluation sequence of the hydraulic support obtained for multiple days to predict, obtaining more accurate alarm time, finding and processing anomalies in time, and avoiding accidents.
It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit of the present invention.
Claims (7)
1. An automatic alarm method for pressure abnormity of a mine hydraulic support is characterized by comprising the following steps:
collecting the pressure value of each supporting point on the mine hydraulic support in a plurality of time periods; obtaining a quality evaluation index of the hydraulic support according to the difference between the pressure values of the support points in the current time period and the pressure value of the support point in the previous time period adjacent to the current time period;
acquiring a quality evaluation index sequence of each hydraulic support within a period of time, performing phase space reconstruction on the quality evaluation index sequence to acquire a tracking index, and primarily judging the hydraulic support to be in a normal state when the tracking index is smaller than a preset index;
acquiring a horizontal inclination angle of the hydraulic support in each normal state in multiple time periods, fitting the horizontal inclination angles of all the hydraulic supports in each time period to obtain a Gaussian model, obtaining probability distribution of the hydraulic support based on the Gaussian model, and obtaining abnormal pressure evaluation according to the probability distribution and the quality evaluation index;
acquiring pressure abnormity evaluation of the hydraulic support in a historical time period to form an abnormal sequence, inputting the abnormal sequence into a trained neural network to obtain a predicted abnormal sequence, and obtaining an abnormal alarm time based on the predicted abnormal sequence.
2. The automatic alarm method for the pressure abnormity of the mine hydraulic support according to claim 1, wherein the method for obtaining the quality evaluation index of the hydraulic support according to the difference between the current time period and the pressure value of the support point in the previous time period adjacent to the current time period comprises the following steps:
acquiring a pressure sequence corresponding to the pressure value of the supporting point in the current time period and a pressure sequence corresponding to the pressure value in the adjacent previous time period;
calculating a correlation coefficient between the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period; splicing the pressure sequence in the current time period with the pressure sequence in the adjacent previous time period to obtain a spliced sequence, calculating the standard deviation of the spliced sequence, and obtaining the working state corresponding to the supporting point based on the correlation coefficient and the standard deviation;
and the minimum value of the working state corresponding to all the supporting points in the hydraulic support is the quality evaluation index of the hydraulic support.
3. The automatic alarm method for the pressure abnormity of the mine hydraulic support according to claim 1, wherein the method for performing phase space reconstruction on the quality evaluation index sequence to obtain the tracking index comprises the following steps:
performing phase space reconstruction on the quality evaluation index sequence, and predicting a phase point value of a next time of different phase spaces based on the phase space reconstruction;
calculating the difference between the predicted phase point value and the true value of the phase point in the quality evaluation index sequence and recording the difference as a phase difference; the phase point is an element in the quality evaluation index sequence; and calculating a standard deviation according to the phase differences corresponding to all the phase points in the quality evaluation index sequence to obtain a tracking index corresponding to the quality evaluation index sequence.
4. The automatic alarm method for the pressure abnormality of the hydraulic support in the mine well according to claim 1, wherein the method for obtaining the probability distribution of the hydraulic support based on the Gaussian model comprises the following steps:
substituting each horizontal inclination angle into the Gaussian model, obtaining probability values corresponding to all the horizontal inclination angles acquired every second by looking up a table, and counting the number of the probability values corresponding to all the horizontal inclination angles in a convergence domain; and calculating the ratio of the number in the convergence domain to the total number of all horizontal inclination angles to be probability distribution, wherein the probability distribution is the probability distribution of each hydraulic support.
5. The automatic alarm method for the pressure abnormity of the mine hydraulic support according to claim 1, wherein the method for obtaining the pressure abnormity evaluation according to the probability distribution and the quality evaluation index comprises the following steps:
and calculating the product of the probability distribution and the quality evaluation index, wherein the product is the pressure anomaly evaluation.
6. The automatic alarm method for the pressure abnormality of the hydraulic support in the mine well as the claim 2, wherein the method for calculating the correlation coefficient between the pressure sequence in the current time period and the pressure sequence in the adjacent previous time period is a Pearson correlation coefficient method.
7. The automatic alarm method for the pressure abnormality of the hydraulic support in the mine as claimed in claim 1, wherein the neural network is a time convolution network.
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Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090056467A1 (en) * | 2007-09-05 | 2009-03-05 | Key Energy Services, Inc. | Method and System for Evaluating Rod Breakout Based on Tong Pressure Data |
| CN104481590A (en) * | 2014-11-18 | 2015-04-01 | 煤炭科学技术研究院有限公司 | Long wall coal mining roof pressure hydraulic support resistance change characteristic pre-warning method |
| CN105607630A (en) * | 2016-03-22 | 2016-05-25 | 广州日滨科技发展有限公司 | Self-checking method and device for checking initial state of electro-hydraulic control system of hydraulic support for coal mines |
| JP2017146278A (en) * | 2016-02-19 | 2017-08-24 | 株式会社日立ハイテクノロジーズ | Automatic analyzer and method of detecting abnormality of transport line |
| WO2017215192A1 (en) * | 2016-06-14 | 2017-12-21 | 中国矿业大学 | Method for evaluating filling characteristics of filling coal-mining hydraulic support |
| US20180135412A1 (en) * | 2015-07-20 | 2018-05-17 | Taiyuan University Of Technology | Method for implementing a centralized control platform of hydraulic support on fully mechanized mining working face in underground coal mines |
| US20180294174A1 (en) * | 2017-04-05 | 2018-10-11 | Ebara Corporation | Semiconductor manufacturing apparatus, failure prediction method for semiconductor manufacturing apparatus, and failure prediction program for semiconductor manufacturing apparatus |
| CN110866658A (en) * | 2019-12-05 | 2020-03-06 | 国网江苏省电力有限公司南通供电分公司 | Method for predicting medium and long term load of urban power grid |
| CN111911214A (en) * | 2020-06-24 | 2020-11-10 | 中煤科工开采研究院有限公司 | Method for monitoring working state of safety valve of hydraulic support |
| CN112145231A (en) * | 2020-08-20 | 2020-12-29 | 中煤科工开采研究院有限公司 | Early warning method for working condition of hydraulic support |
| CN112528229A (en) * | 2020-11-30 | 2021-03-19 | 太原理工大学 | Working surface hydraulic support quality evaluation method based on control chart analysis |
| CN113283375A (en) * | 2021-06-08 | 2021-08-20 | 中国矿业大学 | Early fault real-time early warning method for mine hydraulic support system |
| CN113420465A (en) * | 2021-07-30 | 2021-09-21 | 太原理工大学 | Hydraulic support full-life cycle management method based on digital twin model |
| CN113482677A (en) * | 2021-08-02 | 2021-10-08 | 中煤科工开采研究院有限公司 | Four-column hydraulic support working condition evaluation method based on clustering algorithm |
| CN113569459A (en) * | 2021-06-08 | 2021-10-29 | 中国矿业大学 | Group intelligence-based hydraulic support performance degradation quantitative evaluation method and system |
| CN113672600A (en) * | 2021-07-20 | 2021-11-19 | 微梦创科网络科技（中国）有限公司 | Anomaly detection method and system |
| CN114111918A (en) * | 2020-12-30 | 2022-03-01 | 中国矿业大学 | Intelligent diagnosis method for working state and supporting quality of hydraulic support |
| CN114254457A (en) * | 2021-12-17 | 2022-03-29 | 中国矿业大学(北京) | Mine hydraulic support fatigue life prediction method based on digital twinning |
| CN114298605A (en) * | 2022-01-04 | 2022-04-08 | 北京康宁电通科技发展有限公司 | Hydraulic support full life cycle evaluation method |
| CN114445398A (en) * | 2022-02-16 | 2022-05-06 | 西安华创马科智能控制系统有限公司 | Method and device for monitoring state of side protection plate of hydraulic support of coal mining machine |
| WO2022110557A1 (en) * | 2020-11-25 | 2022-06-02 | 国网湖南省电力有限公司 | Method and device for diagnosing user-transformer relationship anomaly in transformer area |
| CN114611378A (en) * | 2022-01-31 | 2022-06-10 | 扬州来源液压设备有限公司 | Hydraulic support rod pressure regulation and control system based on mine industry Internet of things |
| WO2022121139A1 (en) * | 2020-12-11 | 2022-06-16 | 中国矿业大学 | Coal/gangue recognition method in top coal caving process based on multi-sensor information fusion |
| CN114770891A (en) * | 2022-06-17 | 2022-07-22 | 南通倍佳机械科技有限公司 | Injection molding machine abnormity analysis method and system |
| CN114925753A (en) * | 2022-04-28 | 2022-08-19 | 南通东升灯饰有限公司 | Use abnormity alarm system of LED floor lamp |
| CN115081966A (en) * | 2022-08-22 | 2022-09-20 | 南通俊朗智能科技有限公司 | Abnormal state monitoring method and aluminum alloy extrusion process controller applying same |
-
2022
- 2022-09-26 CN CN202211169484.3A patent/CN115235676B/en active Active
Patent Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090056467A1 (en) * | 2007-09-05 | 2009-03-05 | Key Energy Services, Inc. | Method and System for Evaluating Rod Breakout Based on Tong Pressure Data |
| CN104481590A (en) * | 2014-11-18 | 2015-04-01 | 煤炭科学技术研究院有限公司 | Long wall coal mining roof pressure hydraulic support resistance change characteristic pre-warning method |
| US20180135412A1 (en) * | 2015-07-20 | 2018-05-17 | Taiyuan University Of Technology | Method for implementing a centralized control platform of hydraulic support on fully mechanized mining working face in underground coal mines |
| JP2017146278A (en) * | 2016-02-19 | 2017-08-24 | 株式会社日立ハイテクノロジーズ | Automatic analyzer and method of detecting abnormality of transport line |
| CN105607630A (en) * | 2016-03-22 | 2016-05-25 | 广州日滨科技发展有限公司 | Self-checking method and device for checking initial state of electro-hydraulic control system of hydraulic support for coal mines |
| WO2017215192A1 (en) * | 2016-06-14 | 2017-12-21 | 中国矿业大学 | Method for evaluating filling characteristics of filling coal-mining hydraulic support |
| US20180294174A1 (en) * | 2017-04-05 | 2018-10-11 | Ebara Corporation | Semiconductor manufacturing apparatus, failure prediction method for semiconductor manufacturing apparatus, and failure prediction program for semiconductor manufacturing apparatus |
| CN110866658A (en) * | 2019-12-05 | 2020-03-06 | 国网江苏省电力有限公司南通供电分公司 | Method for predicting medium and long term load of urban power grid |
| CN111911214A (en) * | 2020-06-24 | 2020-11-10 | 中煤科工开采研究院有限公司 | Method for monitoring working state of safety valve of hydraulic support |
| CN112145231A (en) * | 2020-08-20 | 2020-12-29 | 中煤科工开采研究院有限公司 | Early warning method for working condition of hydraulic support |
| WO2022110557A1 (en) * | 2020-11-25 | 2022-06-02 | 国网湖南省电力有限公司 | Method and device for diagnosing user-transformer relationship anomaly in transformer area |
| CN112528229A (en) * | 2020-11-30 | 2021-03-19 | 太原理工大学 | Working surface hydraulic support quality evaluation method based on control chart analysis |
| WO2022121139A1 (en) * | 2020-12-11 | 2022-06-16 | 中国矿业大学 | Coal/gangue recognition method in top coal caving process based on multi-sensor information fusion |
| CN114111918A (en) * | 2020-12-30 | 2022-03-01 | 中国矿业大学 | Intelligent diagnosis method for working state and supporting quality of hydraulic support |
| CN113283375A (en) * | 2021-06-08 | 2021-08-20 | 中国矿业大学 | Early fault real-time early warning method for mine hydraulic support system |
| CN113569459A (en) * | 2021-06-08 | 2021-10-29 | 中国矿业大学 | Group intelligence-based hydraulic support performance degradation quantitative evaluation method and system |
| CN113672600A (en) * | 2021-07-20 | 2021-11-19 | 微梦创科网络科技（中国）有限公司 | Anomaly detection method and system |
| CN113420465A (en) * | 2021-07-30 | 2021-09-21 | 太原理工大学 | Hydraulic support full-life cycle management method based on digital twin model |
| CN113482677A (en) * | 2021-08-02 | 2021-10-08 | 中煤科工开采研究院有限公司 | Four-column hydraulic support working condition evaluation method based on clustering algorithm |
| CN114254457A (en) * | 2021-12-17 | 2022-03-29 | 中国矿业大学(北京) | Mine hydraulic support fatigue life prediction method based on digital twinning |
| CN114298605A (en) * | 2022-01-04 | 2022-04-08 | 北京康宁电通科技发展有限公司 | Hydraulic support full life cycle evaluation method |
| CN114611378A (en) * | 2022-01-31 | 2022-06-10 | 扬州来源液压设备有限公司 | Hydraulic support rod pressure regulation and control system based on mine industry Internet of things |
| CN114445398A (en) * | 2022-02-16 | 2022-05-06 | 西安华创马科智能控制系统有限公司 | Method and device for monitoring state of side protection plate of hydraulic support of coal mining machine |
| CN114925753A (en) * | 2022-04-28 | 2022-08-19 | 南通东升灯饰有限公司 | Use abnormity alarm system of LED floor lamp |
| CN114770891A (en) * | 2022-06-17 | 2022-07-22 | 南通倍佳机械科技有限公司 | Injection molding machine abnormity analysis method and system |
| CN115081966A (en) * | 2022-08-22 | 2022-09-20 | 南通俊朗智能科技有限公司 | Abnormal state monitoring method and aluminum alloy extrusion process controller applying same |
Non-Patent Citations (6)
| Title |
|---|
| JUN WU 等: "Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems", 《IEEE/ASME TRANSACTIONS ON MECHATRONICS》 * |
| 张蕾等: "基于极限学习机的念坛公园水质预测模型", 《水土保持应用技术》 * |
| 昝瑞栓等: "一种液压支架的寿命评价方法", 《液压与气动》 * |
| 李世科: "基于ＬＭ－ＢＰ神经网络的液压支架顶梁疲劳寿命预测及应用", 《中国矿业》 * |
| 王占飞等: "电液控制系统关键传感器的故障预警方法研究", 《中国煤炭》 * |
| 马旭东 等: "液压支架健康评估与寿命预测模型研究", 《煤炭科学技术》 * |
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