CN107679330B - Real-time evaluation method for rock breaking performance loss degree of TBM cutter head system - Google Patents
Real-time evaluation method for rock breaking performance loss degree of TBM cutter head system Download PDFInfo
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- 230000007547 defect Effects 0.000 abstract description 2
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- 238000005299 abrasion Methods 0.000 description 4
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Abstract
The invention provides a real-time evaluation method for rock breaking performance loss degree of a TBM cutter head system, which is used for solving the problem that the rock breaking performance loss degree of the TBM cutter head system cannot be estimated in real time; establishing an engineering data sample library, and solving a tool changing index counted by days; drawing an FPI-TPI distribution scatter diagram according to the tunneling state data; performing least square regression processing on the obtained FPI-TPI distribution scatter diagram to obtain a regression equation, and solving the upper boundary of the regression equation; counting points higher than an upper boundary in the FPI-TPI distribution scatter diagram, and performing minimum quadratic regression processing on the accumulated deviation sum Set and the cutter changing index W' to obtain a mathematical model of the cutter changing index; the tool changing index is obtained by acquiring the construction parameters of the TBM cutter head system in real time according to the on-site upper computer, guidance is provided for whether the tool needs to be replaced and maintained, the defect that the performance loss degree of the TBM cutter head system cannot be quantitatively judged is overcome, and on-site construction is not influenced.
Description
Technical Field
The invention relates to the technical field of tunnel tunneling equipment construction, in particular to a real-time evaluation method for rock breaking performance loss degree of a TBM cutter head system.
Background
A hard rock tunnel boring machine (hereinafter referred to as hard rock TBM) is large-scale high-tech construction equipment specially applied to rock tunnel excavation and underground passage engineering. The cutterhead tunneling system is the most critical tunneling tool on the hard rock TBM. In the construction process, the abrasion problem of the cutter head in the rock breaking process is very serious due to the complex and variable surrounding rock types. In some projects, the cost and time consumed by the regular inspection, repair and tool change of the tool is nearly one third of the construction cost and the construction time. Therefore, the evaluation of the performance loss degree of the TBM cutter head system can monitor the wear state of the cutter in real time, and convert the past timing maintenance into predicted maintenance and active maintenance, thereby improving the reliability of maintenance or replacement, improving the construction efficiency and saving the construction cost, and the method is an object to be researched by the TBM cutter head system.
Disclosure of Invention
Aiming at the technical problems that the rock breaking performance loss degree of a TBM cutterhead system cannot be estimated in real time, a cutter needs to be checked regularly, and time and labor are consumed, the invention provides the real-time evaluation method of the rock breaking performance loss degree of the TBM cutterhead system, the rock breaking performance loss degree of the cutterhead system is evaluated in real time according to construction parameters acquired by an upper computer in the TBM tunneling process, no additional sensor or procedure is needed, field construction is not affected, and therefore guidance is provided for whether the cutter needs to be replaced and maintained.
In order to achieve the purpose, the technical scheme of the invention is realized as follows: a real-time evaluation method for rock breaking performance loss degree of a TBM cutter head system comprises the following steps:
the method comprises the following steps: establishing an engineering data sample library of construction parameters, tool changing quantity and tool maintenance quantity of a TBM cutter head system, establishing a relation graph of tool changing days and tool changing indexes W 'according to data in the engineering data sample library, and solving the tool changing indexes W' counted by days;
step two: summarizing the tunneling state data of the construction parameters in the engineering data sample library, and independently storing time, penetration p, single-blade thrust Ft and single-blade torque Tn; obtaining a ratio FPI of single-cutter thrust to penetration and a ratio TPI of cutter head torque to penetration according to the tunneling state data, and drawing an FPI-TPI distribution scatter diagram;
step three: performing least square regression processing on the FPI-TPI distribution scatter diagram obtained in the second step to obtain a regression equation: TPI ═ f (fpi), the upper boundary UCL of the regression equation is solved; wherein f represents a functional relationship;
step four: counting the date corresponding to the point higher than the upper boundary UCL in the FPI-TPI distribution scatter diagram, simultaneously counting the deviation cumulative sum Set of the corresponding date, and performing minimum quadratic regression processing on the deviation cumulative sum Set and the cutter changing index W' to obtain a mathematical model of the cutter changing index: w ═ g (set); wherein g represents a functional relationship;
step five: the method comprises the steps of obtaining construction parameters of a TBM cutter head system in real time according to an on-site upper computer, obtaining a ratio FPI of single-cutter thrust to penetration and a ratio TPI of cutter head torque to penetration, obtaining a cutter changing index W according to a mathematical model W ═ g (set) of the cutter changing index, and evaluating the cutter changing in real time.
The method for calculating the tool changing index W' counted according to the day comprises the following steps: the tool changing quantity in the engineering data sample library is counted according to days, a tool changing index W' is weighted according to the influence of different changing reasons on the tunneling performance of a cutter head system, and the value of a weighting coefficient wi is shown in the following table:
the tool changing index W' counted by day is as follows:
and wi is a weighting coefficient of the ith cutter changing reason, Ni is the total number of the ith cutter changing reason changed on the same day, and n is the number of the ith cutter changing reason.
The step of solving the upper boundary UCL of the regression equation is as follows:
the method comprises the following steps: the regression equation TPI' ═ F (FPI) obtained from the FPI-TPI distribution scattergram is: TPI ═ -6.8422+3.3358 in (fpi);
step two: the cumulative sum et of the fitted TPI 'and the actual TPI deviation values is taken from the regression equation TPI' ═ -6.8422+3.3358 in (fpi):
wherein abs is obtained by calculating the absolute value of the difference between abs and TPIi' PreEstimated value of TPI, TPIiThe actual value is obtained according to actual tunneling, j represents a certain tunneling cycle counted by days, and M represents the total number of days;
step three: calculating the mean deviation from the cumulative sum et
And sliding range average
Obtaining an upper boundary UCL:
wherein the content of the first and second substances,
diff (et) refers to the discrete derivation of et.
The method for acquiring the mathematical model W of the tool changing index g (set) comprises the following steps:
the method comprises the following steps: counting the tool changing date of the tunneling circulation higher than the upper boundary UCL, and simultaneously counting the accumulated deviation sum Set under the tool changing date, wherein,
k represents a statistically higher upper bound UCLkM represents that all driving cycles TPI' of the current day of the cycle exceed the upper boundary UCLkThe number of (2).
Step two: sequencing the accumulated deviation sum Set and the corresponding dates thereof in the descending order, and sequencing the cutter changing index W' and the corresponding dates thereof in the descending order, wherein the statistics of the first ten are shown in the following table:
step three: and performing minimum quadratic regression processing on the tool changing index W' and the accumulated deviation sum Set in the table to obtain a mathematical model W of the tool changing index, which is 63.8634+22.2782 Set.
The invention has the beneficial effects that: establishing an engineering data sample library of construction parameters and the number of tools to be replaced and maintained, searching for rules between the construction parameters and the number of tools to be replaced by a big data analysis mode, establishing a mathematical model, and realizing the evaluation of the rock breaking performance loss degree of the cutter head system according to the construction parameters acquired by an on-site upper computer, thereby providing guidance for whether the tools need to be replaced and maintained without adding additional sensors or processes and influencing on-site construction; the defect that the performance loss degree of the TBM cutter head system cannot be quantitatively judged in the prior art is overcome; meanwhile, by using the method for establishing the analysis model based on the field construction data, the limitation that the theoretical analysis model is not suitable for the actual tunneling working condition is avoided, and the guidance suggestion whether the cutter needs to be replaced or not and the maintenance is realized.
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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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a graph showing the relationship between the number of tool change days and the corresponding index of the cutter head.
Fig. 2 is a FPI-KPI distribution scatter plot of all the drive cycles in the engineering database.
FIG. 3 is a fitting regression equation on a scatter plot of the FPI-KPI distribution and its upper boundary UCL.
FIG. 4 is a tool change index and deviation cumulative sum distribution statistical chart according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
A real-time evaluation method for rock breaking performance loss degree of a TBM cutter head system comprises the following steps:
the method comprises the following steps: and establishing an engineering data sample library of the construction parameters, the cutter replacing number and the cutter maintaining number of the TBM cutter head system, establishing a relation graph of the number of cutter changing days and the cutter changing index W 'according to data in the engineering data sample library, and solving the cutter changing index W' counted by days.
The construction parameters can be summarized through the tunneling parameters recorded by the upper computer, and the average value of the single-cutter thrust-penetration ratio FPI and the cutter head torque-penetration ratio TPI of each tunneling cycle is summarized. In addition, the tool replacement time, the tool replacement number and the tool replacement reason are summarized, so that the subsequent use is facilitated.
And the tool changing index W' counted by days represents the loss degree of the performance of the current whole cutter head. The method for calculating the tool changing index W' of the tool counted by days comprises the following steps: and counting the number of tool changes in the engineering data sample library according to the days, weighting the tool change index W 'according to the influence of different change reasons on the tunneling performance of the cutter head system, and solving the tool change index W' counted according to the days. The values of the weighting coefficients wi are shown in table 1.
TABLE 1 weighted weight of different cutter changing reasons to the tunneling performance of cutter head system
| Reason for changing tools | Normal wear and tear | String grinder | The cutter ring is broken, dropped, damaged, etc | Bearing damage | Eccentric wear |
| Weighting coefficient wi | 7 | 10 | 10 | 9 | 8 |
The tool changing index W' counted by day is as follows:
and wi is a weighting coefficient of the ith cutter changing reason, Ni is the total number of the ith cutter changing reason changed on the same day, and n is the number of the ith cutter changing reason. The tool changing index W 'counted by day is in negative correlation with the tunneling performance of the current tunneling machine cutter head system, and the tool changing index W' in the engineering data sample library is shown in figure 1.
As can be seen from fig. 1, the number of days with tool changing in the engineering data sample library is 144 days, and the tool changing index W' counted by days gradually increases in an intensive manner with the increase of time, which corresponds to the tendency that the overall tunneling performance of the cutterhead system decreases with the increase of tunneling time.
Step two: and summarizing the tunneling state data of the construction parameters in the engineering data sample library, and independently storing the time, the penetration degree p, the single-blade thrust Ft and the single-blade torque Tn. And (3) solving a ratio FPI of the single-cutter thrust to the penetration and a ratio TPI of the cutter head torque to the penetration according to the tunneling state data, and drawing a distribution scatter diagram of the FPI-TPI.
The time, the penetration degree p, the single-blade thrust force Ft and the single-blade torque Tn are independently stored, so that the ratio FPI of the single-blade thrust force to the penetration degree and the ratio TPI of the cutter head torque to the penetration degree are conveniently obtained subsequently, and then an FPI-KPI distribution scatter diagram is obtained according to the tunneling state data, as shown in fig. 2.
Step three: performing least square regression processing on the FPI-TPI distribution scatter diagram obtained in the second step to obtain a regression equation: TPI ═ f (fpi), the upper boundary UCL of the regression equation is solved; wherein f represents a functional relationship.
And summarizing according to the obtained FPI-TPI mean values, and solving a regression model and an upper boundary UCL of the FPI-TPI mean values. The least square regression method is a mathematical model solving method, a nonlinear law of the method is found out according to distribution scatter points, and the mathematical model is fitted by a least square method after a nonlinear function of the method is converted into a linear function. The step of solving the upper boundary UCL of the regression equation is as follows:
step 1: the regression equation TPI' ═ F (FPI) obtained from the FPI-TPI distribution scattergram is: TPI ═ -6.8422+3.3358 in (fpi);
step 2: the cumulative sum et of the fitted TPI 'and the actual TPI deviation values is taken from the regression equation TPI' ═ -6.8422+3.3358 in (fpi):
wherein abs is obtained by calculating the absolute value of the difference between abs and TPIi' estimated TPI value, TPIiThe actual value is obtained according to actual tunneling, j represents a certain tunneling cycle counted by days, and M represents the total number of days;
and step 3: calculating the mean deviation from the cumulative sum etAnd sliding range average
Obtaining an upper boundary UCL:
wherein the content of the first and second substances,
diff (et) refers to the discrete derivation of et.
The upper boundary UCL of the FPI-KPI distribution scatter plot is shown in FIG. 3.
Step four: counting the date corresponding to the point higher than the upper boundary UCL in the FPI-TPI distribution scatter diagram, simultaneously counting the deviation cumulative sum Set of the corresponding date, and performing minimum quadratic regression processing on the deviation cumulative sum Set and the cutter changing index W' to obtain a mathematical model of the cutter changing index: w ═ g (set); wherein g represents a functional relationship.
The method for acquiring the mathematical model W of the tool changing index g (set) comprises the following steps:
step 1: counting the tool changing date of the tunneling circulation higher than the upper boundary UCL, and simultaneously counting the accumulated deviation sum Set under the tool changing date, wherein,
k represents a statistically higher upper bound UCLkM represents that all driving cycles TPI' of the current day of the cycle exceed the upper boundary UCLkThe number of (2).
Step 2: and (3) sequencing the accumulated deviation sum Set and the corresponding dates thereof according to the sequence from large to small, sequencing the cutter changing index W' and the corresponding dates thereof according to the sequence from large to low, and counting the first ten as shown in Table 2. The two are sorted separately, and the first ten bits with the largest data are taken out.
TABLE 2 cumulative sum of deviation and tool change index statistical table
As can be seen from table 2, when the tool changing index is large, it means that the number of tool changes is large on the same day, and the tunneling performance of the cutterhead system is reduced to a high degree; when the deviation accumulation and the Set index are high, the heading performance of the cutterhead system predicted by the mathematical model is high in decline degree. The date of the two is mutually corresponding, and the numerical values have a corresponding relation, which means that the actual tunneling performance reduction condition of the cutterhead system can be predicted through a mathematical model. The first 10 cutters are selected because when the number of cutters is large, the batch abrasion of the cutters is predicted, and because the abrasion is usually caused by abnormal reasons, the operators can be informed to quickly respond to the prediction in advance, the tunneling parameters can be adjusted in time or field inspection can be carried out, and the cutter abrasion damage to a greater extent is avoided.
And step 3: and performing minimum quadratic regression processing on the tool changing index W' and the accumulated deviation sum Set in the table 2 to obtain a mathematical model W of the tool changing index, which is 63.8634+22.2782 Set.
The fitted curve obtained by the minimum quadratic regression is shown in fig. 4, and a model for obtaining the tool changing index W is obtained, wherein the straight line is fitted in fig. 4, so that the strong linear correlation relationship exists between the tool changing index and the accumulated sum of the deviations, and the tool changing index can be predicted according to the accumulated sum of the deviations.
Step five: the method comprises the steps of obtaining construction parameters of a TBM cutter head system in real time according to an on-site upper computer, obtaining a ratio FPI of single-cutter thrust to penetration and a ratio TPI of cutter head torque to penetration, obtaining a cutter changing index W according to a mathematical model W ═ g (set) of the cutter changing index, and evaluating the cutter changing in real time.
According to the input values of the tunneling parameters FPI and KPI, the invention calculates the deviation accumulated Set value, namely the tool changing index W of the tool can be calculated, and whether the tool needs to be replaced or maintained or not can be estimated. According to the tool changing index W, whether the tool needs to be changed or not can be directly judged, and a guidance suggestion is provided for maintenance.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A real-time evaluation method for rock breaking performance loss degree of a TBM cutter head system is characterized by comprising the following steps:
the method comprises the following steps: establishing an engineering data sample library of construction parameters, tool changing quantity and tool maintenance quantity of a TBM cutter head system, establishing a relation graph of tool changing days and tool changing indexes according to data in the engineering data sample library, and solving a tool changing index W' counted by days;
step two: summarizing the tunneling state data of the construction parameters in the engineering data sample library, and independently storing time, penetration p, single-blade thrust Ft and single-blade torque Tn; obtaining a ratio FPI of single-cutter thrust to penetration and a ratio TPI of cutter head torque to penetration according to the tunneling state data, and drawing an FPI-TPI distribution scatter diagram;
step three: performing least square regression processing on the FPI-TPI distribution scatter diagram obtained in the second step to obtain a regression equation: TPI ═ f (fpi), the upper boundary UCL of the regression equation is solved; wherein f represents a functional relationship;
step four: counting the date corresponding to the point higher than the upper boundary UCL in the FPI-TPI distribution scatter diagram, simultaneously counting the deviation cumulative sum Set of the corresponding date, and performing minimum quadratic regression processing on the deviation cumulative sum Set and the cutter changing index W' to obtain a mathematical model of the cutter changing index: w ═ g (set); wherein g represents a functional relationship, and W is a predicted value;
step five: the method comprises the steps of obtaining construction parameters of a TBM cutter head system in real time according to an on-site upper computer, obtaining a ratio FPI of single-cutter thrust to penetration and a ratio TPI of cutter head torque to penetration, obtaining a cutter changing index W according to a mathematical model W ═ g (set) of the cutter changing index, and evaluating the cutter changing in real time.
2. The method for evaluating the rock breaking performance loss degree of the TBM cutter head system according to claim 1, wherein the method for calculating the tool changing index W' counted by days comprises the following steps: the tool changing quantity in the engineering data sample library is counted according to days, a tool changing index W' is weighted according to the influence of different changing reasons on the tunneling performance of a cutter head system, and the value of a weighting coefficient wi is shown in the following table:
；
The tool changing index W' counted by day is as follows:
and wi is a weighting coefficient of the ith cutter changing reason, Ni is the total number of the ith cutter changing reason changed on the same day, and n is the number of the ith cutter changing reason.
3. The method for evaluating the rock breaking performance loss degree of the TBM cutterhead system in real time according to claim 1, wherein the step of solving the upper boundary UCL of the regression equation is as follows:
the method comprises the following steps: the regression equation TPI' ═ F (FPI) obtained from the FPI-TPI distribution scattergram is: TPI ═ -6.8422+3.3358 in (fpi);
step two: calculating a fitted TPI ' according to a regression equation TPI ' ═ 6.8422+3.3358 in (FPI) 'Cumulative sum with actual TPI deviation value et:
wherein abs is obtained by calculating the absolute value of the difference between abs and TPI'jIs an estimated TPI value, TPIjThe actual value is obtained according to actual tunneling, j represents a certain tunneling cycle counted by days, and M represents the total number of days;
step three: calculating the mean deviation from the cumulative sum et
And sliding range average
Obtaining an upper boundary UCL:
wherein the content of the first and second substances,
diff (et) refers to the discrete derivation of et, and N is the number of cycles involved in the statistics.
4. The method for evaluating the rock breaking performance loss degree of the TBM cutter head system according to claim 3, wherein the step of obtaining the mathematical model W (g (set)) of the cutter changing index is as follows:
the method comprises the following steps: counting the tool changing date of the tunneling circulation higher than the upper boundary UCL, and simultaneously counting the accumulated deviation sum Set under the tool changing date, wherein,
k represents a statistically higher upper bound UCLkM represents that all driving cycles TPI' of the current day of the cycle exceed the upper boundary UCLkThe number of (2);
step two: sequencing the accumulated deviation sum Set and the corresponding dates thereof according to the sequence from large to small, and simultaneously sequencing the tool changing index W' counted according to days and the corresponding dates thereof according to the sequence from large to small, wherein the statistics of the first ten are shown in the following table:
step three: and performing minimum quadratic regression processing on the tool changing index W' counted by days and the accumulated deviation sum Set in the table to obtain a mathematical model W of the tool changing index which is 63.8634+22.2782 Set.
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| CN104863604A (en) * | 2015-02-04 | 2015-08-26 | 天津大学 | Method for real-time estimation of tool abrasion condition of cutter head tunneling system of hard rock tunnel boring machine |
| CN105260513A (en) * | 2015-09-23 | 2016-01-20 | 中国人民解放军军事交通学院 | Judging method of boring state of hard rock TMB |
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| CN105260513A (en) * | 2015-09-23 | 2016-01-20 | 中国人民解放军军事交通学院 | Judging method of boring state of hard rock TMB |
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Denomination of invention: A real-time evaluation method of rock breaking performance loss of TBM cutter head system Effective date of registration: 20221226 Granted publication date: 20201030 Pledgee: China Construction Bank Corporation Zhengzhou Railway Sub Branch Pledgor: CHINA RAILWAY ENGINEERING EQUIPMENT GROUP Co.,Ltd. Registration number: Y2022980029005 |
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| PC01 | Cancellation of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Granted publication date: 20201030 Pledgee: China Construction Bank Corporation Zhengzhou Railway Sub Branch Pledgor: CHINA RAILWAY ENGINEERING EQUIPMENT GROUP Co.,Ltd. Registration number: Y2022980029005 |