CN115387777A - Feeding and rotating control method of hydraulic tunnel drilling machine based on coal rock sensing - Google Patents
Feeding and rotating control method of hydraulic tunnel drilling machine based on coal rock sensing Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
- E21B44/02—Automatic control of the tool feed
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
Abstract
The invention provides a feeding and rotating control method of a hydraulic tunnel drilling machine based on coal rock sensing, which comprises the following steps: step one, acquiring characteristics; step two, characteristic screening; step three, building a coal bed hardness recognition model; step four, obtaining a recommended drilling speed and a recommended rotating speed; step five, preprocessing an input signal; step six, constructing a feeding rotary control system; step seven, constant torque control; step eight, controlling the drilling speed constantly; step nine, controlling the drilling speed in a fine adjustment manner; the method can break through the excessive dependence of the traditional drilling process on manual experience, obtains the optimal drilling speed and the rotating speed under the current drilling working condition through the coal bed hardness recognition module and the optimal drilling speed and rotating speed recommendation module, realizes the control of the drilling speed of the drilling machine on the basis of constant torque control, and meanwhile finely adjusts the recommended drilling speed by utilizing the rotating speed, so that the efficiency of the drilling process can be effectively improved, and the risk of the drilling process is reduced.
Description
Technical Field
The invention belongs to the technical field of underground coal mine tunnel drilling, relates to feeding and rotating control of a hydraulic tunnel drilling machine, and particularly relates to a feeding and rotating control method of the hydraulic tunnel drilling machine based on coal rock sensing.
Background
The hydraulic tunnel drilling machine is widely used for underground gas extraction of coal mines, rock burst prevention and control, water exploration and drainage of top and bottom plates, advanced detection of driving tunnels, detection of hidden disaster factors and the like. However, in the drilling process of the hydraulic tunnel drilling rig, the geomechanical environment of a coal seam is complex, and the characteristics of nonlinearity, strong coupling and strong interference, such as faults, collapse columns, tectonic zones, geological abnormal bodies and the like, are prominent, so that the feeding resistance of a feeding system and the load torque of a rotary system of the hydraulic tunnel drilling rig are complex and various. The feeding rotary system of the drilling machine is required to be capable of adapting to changes of coal beds, otherwise, the drilling efficiency of the drilling machine can be reduced, the working period is influenced, and meanwhile, the accidents of drill sticking, drill breaking and the like can be even caused by inappropriate drilling pressure and rotating speed.
The drilling pressure and the rotating speed of the existing hydraulic underground drill rig cannot be automatically adjusted according to working conditions, construction personnel need to manually adjust according to observed main parameters such as the drilling pressure, the rotary pressure, the rotating speed, the slag return and water return conditions and the like, various working condition parameters and control parameters cannot be comprehensively collected in real time, various data cannot be timely and accurately judged and decided, the drilling parameters are adjusted completely depending on the experience of the construction personnel in the drilling construction process, and the development of underground coal mine drilling technology is severely restricted. Therefore, the intelligent sensing and control technology for the change of the coal bed by researching the bit pressure and the rotating speed of the hydraulic tunnel drilling machine has important practical significance and application value for realizing the intelligent development of 'mechanical person changing and automatic person reducing' under the coal mine.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a feeding and rotating control method of a hydraulic underground drill rig based on coal rock sensing, and solve the technical problems that the prior art excessively depends on manual experience and intelligent sensing and self-adaptive control cannot be performed on the drilling working condition.
In order to solve the technical problems, the invention adopts the following technical scheme:
the overall technical concept of the technical scheme adopted by the invention is as follows: analyzing the correlation between the coal bed hardness and the drilling process parameters according to the drilling machine working parameters in the drilling process, selecting the bit pressure and the rotary pressure as the input parameters of the coal bed hardness identification model to be established; respectively extracting features of the drilling process data by wavelet packet decomposition to obtain sample data and a test set, and outputting the sample data and the test set by using the prior coal bed hardness as a model; and training and verifying the coal bed hardness recognition model by using a Back Propagation (BP) neural network to obtain a final coal bed hardness recognition model. And obtaining the recommended set values of the drilling speed and the rotating speed under the current working condition according to the coal bed hardness f. The feeding rotation control system of the drilling machine realizes the constant rotation pressure control (constant rotation torque control) of the drilling machine by adjusting the bit pressure under the action of the PID1, and the setting torque is adjusted by the PID2 in consideration of the drilling speed on the basis, so that the control of the drilling speed is realized. Meanwhile, the recommended drilling speed is finely adjusted by utilizing the rotation speed, so that the safety of the drilling machine is ensured.
Compared with the prior art, the invention has the following technical effects:
the method can break through the excessive dependence of the traditional drilling process on manual experience, obtains the optimal drilling speed and the rotating speed under the current drilling working condition through the coal bed hardness recognition module and the optimal drilling speed and rotating speed recommendation module, realizes the control of the drilling speed of the drilling machine on the basis of constant torque control, can effectively improve the efficiency of the drilling process, and reduces the risk of the drilling process.
The method comprises the steps of coal bed hardness identification, optimal rotating speed and drilling speed recommendation, drilling speed fine adjustment and drilling speed control based on torque control, and can lay a good foundation for intelligent feeding and rotating control of the drilling machine.
Drawings
Fig. 1 is a torque variation curve under constant torque control.
Fig. 2 is a rotation speed variation curve under constant torque control.
Fig. 3 is a plot of the rate of penetration under constant torque control.
Fig. 4 is a feed force variation curve under constant torque control.
Fig. 5 is a graph showing the change in weight on bit under constant torque control.
Fig. 6 is a torque curve under constant rate of penetration control.
Fig. 7 is a graph showing the variation of the rotation speed under the constant drilling rate control.
Fig. 8 is a plot of the rate of penetration under constant rate of penetration control.
Fig. 9 is a feed force variation curve under constant drilling rate control.
Fig. 10 is a graph showing the change in weight-on-bit under constant rate-of-penetration control.
Fig. 11 is a torque curve for setting the rate of penetration adjustment.
Fig. 12 is a graph of the change in rotational speed at the set rate of penetration adjustment.
Fig. 13 is a graph of the change in drilling rate with the set rate of penetration adjustment.
Fig. 14 is a feed force variation curve at set drilling rate adjustment.
Fig. 15 is a graph of weight on bit change at set rate of penetration adjustment.
Fig. 16 is a structural view of the feed swing control system.
Fig. 17 is a flow chart of feature extraction using wavelet packet decomposition.
The present invention will be explained in further detail with reference to examples.
Detailed Description
It should be noted that all algorithms, modules and devices in the present invention, unless otherwise specified, all employ algorithms, modules and devices known in the art.
The following embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention are within the protection scope of the present invention.
Example (b):
the embodiment provides a feeding and rotating control method of a hydraulic tunnel drilling machine based on coal rock sensing, which comprises the following steps:
step one, feature acquisition:
the working condition in the drilling process is complex, the frequency of the rotary pressure and the bit pressure signals acquired by the sensor is not concentrated, the interference of the coal bed is large, the signal fluctuation is large, the fitting is difficult to be carried out by using a determined function, the hardness of the coal bed under the current working condition is difficult to judge from the signals, and therefore the wavelet packet transformation is adopted to carry out feature extraction on the state parameters of the drilling machine.
And selecting the bit pressure and the rotation pressure of the drilling machine as input signals for identifying the hardness of the coal bed, and decomposing the bit pressure and the rotation pressure by wavelet packet decomposition to obtain a characteristic vector.
Specifically, as shown in fig. 17, the wavelet packet decomposition adopts three-level wavelet packet decomposition, and a mathematical expression of a signal reconstruction function after decomposition is as follows:
S 3 ＝S 31 +S 32 +S 33 +S 34 +S 35 +S 36 +S 37 ++S 38
in the formula:
S 3 is a signal reconstruction function;
S 3j j =1,2 and … for a j frequency band signal reconstruction function after three-level wavelet packet decomposition;
energy E of reconstructed signal 3j Comprises the following steps:
E 3j ＝∫|S 3j (t)| 2 dt
in the formula:
E 3j to reconstruct the energy of the signal;
t is time.
Energy E of reconstructed signal 3j And as the characteristics of each frequency band signal, performing characteristic extraction on the bit pressure and the rotation pressure to obtain a characteristic vector E which is as follows:
E＝[E 31j ,E 32j ]
in the formula:
E 31j the j frequency band signal characteristic vector after the drill pressure decomposition is obtained;
E 32j the characteristic vector of the j frequency band signal after the revolution pressure decomposition is obtained;
j＝1，2，…8。
step two, characteristic screening:
the information of each frequency band can not reflect the hardness of the coal bed, and the characteristic components need to be screened.
Performing correlation analysis on each component in the feature vector E obtained in the step one and the coal bed hardness to obtain a strong correlation feature vector consisting of components with strong correlation with the coal bed hardness; and normalizing the strong correlation characteristic vector to obtain a normalized characteristic vector.
The specific process of the correlation analysis adopts a conventional correlation analysis method, for example, a Pearson correlation analysis method.
The expression of the strong correlation characteristic vector is as follows:
E'＝[E' 31m ,E' 32n ]
in the formula:
e' is a strongly correlated feature vector;
E′ 31m the m frequency band signal after the weight on bit decomposition is a strong correlation characteristic vector;
E′ 32n and (4) strongly correlating the nth frequency band signal after the revolving pressure decomposition with the characteristic vector.
The normalized formula is as follows:
in the formula:
e' is the normalized feature vector.
Step three, building a coal bed hardness recognition model:
the BP neural network is structurally divided into an input layer, a hidden layer and an output layer, wherein the activation functions of the hidden layer and the output layer are sigmod functions:
the input layer to hidden layer expression can be obtained as follows:
f k ＝out([ω k,1 ,ω k,2 ,…,ω k,m ,ω k,m+1 ,…ω k,m+n ]*E' T +b k )
the expression from hidden layer to output layer is:
f＝out([ω 1 ,ω 2 ,…,ω k ]*[f 1 ,f 2 ,…,f k ] T +b)
wherein, ω is k,j K is the number of nodes of the hidden layer, and j =1 … m + n is the weight of the hidden layer; b k A threshold value for the hidden layer; omega i I =1 … k is the weight of the output layer, and b is the threshold of the output layer.
Based on the BP neural network, the normalized feature vector is used as the input quantity of the BP neural network, the BP neural network is trained, and the coal bed hardness recognition model is obtained after the training.
In the coal bed hardness identification model, the value range of the coal bed hardness is [0.5, 10], the output range of the BP neural network is [0,1], and the coal bed hardness is mapped by linear mapping; the linear mapping formula is as follows: y =9.5 x +0.5, where x is the output of the BP neural network; and y is the hardness of the coal bed.
For formations with a hardness greater than 10 that may be present in a coal seam, the present invention defaults the hardness to 10.
Step four, obtaining the recommended drilling speed and the recommended rotating speed:
in a certain range, the higher the hardness of the coal seam is, the lower the required drilling speed is, and the lower the rotating speed is.
And taking the normalized feature vector obtained in the step two as an input quantity of the coal bed hardness recognition model constructed in the step three, obtaining the coal bed hardness through the coal bed hardness recognition model, and obtaining the recommended drilling speed and the recommended rotating speed under the current working condition according to the coal bed hardness as follows:
in the formula:
theta is the recommended rotating speed under the current working condition;
v is recommended drilling speed under the current working condition;
d is the effective diameter of the drilling tool;
f is the hardness of the coal rock;
k 1 ，k 2 is a proportionality coefficient;
θ 0 ，V 0 the average values of the rotating speed and the drilling speed of the drilling machine in long-term operation are respectively.
Step five, input signal preprocessing:
in the controller, the feedback signal of the PID1 is the rotation pressure difference, the feedback signal of the PID2 is the drilling speed, and a large amount of burrs and peaks exist in the complex environment of the coal bed, so that the output of the controller is frequently changed, and the input signal needs to be filtered.
Preprocessing the recommended drilling speed and the recommended rotating speed obtained in the step four to obtain the preprocessed recommended drilling speed and the preprocessed recommended rotating speed; the specific process of the pretreatment comprises the following steps: firstly, carrying out amplitude limiting filtering to eliminate a peak in a signal; and then, recursive average filtering is carried out, so that the fluctuation amplitude of the input signal is reduced, and the stability of the system is improved.
The formula of the amplitude limiting filtering is as follows:
the formula of the recursive average filtering is as follows:
in the formula:
out (n) is the limiting and filtering result of the drilling speed or the rotating speed;
ErrorMax allows the maximum amount of fluctuation for the feed slewing system.
Step six, constructing a feeding rotation control system:
during the drilling process of the drilling machine, the Weight On Bit (WOB) of the drill bit is mainly generated due to the resistance of the coal bed, and the cohesive damping model describes the interaction between the drill bit and the coal bed in the axial direction, and can be specifically expressed as:
in the formula:
F d is the interaction force between the drill bit and the coal seam;
k r the Permeability Resistance Coefficient (PRC) of a coal seam depends on the hardness of the coal seam.
The rotary torque of the drilling machine is that the drill bit rotates to cut a coal bed in the drilling process, the drill bit and the coal bed interact in the rotating direction to generate the rotary torque, and the karnopp model describes the relationship among the rotating speed, the drilling pressure and the rotary resistance, and is specifically represented as follows:
wherein T is a rotational torque, R b Is the drill diameter, mu sb ,μ cb As coefficient of static friction and sliding friction of drill bitThe coefficient of friction depends on the hardness of the coal seam.
Through the karnopp model and the drilling process, the rotation torque of the rotation system reflects the drilling load, meanwhile, the change of the torque can be realized by changing the bit pressure, the rotation torque of the drilling machine is kept constant, and the drilling machine can work at the rated load.
Based on the analysis, the invention firstly designs PID1 to realize constant torque control by adjusting the bit pressure; when the rotary torque is larger, the bit pressure is properly reduced, so that accidents such as drill jamming and the like caused by overlarge drilling load are prevented, and when the rotary torque is smaller, the bit pressure can be properly increased, and the drilling speed is improved. Based on the obtained optimal torque set value, the system can keep the optimal drilling load all the time, and the system can work with the maximum efficiency on the basis of safety.
The rotary torque can not be measured, and the working principle of the hydraulic motor can be known, when the rotating speed is stable, the rotary pressure difference is in direct proportion to the load torque, so that the constant torque control of the drilling machine can be realized by realizing the constancy of the rotary pressure difference. Therefore, the set value of the constant torque controller is the rotary pressure difference corresponding to the target torque, the feedback quantity is the rotary pressure difference, the controller adopts an integral separation PI controller, and the output of the controller
out＝k p *error+ε(error)k i *error
In the formula, error set The method is characterized in that an error integral upper limit is set according to the performance of the drilling machine, and an integral separation method is adopted, so that the phenomenon that an integral term in a constant torque control system is too large can be effectively avoided, and the influence on the system regulation time is reduced.
As shown in fig. 16, the feed rotation control system includes PID1, PID2 and a drilling rate fine-tuning module; the PID1 and the PID2 both adopt integral separation PI controllers; the drilling speed fine-tuning module comprises:
in the formula:
Feedspeed set setting the drilling speed;
Feedspeed 0 the drilling speed is recommended;
Speed 0 a threshold value for rotational speed reduction;
Rspeed now is a measure of rotational speed;
Rspeed set is a set value of the rotating speed;
k is an integral coefficient;
1/s is integral operation;
ε is the start signal.
The input end of the PID1 inputs a torque signal of the drilling machine in the coal bed load, and the output end of the PID1 outputs a bit pressure signal.
And a drilling speed signal of the drilling machine in the coal bed load and the preprocessed recommended drilling speed signal obtained in the fifth step are input into the input end of the PID2, and a torque signal is output from the output end of the PID2 to the input end of the PID 1.
The input end of the drilling speed fine adjustment module inputs a rotating speed signal of a rotary system and a rotating speed signal of a drilling machine in coal bed load, and the output end of the drilling speed fine adjustment module outputs a drilling speed fine adjustment quantity signal to the input end of the PID 2.
In this embodiment, the feed system and the swing system are both known systems in a drilling rig.
Step seven, constant torque control:
and inputting the preprocessed recommended rotating speed obtained in the fifth step into a rotating system of the drilling machine for rotating speed control.
And in the feeding rotary control system built in the step six, a set constant target torque signal is input to the input end of the PID1, the set constant target torque signal is compared with a torque signal of the drilling machine in the coal bed load, which is input to the input end of the PID1, and the bit pressure signal output by the output end of the PID1 controls the bit pressure of the feeding system of the drilling machine, so that constant torque control is realized.
In the embodiment, the response curve of the system when the rotation speed of the drilling machine is 100r/min and the set pressure difference is 10Mpa is shown in fig. 1 to 5, the hardness of the coal seam is suddenly increased when 50s is reached.
As can be seen from fig. 1 to 5, the slewing torque is stabilized at a value corresponding to 10Mpa slewing differential pressure by adjusting the weight on bit (feed force) under the action of PID1, as shown in 20-40 s. At 50s, due to the fact that the coal bed hardness is suddenly increased, the gyration torque is increased under the effect of the current feeding force, and in order to maintain the gyration torque unchanged, PID1 reduces the torque by reducing the bit pressure, as shown in 50-60 seconds. It can be known from simulation that PID1 enables constant torque control during drilling.
Step eight, controlling the constant drilling speed:
on the basis of the seventh step, inputting the preprocessed recommended drilling rate obtained in the fifth step into a PID2 as a constant input signal, in the PID2, comparing the preprocessed recommended drilling rate signal with a drilling rate signal of the drilling machine in a coal bed load, outputting a torque signal output by the PID2 as a new target torque signal to the PID1, and then performing a control method which is the same as the constant torque control in the seventh step to control the drilling pressure of a feeding system of the drilling machine so as to realize the constant drilling rate control;
in the drilling process of the drilling machine, the hardness of the coal bed is complex and various, an ideal control effect is difficult to realize only by adopting constant torque control, when the hardness of the coal bed is suddenly reduced, if the rotary torque is kept unchanged, the drilling pressure of the system is overhigh, the drilling speed is overhigh, and the safety of the drilling process is reduced. Considering the actual drilling working condition, when the load torque is the same, if the hardness of the coal seam is large, the drilling speed of the system is slow, the torque is increased, and if the hardness of the coal seam is small, the drilling speed of the system is fast, so that the set value of the rotary pressure difference can be finely adjusted according to the drilling speed, the error of the recommended drilling speed and the actual drilling speed is used as input, the set value of the rotary torque is used as output, and a controller PID2 is designed to achieve fine adjustment of the rotary torque.
In the embodiment, the drilling rig increases the hardness of the coal seam at the drilling speed of 5mm/s and at 50s, and the change curves of main variables of the drilling rig are shown in fig. 6 to 10.
As can be seen from fig. 6 to 10, the recommended drilling speed is 5mm/s, in order to enable the drilling machine to reach the current drilling speed, the power of the drilling machine, that is, the set value of the slewing torque, needs to be adjusted, and further the bit pressure is changed to realize the control of the drilling speed, the adjustment process of each parameter is as shown in fig. 6 to 10, the hardness of the coal seam is increased at 50s, so that the drilling speed is suddenly reduced, and in order to realize the control of the drilling speed, the PID2 increases the set value of the slewing torque to realize the constant drilling speed.
Step nine, drilling speed fine adjustment control:
on the basis of the step eight, in a drilling speed fine adjustment module, comparing a rotating speed signal of a rotary system with a rotating speed signal in a coal bed load, and outputting a drilling speed fine adjustment quantity signal to a PID2 by the drilling speed fine adjustment module; in PID2, the drilling speed fine adjustment quantity signal and the preprocessed recommended drilling speed signal are summed, and then the sum is used as a new propelling drilling speed to be compared with the drilling speed signal in the coal bed load, and then the control method which is the same as the constant drilling speed control in the step eight is carried out to control the drilling pressure of the feeding system of the drilling machine, so that the drilling speed fine adjustment control is realized.
For the recommended drilling rate, the unreasonable situation also exists, when the recommended drilling rate is too large for the current working condition, the rotation torque of the drilling machine is too large, and further the rotation speed of the system is reduced, so that the recommended drilling rate is finely adjusted according to the actual rotation speed and the set rotation speed, and the value of the set drilling rate is as follows:
in the formula:
Feedspeed set setting the drilling speed;
Feedspeed 0 the drilling speed is recommended;
Speed 0 a threshold value for the rotational speed reduction;
Rspeed now is a measure of rotational speed;
Rspeed set is a set value of the rotating speed;
k is an integral coefficient;
1/s is integral operation;
ε is the start signal.
And when the actual rotating speed is lower than the set value to a certain degree, reducing the recommended drilling speed, and further reducing the rotary torque and the drilling pressure of the system, so that the drilling machine works in a safe range. The variation curves of the main variables of the rig are shown in fig. 11 to 15.
From fig. 11 to fig. 15, it can be seen that the hardness of the coal seam becomes high at 50s, and the recommended drilling rate is too high for the current working condition, so that the rotation torque of the system is too high, and the rotation speed is reduced. At the moment, the drilling speed fine adjustment module can reduce the set value of the drilling speed, reduce the drilling speed and the torque and achieve the purpose of keeping the rotation speed of the drilling machine.
Claims (5)
1. A feeding and rotating control method of a hydraulic tunnel drilling machine based on coal rock sensing is characterized by comprising the following steps:
step one, feature acquisition:
selecting the bit pressure and the rotation pressure of a drilling machine as input signals for coal bed hardness identification, and decomposing the bit pressure and the rotation pressure by wavelet packet decomposition to obtain a characteristic vector;
step two, characteristic screening:
performing correlation analysis on each component in the characteristic vector E obtained in the step one and the coal bed hardness to obtain a strong correlation characteristic vector consisting of components with strong correlation with the coal bed hardness; normalizing the strong correlation characteristic vector to obtain a normalized characteristic vector;
step three, building a coal bed hardness recognition model:
based on the BP neural network, taking the normalized feature vector as the input quantity of the BP neural network, training the BP neural network, and obtaining a coal bed hardness recognition model after training;
step four, obtaining the recommended drilling speed and the recommended rotating speed:
and taking the normalized feature vector obtained in the step two as an input quantity of the coal bed hardness recognition model constructed in the step three, obtaining the coal bed hardness through the coal bed hardness recognition model, and obtaining the recommended drilling speed and the recommended rotating speed under the current working condition according to the coal bed hardness as follows:
in the formula:
theta is the recommended rotating speed under the current working condition;
v is recommended drilling speed under the current working condition;
d is the effective diameter of the drilling tool;
f is the hardness of the coal rock;
k 1 ，k 2 is a proportionality coefficient;
θ 0 ，V 0 respectively the average values of the rotating speed and the drilling speed when the drilling machine works for a long time;
step five, preprocessing an input signal:
preprocessing the recommended drilling speed and the recommended rotating speed obtained in the step four to obtain the preprocessed recommended drilling speed and the preprocessed recommended rotating speed; the specific process of the pretreatment comprises the following steps: firstly, carrying out amplitude limiting filtering to eliminate a peak in a signal; then, carrying out recursive average filtering to reduce the fluctuation amplitude of the input signal;
step six, constructing a feeding rotation control system:
the feeding rotation control system comprises a PID1, a PID2 and a drilling speed fine adjustment module; the PID1 and the PID2 both adopt integral separation PI controllers; the drilling speed fine-tuning module comprises:
in the formula:
Feedspeed set setting the drilling speed;
Feedspeed 0 the drilling speed is recommended;
Speed 0 a threshold value for the rotational speed reduction;
Rspeed now is a measure of rotational speed;
Rspeed set is a set value of the rotating speed;
k is an integral coefficient;
1/s is integral operation;
ε is the start signal.
The input end of the PID1 inputs a torque signal of the drilling machine in the coal bed load, and the output end of the PID1 outputs a bit pressure signal;
the input end of the PID2 inputs a drilling speed signal of a drilling machine in a coal bed load and the preprocessed recommended drilling speed signal obtained in the step five, and the output end of the PID2 outputs a torque signal to the input end of the PID 1;
the input end of the drilling speed fine-tuning module inputs a rotating speed signal of a rotary system and a rotating speed signal of a drilling machine in a coal bed load, and the output end of the drilling speed fine-tuning module outputs a drilling speed fine-tuning quantity signal to the input end of the PID2;
step seven, constant torque control:
inputting the preprocessed recommended rotating speed obtained in the fifth step into a rotating system of the drilling machine for rotating speed control;
in the feeding rotary control system built in the sixth step, a set constant target torque signal is input to the input end of the PID1, the set constant target torque signal is compared with a torque signal of the drilling machine in the coal bed load, which is input to the input end of the PID1, and a bit pressure signal output by the output end of the PID1 controls the bit pressure of the feeding system of the drilling machine, so that constant torque control is realized;
step eight, controlling the drilling speed constantly:
on the basis of the seventh step, inputting the preprocessed recommended drilling speed obtained in the fifth step into a PID2 as a constant input signal, in the PID2, comparing the preprocessed recommended drilling speed signal with a drilling speed signal of the drilling machine in a coal bed load, outputting a torque signal output by the PID2 as a new target torque signal to the PID1, and then performing a control method which is the same as the constant torque control in the seventh step to control the drilling pressure of a feeding system of the drilling machine so as to realize the constant drilling speed control;
step nine, drilling speed fine adjustment control:
on the basis of the step eight, in the drilling speed fine adjustment module, comparing a rotating speed signal of the rotary system with a rotating speed signal in the coal bed load, and outputting a drilling speed fine adjustment quantity signal to a PID2 by the drilling speed fine adjustment module; in PID2, the drilling speed fine adjustment quantity signal and the preprocessed recommended drilling speed signal are summed, and then the sum is used as a new propelling drilling speed to be compared with the drilling speed signal in the coal bed load, and then the control method which is the same as the constant drilling speed control in the step eight is carried out to control the drilling pressure of the feeding system of the drilling machine, so that the drilling speed fine adjustment control is realized.
2. The coal rock sensing-based feed rotation control method for the hydraulic underground drill rig is characterized in that in the first step, three-level wavelet packet decomposition is adopted for wavelet packet decomposition, and a mathematical expression of a signal reconstruction function after decomposition is as follows:
S 3 ＝S 31 +S 32 +S 33 +S 34 +S 35 +S 36 +S 37 ++S 38
in the formula:
S 3 is a signal reconstruction function;
S 3j j =1,2 and … for a j frequency band signal reconstruction function after three-level wavelet packet decomposition;
energy E of reconstructed signal 3j Comprises the following steps:
E 3j ＝∫|S 3j (t)| 2 dt
in the formula:
E 3j to reconstruct the energy of the signal;
t is time;
energy E of reconstructed signal 3j And as the characteristics of each frequency band signal, performing characteristic extraction on the bit pressure and the rotation pressure to obtain a characteristic vector E which is as follows:
E＝[E 31j ,E 32j ]
in the formula:
E 31j the j frequency band signal feature vector after the bit pressure decomposition is obtained;
E 32j the j frequency band signal characteristic vector is decomposed by the revolving pressure;
j＝1，2，…8。
3. the coal rock sensing-based feed rotation control method for the hydraulic tunnel drilling machine is characterized in that in the second step, the expression of the strongly correlated characteristic vector is as follows:
E＝[E 31m ,E 32n ]
in the formula:
e' is a strongly correlated feature vector;
E′ 31m the m frequency band signal after the weight on bit decomposition is a strong correlation characteristic vector;
E′ 32n the nth frequency band signal after the revolving pressure decomposition is a strong correlation characteristic vector;
the normalized formula is as follows:
in the formula:
e' is the normalized feature vector.
4. The coal rock perception-based feed rotation control method for the hydraulic underground drill rig according to claim 1, wherein in the third step, in the coal bed hardness recognition model, the value range of the coal bed hardness is [0.5, 10], the output range of a BP neural network is [0,1], and the coal bed hardness is mapped through linear mapping; the linear mapping formula is as follows: y =9.5 x +0.5, where x is the output of the BP neural network; and y is the hardness of the coal bed.
5. The coal rock sensing-based hydraulic tunnel drilling machine feeding rotation control method as claimed in claim 1, wherein in step five, the formula of the amplitude limiting filter is as follows:
the formula of the recursive average filtering is as follows:
in the formula:
out (n) is the limiting and filtering result of the drilling speed or the rotating speed;
ErrorMax is the maximum amount of fluctuation allowed for the feed slew system.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210950365.5A CN115387777A (en) | 2022-08-09 | 2022-08-09 | Feeding and rotating control method of hydraulic tunnel drilling machine based on coal rock sensing |
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| CN117328850A (en) * | 2023-09-22 | 2024-01-02 | 安百拓(张家口)建筑矿山设备有限公司 | Drilling machine control method, device, terminal and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117328850A (en) * | 2023-09-22 | 2024-01-02 | 安百拓(张家口)建筑矿山设备有限公司 | Drilling machine control method, device, terminal and storage medium |
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