A multi-sensor fusion intelligent cutting control method for tunneling equipment

Abstract

The present application belongs to the technical field of automatic control of coal machine equipment, and aims to solve the problem that the cutting speed cannot be adjusted in real time according to the dynamic change of coal hardness in the traditional fixed cutting parameter mode, and provides a kind of intelligent cutting control method of tunneling equipment based on multi-sensor fusion, which comprises the following steps: obtaining real-time current data and real-time vibration data; obtaining the current reference value and vibration intensity reference value under the no-load working condition of the tunneling equipment, and performing normalized deviation calculation, and obtaining the load coefficient according to the normalized deviation calculation result; when the load coefficient continuously exceeds the rated load coefficient, it is determined that the target working area is hard rock working condition, and the descending speed and advancing speed of the cutting arm of the tunneling equipment are controlled to be reduced in combination with the speed regulation factor. The present application improves the accuracy of intelligent cutting control of tunneling equipment, can dynamically adjust the descending speed and advancing speed of the cutting arm according to the working condition of the target working area, and greatly improves the service life of the tunneling equipment.

Description

Technical Field

[0001] This invention belongs to the field of automatic control technology for coal mining equipment, specifically relating to an intelligent cutting control method for tunneling equipment that integrates multiple sensors. Background Technology

[0002] In underground coal mines, the geological properties of coal and rock, such as hardness and structure, change frequently and are difficult to predict. While widely used horizontal shaft tunneling equipment offers advantages such as high cutting power and efficient roadway formation, its cutting motors typically employ non-frequency drive, operating at a constant speed for extended periods. This fixed-parameter mode has significant drawbacks: 1. Poor adaptability and inability to respond dynamically: The equipment cannot dynamically adjust the cutting speed according to real-time changes in coal and rock hardness; 2. High risk in hard rock conditions: When encountering semi-coal-rock or full-rock strata, the cutting resistance increases sharply, easily leading to overload or even burnout of the cutting motor; 3. Wasted efficiency in soft coal conditions: In softer conditions such as pure coal, the inability to actively increase speed results in underutilized production capacity and wasted resources.

[0003] To overcome the limitations of traditional fixed rotation speed and more accurately match instantaneous load and cutting power, it is necessary to study an intelligent cutting control method for tunneling equipment that can simultaneously control the descent speed (affecting the cutting depth) and forward speed (affecting the feed rate) of the cutting arm. This method can effectively avoid overloading in hard rock and maximize tunneling efficiency in soft coal conditions, significantly improving the adaptability and overall performance of tunneling equipment under complex geological conditions. Summary of the Invention

[0004] In order to solve at least one of the above-mentioned technical problems in the prior art, the present invention provides a multi-sensor fusion intelligent cutting control method for tunneling equipment.

[0005] This invention is achieved using the following technical solution: a multi-sensor fusion-based intelligent cutting control method for tunneling equipment, comprising the following steps:

[0006] The cutting current data and vibration acceleration data of the tunneling equipment during cutting are acquired, and the cutting current data and vibration acceleration data are filtered to obtain real-time current data and real-time vibration data.

[0007] Obtain the current reference value and vibration intensity reference value of the tunneling equipment under no-load conditions. Based on the real-time current data and the current reference value, as well as the real-time vibration data and the vibration intensity reference value, perform normalized deviation calculations respectively. Then, perform weighted fusion based on the normalized deviation calculation results to obtain the load coefficient.

[0008] The speed regulation range is set according to the rated load factor and the load factor, and the speed regulation factor is set according to the corresponding speed regulation range. When the load factor continuously exceeds the rated load factor, the target working area is determined to be a hard rock working condition and is in the deceleration range. The descent speed and forward speed of the cutting arm of the tunneling equipment are controlled to be reduced according to the speed regulation factor in the deceleration range. When the load factor continuously falls below the rated load factor, the target working area is determined to be a soft coal working condition and is in the acceleration range. The descent speed and forward speed of the cutting arm of the tunneling equipment are controlled to be increased according to the speed regulation factor in the acceleration range.

[0009] Preferably, after obtaining the reference values ​​of current and vibration intensity under no-load conditions of the tunneling equipment, the process further includes:

[0010] Obtain the average current value within a preset sliding window, and update the current reference value according to a preset current weighting smoothing coefficient and the average current value;

[0011] Obtain the average vibration intensity within a preset sliding window, and update the vibration intensity benchmark value based on a preset vibration weight smoothing coefficient and the average vibration intensity.

[0012] Preferably, the load coefficient is obtained by weighted fusion based on the normalized deviation calculation results, including:

[0013] The current deviation value is obtained based on real-time current data and current reference value;

[0014] The vibration deviation value is obtained based on real-time vibration data and vibration intensity benchmark value;

[0015] The current deviation value and the vibration deviation value are weighted and fused according to a preset weighting coefficient to obtain the load coefficient.

[0016] Preferably, controlling and reducing the descent speed and forward speed of the cutting arm of the tunneling equipment includes:

[0017] When the load factor continuously exceeds the rated load factor, the speed regulation factor is set to a deceleration factor;

[0018] The real-time descent speed and real-time forward speed of the cutting arm of the tunneling equipment are obtained, and the real-time descent speed and real-time forward speed are reduced to a first target descent speed and a first target forward speed according to the deceleration factor.

[0019] Preferably, controlling the descent speed and forward speed of the hoisting tunneling equipment's cutting arm includes:

[0020] When the load factor is consistently lower than the rated load factor, the speed regulation factor is set to the acceleration factor;

[0021] The real-time descent speed and real-time forward speed of the cutting arm of the tunneling equipment are obtained, and the real-time descent speed and real-time forward speed are increased to a second target descent speed and a second target forward speed according to the acceleration factor.

[0022] Preferably, it further includes:

[0023] A linear ramp approximation method is adopted to control the real-time descent speed and real-time forward speed at a preset rate to approach the target descent speed and target forward speed.

[0024] Preferably, it further includes:

[0025] When the cutting current data continuously exceeds the rated cutting current, an alarm message is generated, and the descent speed and forward speed of the cutting arm of the tunneling equipment are reduced until the tunneling equipment stops.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] This invention uses dual-source fusion weighted judgment based on cutting current data and vibration acceleration data to avoid misjudgments of coal and rock properties caused by relying on a single signal. At the same time, through dynamic benchmark value updates, it effectively solves the problems of slow rise in current benchmark value caused by aging of tunneling equipment and sensor drift caused by environmental influences, thus improving the accuracy of intelligent cutting control of tunneling equipment. Furthermore, it can dynamically adjust the descent and forward speeds of the cutting arm according to the working conditions of the target working area, which protects equipment safety and improves construction efficiency. In addition, the linear slope approximation method is used to uniformly approximate the target descent and forward speeds, eliminating hydraulic impact peak stress and greatly extending the service life of the tunneling equipment. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating an intelligent cutting control method for tunneling equipment based on multi-sensor fusion, provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should fall within the scope of the technical content disclosed in the present invention. It should be noted that in this specification, relational terms such as "first" and "second" are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0032] Traditional cutting methods typically employ fixed or manually adjusted cutting parameters, failing to dynamically optimize based on real-time changes in coal and rock conditions. This leads to problems such as low cutting efficiency, excessive equipment energy consumption, severe cutter wear, and even equipment damage when facing complex geological conditions. Intelligent cutting technology, by comprehensively utilizing current and vibration sensors, monitors cutting current and vibration acceleration data in real time. Based on a condition assessment mechanism, it dynamically determines whether rock has been encountered in the target working area and promptly adjusts the descent and forward speeds of the cutting arm to protect the tunneling equipment.

[0033] like Figure 1 As shown in the figure, this embodiment of the invention provides a flowchart of an intelligent cutting control method for tunneling equipment based on multi-sensor fusion, which includes the following steps:

[0034] The cutting current data and vibration acceleration data of the tunneling equipment during cutting are acquired, and the cutting current data and vibration acceleration data are filtered to obtain real-time current data and real-time vibration data.

[0035] In this embodiment, to monitor the vibration state of the tunneling equipment, a vibration sensor is installed on the cutting arm of the tunneling equipment to collect vibration acceleration data. This vibration acceleration data reflects the impact intensity between the cutting head of the tunneling equipment and the target working area. The sampling frequency of the vibration sensor... The sampling frequency should be selected based on the frequency characteristics of the vibration signal to ensure that changes in the vibration signal can be captured. In practical applications, the sampling frequency is generally set to... This is to ensure that changes in vibration signals can be detected in a timely manner in underground tunnels.

[0036] In this embodiment, current sensors are installed on the power supply lines of the left and right cutting motors of the tunneling equipment to collect cutting current data. This cutting current data directly reflects the load status of the corresponding cutting motor. The left cutting current is set as... The right-hand cutting current is The average of the left and right cutting currents is taken as the input value of the cutting current data for the tunneling equipment, which is... In practical applications, the sampling frequency of the current sensor... It can be set to the same sampling frequency as the vibration sensor to ensure synchronous data acquisition and avoid errors in subsequent calculations due to asynchronous data, which could lead to incorrect judgment of working conditions.

[0037] In this embodiment, the sensor data collected by sensors in underground roadways typically contains high-frequency noise (such as electromagnetic interference and mechanical vibration). Directly using the raw sensor data may lead to misjudgment. Therefore, the collected cutting current data and vibration acceleration data are filtered using a Butterworth low-pass filter to remove noise interference. When using a Butterworth low-pass filter to filter the vibration acceleration data, its cutoff frequency can be set to... When using a Butterworth low-pass filter to filter cutoff current data, its cutoff frequency can be set to... .

[0038] Obtain the current reference value and vibration intensity reference value of the tunneling equipment under no-load conditions. Based on the real-time current data and the current reference value, as well as the real-time vibration data and the vibration intensity reference value, perform normalized deviation calculations respectively. Then, perform weighted fusion based on the normalized deviation calculation results to obtain the load coefficient.

[0039] Optionally, after obtaining the current reference value and vibration intensity reference value under the no-load condition of the tunneling equipment, the method further includes: obtaining the average current value within a preset sliding window, and updating the current reference value according to a preset current weighting smoothing coefficient and the average current value; obtaining the average vibration intensity value within a preset sliding window, and updating the vibration intensity reference value according to a preset vibration weighting smoothing coefficient and the average vibration intensity value.

[0040] In this embodiment, the current reference value is the reference value of the tunneling equipment under no-load conditions of the left and right cutting motors. When the cutting motors age, the no-load current tends to increase slowly. At the same time, with changes in the external environment (such as temperature changes), the zero point of the vibration sensor will also shift. To enable the method of this invention to adapt to situations such as performance degradation of the tunneling equipment or environmental changes, a weighted average method is used to dynamically update the current reference value and vibration intensity reference value each time the tunneling equipment is started and the cutting motor is under no-load conditions, so that the corresponding baseline values ​​gradually converge to the current and vibration frequency under no-load conditions.

[0041] In this embodiment, the formula for dynamically updating the current reference value is:

[0042]

[0043] In the formula, This is a preset current weighting smoothing coefficient, used to control the weight of the current reference value before it is updated; The preset sliding window size is set to the average current value within the preset sliding window. ; The current reference value is the one before it was updated; This is the updated current reference value.

[0044] The formula for dynamically updating the average vibration intensity is:

[0045]

[0046] In the formula, The preset vibration weighting smoothing coefficient has a value range of [value range missing]. , used to control the weight of the vibration intensity benchmark value before it was updated; The average vibration intensity within a preset sliding window is set to the preset sliding window size. ; This represents the average vibration intensity before the update. This is the updated vibration intensity benchmark value.

[0047] Optionally, the load coefficient is obtained by weighted fusion based on the normalized deviation calculation results, including: obtaining the current deviation value based on real-time current data and current reference value; obtaining the vibration deviation value based on real-time vibration data and vibration intensity reference value; and weighting and fusing the current deviation value and the vibration deviation value according to a preset weighting coefficient to obtain the load coefficient.

[0048] In this embodiment, the formula for calculating the current deviation value is:

[0049]

[0050] In the formula, Represents real-time current data; This indicates the current deviation value, used to measure the load level of the cutting motor.

[0051] The formula for calculating the vibration deviation value is:

[0052]

[0053] In the formula, Represents real-time vibration data; This represents the vibration deviation value, used to measure the load level of the cutting motor.

[0054] The formula for calculating the load factor is:

[0055]

[0056] In the formula, This represents a preset weighting coefficient used to fuse current deviation values ​​and vibration deviation values. This indicates the load factor.

[0057] The speed regulation range is set according to the rated load factor and the load factor, and the speed regulation factor is set according to the corresponding speed regulation range. When the load factor continuously exceeds the rated load factor, the target working area is determined to be a hard rock working condition and is in the deceleration range. The descent speed and forward speed of the cutting arm of the tunneling equipment are controlled to be reduced according to the speed regulation factor in the deceleration range. When the load factor continuously falls below the rated load factor, the target working area is determined to be a soft coal working condition and is in the acceleration range. The descent speed and forward speed of the cutting arm of the tunneling equipment are controlled to be increased according to the speed regulation factor in the acceleration range.

[0058] Optionally, controlling the reduction of the descent speed and forward speed of the tunneling equipment cutting arm includes: setting a deceleration factor when the load factor continuously exceeds the rated load factor; acquiring the real-time descent speed and real-time forward speed of the tunneling equipment cutting arm; and reducing the real-time descent speed and real-time forward speed to a first target descent speed and a first target forward speed according to the deceleration factor.

[0059] Optionally, controlling the descent speed and forward speed of the cutting arm of the tunneling equipment includes: setting an acceleration factor when the load factor is continuously lower than the rated load factor; obtaining the real-time descent speed and real-time forward speed of the cutting arm of the tunneling equipment; and increasing the real-time descent speed and real-time forward speed to a second target descent speed and a second target forward speed according to the acceleration factor.

[0060] In this embodiment, the rated load factor of the tunneling equipment operating under rated power conditions is obtained. The speed regulation factor is set according to the following formula:

[0061]

[0062] In the formula, The first acceleration factor; The second acceleration factor; This is the first deceleration factor; This is the second deceleration factor.

[0063] In this embodiment, when the load factor Greater than the rated load factor When this occurs, it indicates that the cutting head of the tunneling equipment is in hard rock conditions. At this time, the descent speed and forward speed of the cutting arm of the tunneling equipment should be reduced immediately to reduce the unit advance, alleviate the load on the cutting motor, and protect the tunneling equipment.

[0064] When the load factor Less than the rated load factor When the cutting head of the tunneling equipment is in soft coal conditions, the descent speed and forward speed of the cutting arm should be increased accordingly to increase the unit advance, fully release the production capacity, and improve the overall cutting efficiency.

[0065] In this embodiment, when the load factor Greater than the rated load factor At time, or load factor Less than the rated load factor At that time, and the duration exceeded At any given time, the corresponding target descent velocity and target forward velocity are calculated using the following formulas:

[0066]

[0067] In the formula, This indicates the corresponding target velocity value; This indicates the corresponding real-time speed value; This represents the corresponding speed regulation factor, when When this time, it indicates that the tunneling equipment is in the first acceleration zone, and the corresponding value is [value missing]. ,when When this time, it indicates that the tunneling equipment is in the second acceleration zone, and the corresponding value is [value missing]. ,when When this time, it indicates that the tunneling equipment is in the first deceleration range, and the corresponding value is [value missing]. ,when When this time, it indicates that the tunneling equipment is in the second deceleration range, and the corresponding value is [value missing]. .

[0068] Optionally, it also includes: using a linear ramp approximation method to control the real-time descent speed and real-time forward speed at a preset rate to approximate the target descent speed and target forward speed.

[0069] In this embodiment, the formula for linear slope approximation is:

[0070]

[0071] In the formula, Preset rate; It has a fixed time step.

[0072] This invention utilizes vibration and current sensors to acquire real-time vibration acceleration data of the cutting head of the tunneling equipment and cutting current data of the cutting motor. To suppress baseline drift caused by interference from factors such as temperature drift, aging, or environmental factors, a sliding window baseline update strategy is adopted. Within a certain period, a current reference value is calculated based on the cutting current data and vibration acceleration data, respectively. and vibration intensity benchmark value This information is continuously updated based on operating conditions. Within each control cycle, the current deviation value is first calculated. Vibration deviation value The two were then normalized and weighted to synthesize the motor load factor. .when Less than the rated load factor If this condition persists for more than 500ms, it is determined to be a soft coal condition, and the descent and forward speeds of the tunneling equipment's cutting arm are automatically increased to improve cutting efficiency; when Greater than the rated load factor If the condition persists for more than 500ms, it is determined to be a hard rock working condition. Immediately reduce the descent speed and forward speed of the cutting arm of the tunneling equipment to reduce the load on the cutting motor and prevent overload damage.

[0073] Optionally, it also includes: when the cutting current data continuously exceeds the rated cutting current, generating an alarm message, and simultaneously controlling the reduction of the descent speed and forward speed of the cutting arm of the tunneling equipment until the tunneling equipment stops.

[0074] In this embodiment, if the hard rock condition is determined, the descent speed and forward speed of the cutting arm of the tunneling equipment are immediately reduced. At the same time, if the cutting current data exceeds the rated cutting current for a long time, an emergency shutdown is triggered to protect the cutting motor.

[0075] This invention achieves real-time detection of rock contact and dynamic adjustment of the descent and forward speeds of the cutting arm in tunneling equipment through joint analysis of cutting current data and vibration acceleration data, balancing efficiency and equipment protection. Key advantages include: reduced false judgments due to the combination of current and vibration data, avoiding misjudgments that may occur if relying solely on current (e.g., due to motor failure or instantaneous load fluctuations), while vibration acceleration data reflects the physical contact strength between the cutting head and the rock in the target working area; the combination of these two factors improves accuracy. It also adapts to environmental changes, effectively solving the problems of slow rise in reference current due to equipment aging and sensor drift caused by environmental influences. Furthermore, it avoids mechanical shocks caused by sudden speed changes. When cutting conditions change, different descent and forward speeds of the cutting arm are matched according to different load coefficients. From the current speed to the target speed, a linear ramp strategy is used to gradually reduce the speed, effectively avoiding hydraulic shocks and mechanical wear caused by sudden speed changes.

[0076] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent cutting control of tunneling equipment using multi-sensor fusion, characterized in that, Includes the following steps: The cutting current data and vibration acceleration data of the tunneling equipment during cutting are acquired, and the cutting current data and vibration acceleration data are filtered to obtain real-time current data and real-time vibration data. Obtain the current reference value and vibration intensity reference value of the tunneling equipment under no-load conditions. Based on the real-time current data and the current reference value, as well as the real-time vibration data and the vibration intensity reference value, perform normalized deviation calculations respectively. Then, perform weighted fusion based on the normalized deviation calculation results to obtain the load coefficient. After obtaining the reference values ​​of current and vibration intensity under no-load conditions for the tunneling equipment, the following is also included: Obtain the average current value within a preset sliding window, and update the current reference value according to a preset current weighting smoothing coefficient and the average current value; Obtain the average vibration intensity within a preset sliding window, and update the vibration intensity baseline value based on a preset vibration weight smoothing coefficient and the average vibration intensity. The load coefficient is obtained by weighting and fusion based on the normalized bias calculation results, including: The current deviation value is obtained based on real-time current data and current reference value; The vibration deviation value is obtained based on real-time vibration data and vibration intensity benchmark value; The current deviation value and the vibration deviation value are weighted and fused according to a preset weighting coefficient to obtain the load coefficient; The speed regulation range is set according to the rated load factor and the load factor, and the speed regulation factor is set according to the corresponding speed regulation range. When the load factor continuously exceeds the rated load factor, the target working area is determined to be a hard rock working condition and is in the deceleration range. The descent speed and forward speed of the cutting arm of the tunneling equipment are controlled to be reduced according to the speed regulation factor in the deceleration range. When the load factor continuously falls below the rated load factor, the target working area is determined to be a soft coal working condition and is in the acceleration range. The descent speed and forward speed of the cutting arm of the tunneling equipment are controlled to be increased according to the speed regulation factor in the acceleration range.

2. The intelligent cutting control method for tunneling equipment based on multi-sensor fusion according to claim 1, characterized in that, Controlling and reducing the descent and forward speeds of the tunneling equipment's cutting arm includes: When the load factor continuously exceeds the rated load factor, the speed regulation factor is set as the deceleration factor; The real-time descent speed and real-time forward speed of the cutting arm of the tunneling equipment are obtained, and the real-time descent speed and real-time forward speed are reduced to a first target descent speed and a first target forward speed according to the deceleration factor.

3. The intelligent cutting control method for tunneling equipment based on multi-sensor fusion according to claim 1, characterized in that, Controlling the descent and forward speeds of the hoisting tunneling equipment's cutting arm includes: When the load factor is consistently lower than the rated load factor, the speed regulation factor is set to the acceleration factor; The real-time descent speed and real-time forward speed of the cutting arm of the tunneling equipment are obtained, and the real-time descent speed and real-time forward speed are increased to a second target descent speed and a second target forward speed according to the acceleration factor.

4. The intelligent cutting control method for tunneling equipment based on multi-sensor fusion according to any one of claims 2 or 3, characterized in that, Also includes: A linear ramp approximation method is adopted to control the real-time descent speed and real-time forward speed at a preset rate to approach the target descent speed and target forward speed.

5. The intelligent cutting control method for tunneling equipment based on multi-sensor fusion according to claim 1, characterized in that, Also includes: When the cutting current data continuously exceeds the rated cutting current, an alarm message is generated, and the descent speed and forward speed of the cutting arm of the tunneling equipment are reduced until the tunneling equipment stops.

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