A method for testing the digging resistance of an excavator

Abstract

The application provides a kind of excavator digging resistance test method, comprising: step 1, using simulation digging device to collect boom cylinder displacement, bucket rod cylinder displacement, bucket cylinder displacement, bucket hinge pin stress data and actual digging resistance data in the process of digging;Step 2, establish the kinematics model of excavator working device and the dynamics model of bucket as mechanism model, calculate the theoretical calculation value of digging resistance and difference with actual digging resistance data to obtain estimated error data;Step 3, according to the collected data, the relationship between the relevant input data and digging resistance estimated error data is fitted using machine learning algorithm, and the error estimation model is obtained;Step 4, combine mechanism model and error estimation model to obtain digging resistance soft measurement model;Step 5, use soft measurement model to measure digging resistance in actual digging operation;The application can more accurately obtain the digging resistance in the process of excavator digging operation.

Description

Technical Field

[0001] This application relates to the technical field of construction machinery, and in particular to a method for testing the digging resistance of an excavator. Background Technology

[0002] Excavators are flexible and efficient construction machines widely used in mining, construction sites, and other construction scenarios. Accurately sensing the digging resistance during excavator operation is one of the key technologies for achieving excavator structural optimization and energy-saving control. In terms of structural optimization design, accurately acquiring the loads experienced by the excavator during digging is the foundation for modal analysis and topology optimization. Regarding trajectory optimization and energy-saving control, by acquiring the digging resistance, the excavator's digging trajectory can be optimized to find a trajectory with minimal digging resistance, achieving energy savings and reduced consumption. Furthermore, digging resistance is a significant factor affecting trajectory tracking control; sensing digging resistance and implementing feedback control helps improve control accuracy. In conclusion, accurately and conveniently acquiring digging resistance lays the foundation for achieving excavator automation.

[0003] However, due to the limitations of the excavator's structure and the complexity and uncertainty of the digging process, it is impossible to measure digging resistance by directly installing sensors on the bucket. The digging resistance can only be calculated by measuring other easily measurable variables—a soft measurement method. Chinese invention patent CN114878045A calculates digging resistance using data from a series of sensors, including a rangefinder, torque sensor, and force sensor. However, this method involves complex theoretical calculations and sensor installation, and the constructed soft measurement model often lacks accuracy due to various assumptions. Chinese invention patent CN114264400A designs a digging resistance measuring device, which does not require complex theoretical derivation, but it is only suitable for bench tests and cannot be applied to practical engineering. Summary of the Invention

[0004] In response to the above practical problems and the shortcomings of existing technologies, this invention proposes a reliable and accurate method for measuring the digging resistance of excavators, based on the kinematics and dynamics of the excavator's working device and combined with a data-driven modeling method.

[0005] To solve the above-mentioned technical problems, this application provides a method for testing the digging resistance of an excavator, adopting the following technical solution:

[0006] A method for testing the digging resistance of an excavator includes a data acquisition module, a mechanism model, an error estimation model, and a soft measurement model for digging resistance.

[0007] The testing method includes the following steps:

[0008] Step 1: Use the data acquisition module to collect data on the displacement of the boom cylinder, stick cylinder, and bucket cylinder during the simulated excavation operation of the excavator, as well as the force data of the pin at the bucket hinge and the actual digging resistance data of the bucket tip.

[0009] Step 2: Calculate the theoretical value of excavation resistance using the data collected in Step 1 combined with the mechanism model, and subtract the actual excavation resistance data from the theoretical value to obtain the excavation resistance error data.

[0010] The mechanism model calculation includes calculating the bucket acceleration and attitude angle using the kinematic model of the excavator's working device, and calculating the theoretical value of the digging resistance using the bucket dynamics model.

[0011] Step 3: Using the data obtained in Step 1 and Step 2 as a dataset, construct a model for estimating resistance error using machine learning regression algorithms;

[0012] Step 4: Combine the mechanism model and the error estimation model to measure the resistance and obtain the soft measurement model of excavation resistance;

[0013] Step 5: Measure the excavation resistance using a soft measurement model for excavation resistance.

[0014] In a preferred embodiment, the data acquisition module includes a wire displacement sensor, a pin sensor, a tension sensor, and a device for simulating excavation resistance;

[0015] In step 1, the displacement of the boom cylinder, stick cylinder, and bucket cylinder during the simulated excavation operation is obtained using the pull-wire displacement sensor; the force data of the pin at the bucket hinge is obtained using the pin sensor; and the actual excavation resistance data of the bucket tip is obtained using the tension sensor and the simulated excavation resistance device.

[0016] In a preferred embodiment, the simulated excavation resistance device includes a load, a rope, and an excavator body, wherein the tension sensor is connected to the bucket tip of the excavator body and the load via the rope.

[0017] The actual digging resistance at the bucket tip is obtained by using the tension sensor and the simulated digging resistance device, which includes tangential force, normal force, and torque.

[0018] In a preferred embodiment, the wire-type displacement sensor includes a first displacement sensor, a second displacement sensor, and a third displacement sensor, which are correspondingly disposed at the positions of the bucket cylinder, the stick cylinder, and the boom cylinder.

[0019] In a preferred embodiment, the pin sensor is fixedly connected to the bucket of the excavator body. The pin sensor includes a first pin sensor and a second pin sensor, which are used to obtain the magnitude of the force at the bucket hinge along the x and y directions of its coordinate system, respectively.

[0020] In a preferred embodiment, in step 2, the kinematic model calculation of the excavator working device specifically involves: establishing a kinematic model of the excavator working device, and combining geometry to calculate the bucket acceleration and attitude angle based on the displacement of the boom cylinder, stick cylinder, and bucket cylinder;

[0021] The calculation using the bucket dynamics model specifically involves: performing dynamic analysis on the bucket to obtain the bucket dynamics model, and calculating the theoretical value of the digging resistance based on the obtained force data of the pin at the bucket hinge, as well as the bucket acceleration and attitude angle.

[0022] In a preferred embodiment, in step 3, the data on the displacement of the boom cylinder, the displacement of the stick cylinder, the displacement of the bucket cylinder, the force data of the pin at the bucket hinge, as well as the bucket attitude angle and acceleration are used as inputs, and the data on the excavation resistance error are used as outputs to construct dataset D;

[0023] The dataset is divided into a training set and a validation set according to a certain ratio. On the training set, a machine learning algorithm is used to fit the relationship between the input and the output. The performance of the machine learning model is analyzed through the validation set, thereby obtaining the optimal model for estimating the mining resistance error.

[0024] In a preferred embodiment, in step 4, the estimated error of the excavation resistance is obtained using the excavation resistance error estimation model, and the final estimated value of the excavation resistance is obtained by adding the theoretically calculated value to the estimated error.

[0025] In a preferred embodiment, in step 4, the mechanistic model is used to evaluate the numerical values ​​in the soft measurement model of excavation resistance;

[0026] The error estimation model is used in the soft measurement model of excavation resistance to correct the evaluated data.

[0027] In a preferred embodiment, in step 5, the displacement of the boom cylinder, the displacement of the stick cylinder, the displacement of the bucket cylinder, and the force data of the pin at the bucket hinge during the actual excavation operation are obtained and input into the soft measurement model of excavation resistance, thereby estimating the excavation resistance.

[0028] In summary, this application has the following beneficial effects:

[0029] In this invention, the kinematic model of the excavator's working device and the dynamic model of the bucket are combined as a mechanistic model to calculate the initial estimate of the digging resistance. Excavator-related data and the output data of the mechanistic model are collected to construct training data. A machine learning algorithm is used to fit the relationship between the input data and the digging resistance error value as an error estimation model to evaluate the estimation error of the digging resistance. Furthermore, the mechanistic model and the error estimation model are combined to form a soft measurement model for digging resistance, thereby measuring the digging resistance during the excavator's digging operation. This helps to achieve real-time adjustment of the digging trajectory based on the digging resistance, thus realizing energy saving and consumption reduction, high-precision trajectory tracking control of the excavator, improving the excavator's operating efficiency, and promoting the intelligent and automated development of excavators. Attached Figure Description

[0030] Figure 1 This is a flowchart of the excavator digging resistance test method in this embodiment;

[0031] Figure 2 This is a schematic diagram of the simulated excavation device in this embodiment.

[0032] Explanation of reference numerals in the attached diagram: 1. Loaded object; 2. Pull rope; 3. Tension sensor; 4. First pin sensor; 5. Second pin sensor; 6. First displacement sensor; 7. Second displacement sensor; 8. Third displacement sensor; 9. Excavation trajectory. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0034] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0035] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed", "equipped", "sleeved / connected", "connected", etc., should be interpreted broadly. For example, "connection" can be a wall-mounted connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0036] The following is in conjunction with the appendix Figure 1 -2 provides further details regarding this application.

[0037] This application discloses a method for testing the digging resistance of an excavator, comprising: Step 1, using a simulated digging device to collect data on the displacement of the boom cylinder, stick cylinder, and bucket cylinder, the force on the pin at the bucket hinge, and the actual digging resistance during the digging process; Step 2, establishing a kinematic model of the excavator's working device and a dynamic model of the bucket as a mechanistic model, using the collected data to calculate the theoretical value of the digging resistance as an initial estimate, and subtracting the initial estimate from the actual digging resistance data to obtain the estimation error data; Step 3, using a machine learning algorithm to fit the relationship between the relevant input data and the digging resistance estimation error data based on the collected data to obtain an error estimation model; Step 4, combining the mechanistic model and the error estimation model to obtain a soft measurement model for digging resistance, wherein the mechanistic model outputs the initial estimate of the digging resistance, and the error estimation model corrects the initial estimate; Step 5, using the soft measurement model to measure the digging resistance in actual digging operations; This invention can accurately obtain the digging resistance during the excavator's digging operation, providing a foundation for achieving energy saving, consumption reduction, and intelligentization of excavators.

[0038] In this embodiment, a method for measuring the digging resistance of an excavator is provided, including a data acquisition module, a mechanism model, an error estimation model, and a soft measurement model for digging resistance.

[0039] The data acquisition module includes a wire-type displacement sensor, a pin sensor, a tension sensor, and a simulated digging resistance device. The wire-type displacement sensor collects displacement data of each cylinder of the excavator's working device, the pin sensor is used to obtain force data at the bucket hinge, and the tension sensor and the simulated digging resistance device are used to obtain actual digging resistance data.

[0040] The mechanism model, composed of the kinematic model of the excavator's working device and the dynamic model of the bucket, is used to calculate the initial estimate of the digging resistance. The bucket acceleration and attitude angle calculated by the kinematic model provide the input data for the error estimation model. The output data of the mechanism model and the actual digging resistance data are used to obtain the digging resistance error data, which provides the output data for the error estimation model.

[0041] The error estimation model uses machine learning algorithms to fit the relationship between input data such as cylinder displacement, force at bucket hinge, bucket acceleration, and bucket attitude angle and digging resistance error data, thus obtaining the digging resistance error estimation model to evaluate the error of the current excavator resistance estimate.

[0042] The soft measurement model for excavation resistance combines a mechanistic model and an error estimation model. The mechanistic model serves as the evaluation value to output the initial estimate of excavation resistance, while the error estimation model acts as a corrector to correct the initial estimate of excavation resistance, thereby outputting the final estimate of excavation resistance.

[0043] See Figure 1 A method for measuring the digging resistance of an excavator, comprising the following steps:

[0044] Step 1: Use the data acquisition module to collect data on the displacement of the boom cylinder, stick cylinder, and bucket cylinder during the simulated excavation operation of the excavator, as well as the force data of the pin at the bucket hinge and the actual digging resistance data of the bucket tip.

[0045] Specifically, the acquisition module includes a wire-type displacement sensor, a pin sensor, a tension sensor, and a simulated digging resistance device. The wire-type displacement sensor acquires displacement data of each cylinder of the excavator's working device, the pin sensor is used to obtain force data at the bucket hinge, and the tension sensor and the simulated digging resistance device are used to obtain actual digging resistance data.

[0046] See the simulation excavation resistance device. Figure 2 It consists of a load 1, a pull rope 2, a tension sensor 3, a first pin sensor 4, a second pin sensor 5, a first displacement sensor 6, a second displacement sensor 7, a third displacement sensor 8, and an excavator body 10. The tension sensor 3 is connected to the excavator bucket tip and the load 1 respectively through the pull rope 2. The load and the excavator are placed on the same horizontal plane.

[0047] The first displacement sensor 6, the second displacement sensor 7, and the third displacement sensor 8 are respectively installed at the positions of the bucket cylinder, the stick cylinder, and the boom cylinder, and are used to acquire the displacement D of the bucket cylinder during the simulated excavation operation. Bucket , boom cylinder displacement D Stick Boom cylinder displacement D Boom .

[0048] The pin sensor is fixedly connected to the bucket of the excavator body. The pin sensor includes a first pin sensor and a second pin sensor, which are used to obtain the magnitude F of the force on the M pin at the bucket hinge along the x and y directions of the pin coordinate system, respectively. Mx F My The magnitude of the forces acting on the O3 pin at the bucket hinge along the x and y directions of the pin coordinate system.

[0049] Specifically, the tensile sensor and the simulated digging resistance device are used to obtain the actual digging resistance at the tip of the bucket and decompose it into tangential force. Normal force torque

[0050] During the simulated excavation process, the excavator simulates the excavation trajectory 9, thereby dragging the heavy load. The resulting tension is recorded by the tension sensor as the actual excavation resistance, and is decomposed into tangential force along the bucket coordinate system. Normal force torque

[0051] Step 2: Calculate the theoretical value of excavation resistance using the data collected in Step 1 combined with the mechanism model, and subtract the actual excavation resistance data from the theoretical value to obtain the excavation resistance error data.

[0052] The mechanism model calculation includes calculating the bucket acceleration and attitude angle using the kinematic model of the excavator's working device, and calculating the theoretical value of digging resistance using the bucket dynamics model. Specifically, the calculation using the excavator's working device kinematic model involves: establishing a kinematic model of the excavator's working device, and combining geometry to calculate the bucket acceleration and attitude angle based on the displacement of the boom cylinder, stick cylinder, and bucket cylinder. The calculation using the bucket dynamics model involves: performing dynamic analysis on the bucket to obtain the bucket dynamics model, and calculating the theoretical value of digging resistance based on the obtained force data of the pin at the bucket hinge, as well as the bucket acceleration and attitude angle.

[0053] In step 2, the bucket acceleration and attitude angle are calculated based on the collected cylinder displacement data and the kinematic model of the working device. These, along with the bucket pin force data, are input into the bucket dynamics model to calculate the theoretical value of the digging resistance. The kinematic model of the working device and the bucket dynamics model together form a mechanism model. The difference between the theoretical calculation value obtained from the mechanism model and the actual digging resistance data is used to obtain the digging resistance error data. Specifically:

[0054] A kinematic model of the excavator's working device is established based on the DH coordinate system, and the bucket acceleration a and attitude angle ξ are calculated by combining geometry based on the displacement of the boom cylinder, stick cylinder, and bucket cylinder.

[0055] A dynamic model of the bucket is obtained by performing dynamic analysis on the bucket. Based on the obtained force data of the pivot pin at the bucket hinge, as well as the bucket attitude angle and acceleration, the theoretical value of the digging resistance is calculated in combination with the bucket dynamic model.

[0056] Among them, parameters such as bucket weight, center of gravity position, and moment of inertia are calculated by CAD software.

[0057] The obtained theoretical calculation value of excavation resistance Compared with actual excavation resistance The difference is calculated to obtain the excavation resistance error data.

[0058] The kinematic model of the working device and the dynamic model of the bucket together form the mechanism model. Its inputs are the displacement of the boom cylinder, the displacement of the stick cylinder, the displacement of the bucket cylinder, and the force data of the pin at the bucket hinge. The output is the theoretical calculation value of the digging resistance.

[0059] Step 3: Using the data obtained in Step 1 and Step 2 as a dataset, construct a model for estimating resistance error using machine learning regression algorithms;

[0060] Specifically, the following steps are taken: The data on boom cylinder displacement, stick cylinder displacement, bucket cylinder displacement, force on the pin at the bucket hinge, bucket attitude angle, and acceleration are taken as inputs, and the data on excavation resistance error are taken as outputs to construct dataset D.

[0061]

[0062] The dataset is divided into a training set and a validation set according to a certain ratio. On the training set, a machine learning algorithm is used to fit the relationship between the input and the output. The performance of the machine learning model is analyzed through the validation set, thereby obtaining the optimal model for estimating the mining resistance error.

[0063] Step 4: Combine the mechanism model and the error estimation model to measure the resistance and obtain the soft measurement model of excavation resistance;

[0064] Specifically, the following steps are taken: The mechanism model described in step 2 and the excavation resistance error estimation model described in step 3 are combined to form a soft measurement model for excavation resistance. The theoretical excavation resistance, i.e., the initial estimate of the excavation resistance, is calculated by the mechanism model. The estimation error of the excavation resistance is obtained by the excavation resistance error estimation model. The initial estimate of the excavation resistance and the estimation error are added together to obtain the final estimate of the excavation resistance.

[0065] In this model, the mechanistic model acts as the evaluator in the overall soft measurement model of excavation resistance, while the error estimation model acts as the corrector to correct the evaluator's initial estimates.

[0066] Step 5: Measure the excavation resistance using a soft measurement model for excavation resistance.

[0067] Specifically, the following steps are taken: Obtain data on the displacement of the boom cylinder, stick cylinder, bucket cylinder, and the force on the pin at the bucket hinge during the actual excavation operation, and input these data into the soft measurement model for excavation resistance to estimate the excavation resistance.

[0068] Among them, the mechanism model input in the soft measurement model of excavation resistance is the displacement of boom cylinder, stick cylinder, bucket cylinder, and the force data of the pin at the bucket hinge.

[0069] The displacements of the boom cylinder, stick cylinder, and bucket cylinder are input into the kinematic model of the mechanism model to calculate the bucket acceleration and attitude angle. Then, these, along with the pin force data, are input into the dynamic model to calculate the initial estimate of the digging resistance.

[0070] The error estimation model takes the displacement of the boom cylinder, the displacement of the stick cylinder, the displacement of the bucket cylinder, the force on the pin at the bucket articulation, and the bucket acceleration and attitude angle data as inputs to calculate the estimated error value of the digging resistance. The initial estimate of the mechanistic model is added to the estimation error to obtain the final estimate of the excavation resistance.

[0071]

[0072] Among them, the error estimation model input in the soft measurement model of excavation resistance includes not only the displacement of boom cylinder, stick cylinder, bucket cylinder, and the force data of the pin at the bucket hinge, but also the bucket acceleration and attitude angle calculated by the kinematic model of the working device in the mechanism model.

[0073] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for testing the digging resistance of an excavator, characterized in that: This includes a data acquisition module, a mechanism model, an error estimation model, and a soft measurement model for excavation resistance; The testing method includes the following steps: Step 1: Use the data acquisition module to collect data on the displacement of the boom cylinder, stick cylinder, and bucket cylinder during the simulated excavation operation of the excavator, as well as the force data of the pin at the bucket hinge and the actual digging resistance data of the bucket tip. The data acquisition module includes a wire displacement sensor, a pin sensor, a tension sensor, and a device for simulating excavation resistance. In step 1, the displacement of the boom cylinder, stick cylinder, and bucket cylinder during the simulated excavation operation is obtained using the pull-wire displacement sensor; the force data of the pin at the bucket hinge is obtained using the pin sensor; and the actual excavation resistance data of the bucket tip is obtained using the tension sensor and the simulated excavation resistance device. The simulated excavation resistance device includes a load, a rope, and an excavator body. The tension sensor is connected to the bucket tip of the excavator body and the load via the rope. The actual digging resistance at the bucket tip is decomposed into tangential force, normal force, and torque using the aforementioned tension sensor and simulated digging resistance device. Step 2: Calculate the theoretical value of excavation resistance using the data collected in Step 1 combined with the mechanism model, and subtract the actual excavation resistance data to obtain the excavation resistance error data. The mechanism model calculation includes calculating the bucket acceleration and attitude angle using the kinematic model of the excavator's working device, and calculating the theoretical value of the digging resistance using the bucket dynamics model. In step 2, the kinematic model calculation of the excavator working device specifically involves: establishing a kinematic model of the excavator working device, and combining geometry to calculate the bucket acceleration and attitude angle based on the displacement of the boom cylinder, stick cylinder, and bucket cylinder. The calculation using the bucket dynamics model specifically involves: performing dynamic analysis on the bucket to obtain the bucket dynamics model, and calculating the theoretical value of the digging resistance based on the obtained force data of the pin at the bucket hinge and the bucket acceleration and attitude angle. Step 3: Using the data obtained in Step 1 and Step 2 as a dataset, construct a model for estimating resistance error using machine learning regression algorithms; Step 4: Combine the mechanistic model and the excavation resistance error estimation model to form a soft measurement model for excavation resistance. Calculate the theoretical excavation resistance, i.e., the initial estimate of the excavation resistance, from the mechanistic model. Obtain the estimation error of the excavation resistance from the excavation resistance error estimation model. Add the initial estimate of the excavation resistance to the estimation error to obtain the final estimate of the excavation resistance. Step 5: Measure the excavation resistance using a soft measurement model for excavation resistance.

2. The method for testing the digging resistance of an excavator according to claim 1, characterized in that: The pull-wire displacement sensor includes a first displacement sensor, a second displacement sensor, and a third displacement sensor, which are respectively installed at the positions of the bucket cylinder, the stick cylinder, and the boom cylinder.

3. The method for testing the digging resistance of an excavator according to claim 1, characterized in that: The pin sensor is fixedly connected to the bucket of the excavator body. The pin sensor includes a first pin sensor and a second pin sensor, which are respectively used to obtain the coordinate system of the bucket hinge. direction and The magnitude of the force in the direction.

4. The method for testing the digging resistance of an excavator according to claim 1, characterized in that: In step 3, the data on boom cylinder displacement, stick cylinder displacement, bucket cylinder displacement, force on the bucket hinge pin, bucket attitude angle, and acceleration are used as inputs, and the data on digging resistance error are used as outputs to construct a dataset. ; The dataset is divided into a training set and a validation set according to a certain ratio. On the training set, a machine learning algorithm is used to fit the relationship between the input and the output. The performance of the machine learning model is analyzed through the validation set, thereby obtaining the optimal model for estimating the mining resistance error.

5. The method for testing the digging resistance of an excavator according to claim 1, characterized in that: In step 4, the mechanistic model is used to evaluate the numerical values ​​in the soft measurement model of excavation resistance; The error estimation model is used in the soft measurement model of excavation resistance to correct the evaluated data.

6. The method for testing the digging resistance of an excavator according to claim 1, characterized in that: In step 5, the displacement of the boom cylinder, stick cylinder, bucket cylinder, and the force data of the pin at the bucket hinge during the actual excavation operation are obtained and input into the soft measurement model of excavation resistance, thereby estimating the excavation resistance.

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