CN106524962A - Abrasion loss detection device for traveling wheel of coal mining machine and abrasion loss detecting and early-warning method - Google Patents

Abstract

The invention discloses a wear amount detection device and a wear amount detection and early warning method of a coal mining machine walking wheel. The MEMS sensor is an inertial measurement unit and is arranged in an installation groove on the end face of an idler wheel output shaft. The MEMS sensor is provided with a memory card, and The MEMS sensor is connected to the input end of the data receiving device through a cable, and the output end of the data receiving device is connected to a data processing device, and the data receiving device and the data processing device are arranged in the numerical control box; The walking wheels mesh with the pin rail teeth of the scraper conveyor. The invention has simple configuration and simple detection process; it does not require human participation, and is suitable for working in harsh mines, and can detect various kinematic and dynamic parameters of the traveling wheel during operation through the MEMS sensor on the output shaft of the idler wheel; It can get the correct wear amount data and respond in real time, timely and correctly judge whether the running wheel is in normal working condition and make early warning and response, which can avoid the occurrence of serious production accidents.

Description

A wear amount detection device and wear amount detection and early warning method of a coal mining machine traveling wheel

technical field

The invention relates to a wear amount detection device and a wear amount detection and early warning method of a coal shearer traveling wheel, belonging to the field of mining equipment.

Background technique

As the most critical equipment in the fully mechanized mining face, the shearer's product performance, quality and production capacity determine the production capacity of the working face and even the mine. Problems such as meshing and displacement between the wheel and the pin rail of the scraper conveyor lead to serious wear of the traveling wheel. If the amount of wear cannot be detected in time, broken teeth are prone to damage, seriously affecting the production efficiency of the working face, and even more serious accidents occur.

At present, the detection of wear can use detection devices and methods such as gear wear oil detection test bench, gear wear fault acoustic emission detection, and three-dimensional coordinate measuring machine detection of gear wear. However, on the one hand, most of the above methods rely on multiple detection elements. Compound detection, the process is cumbersome, the feedback result is not timely and error-prone; on the other hand, due to the harsh working environment of the coal mining machine, the above method is not suitable for detecting the wear of the coal mining machine's traveling wheel, resulting in the inability to know the working of the traveling wheel in time state.

Contents of the invention

In view of the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a device that can adapt to harsh working environments, has simple configuration and simple detection process, can process the wear data of the road wheels in real time, correctly judge whether the road wheels are in normal working condition, and can A coal mining machine travel wheel wear detection device that avoids serious production accidents; another object of the present invention is to provide a coal mining machine travel wheel wear that is easy to detect, accurate in results, and capable of timely and correctly judging the working status of the travel wheel and making a response Quantitative detection and early warning methods.

In order to achieve the above object, the technical solution adopted by the present invention is: a detection device for the amount of wear of the traveling wheel of a coal mining machine, including a MEMS sensor, the MEMS sensor is arranged in the installation groove of the end face of the output shaft of the idler wheel, and the MEMS sensor A memory card is arranged on it, and the MEMS sensor is connected to the input end of a data receiving device through a cable, and the output end of the data receiving device is connected to a data processing device, and the data receiving device and the data processing device are arranged in the numerical control box; The idler gear is meshed with the shearer traveling wheel, and the traveling wheel is meshed with the pin rail teeth of the scraper conveyor.

Further, it also includes an alarm device for traveling wheels of the coal mining machine, and the output end of the data processing device is connected to the alarm device for traveling wheels of the coal mining machine through a cable.

Preferably, the MEMS sensor is provided with a metal cover.

Preferably, the MEMS sensor is an IMU inertial measurement unit.

Preferably, the MEMS sensor is powered by a battery placed in the numerical control box through cables.

Preferably, the data receiving device is a mass storage device with a cable interface.

Preferably, the data processing device is a microprocessor with high-speed processing capability.

Preferably, the numerical control box is arranged on one side of the traction motor.

Preferably, the cables are downhole communication cables, and after being led out from the MEMS sensor, the downhole communication cables are arranged along the edge of the traction motor and connected into the numerical control box.

The present invention also provides a method for detection and early warning of the amount of wear of the traveling wheel of the coal mining machine, comprising the following steps:

(1) Define the coordinate system needed to calculate the wear amount of the traveling wheel. The navigation coordinate system O _n x _n y _n z _n is the reference coordinate system for calculation. The origin is at the end of the shearer, and the x _n axis is parallel to the shearer The working face of the shearer points to the right side of the fuselage horizontally, the y _n axis points to the hydraulic support and is perpendicular to the working face of the coal mining machine, the z _n axis points upward along the vertical line of the ground, and the navigation coordinate system is denoted as n system; the traveling wheel coordinate system O _b x _b y _b z _b is fixedly connected with the road wheel, the origin is at the center of the idler shaft of the road wheel, the x _b axis points to the right side of the machine body along the horizontal direction of the working face of the shearer, and the y _b axis is perpendicular to the machine body The direction points to the hydraulic support, z _b is upward along the vertical axis of the shearer; the coordinate system of the traveling wheel is counted as the carrier coordinate system b;

(2) Acceleration data a(t) is obtained by MEMS sensor detection, and the acceleration data is stored and transmitted to the data receiving device through the downhole communication cable. After the data is received, the data is imported into the data processing device. Direction cosine matrix obtained with MEMS sensor For attitude calculation, the acceleration defined in the carrier coordinate system left multiplied by the direction cosine Get the acceleration a ⁿ in the navigation coordinate system;

(3) Perform frequency domain integration on a ⁿ , and frequency domain integration will reduce the integration error due to time accumulation. According to the formula of Fourier transform, the Fourier component of the acceleration signal at any frequency can be expressed as

a(t)=Ae ^jωt ,

In the formula: a(t) is the Fourier component of the acceleration signal at frequency ω, A is the coefficient corresponding to a(t), and j is an imaginary number;

When the initial velocity component is 0, the velocity signal component can be obtained by time integrating the acceleration signal component, namely

In the formula: v(t) is the Fourier component of the velocity signal at frequency ω, and V is the coefficient corresponding to v(t);

Since an integral is in the frequency domain, the relational expression is:

When the initial velocity and initial displacement components are both 0, the displacement component can be obtained by integrating the Fourier component of the acceleration signal twice:

In the formula: s(t) is the Fourier component of the displacement signal at frequency ω, and S is the coefficient corresponding to s(t);

(4) Perform Fourier inverse transform on the displacement information after two integrations to obtain the displacement information in the time domain, that is, the position information of the meshing point of the road wheel; it can be known from the carrier coordinate system that the amount of wear that needs to be detected and calculated occurs at x _b z _b plane, so the displacement in the direction of y _b can be neglected. In the meshing time of a pair of teeth, s(t) is recorded as s ₁ (x ₁ , z ₁ ), s ₂ (x ₂ , z ₂ ), s ₃ (x ₃ , z ₃ )...s _n (x _n , z _n ), according to these n points, the profile curve of the meshing teeth of the road wheel is fitted by the least square method polynomial curve fitting principle and method ;

(5) When the next pair of teeth mesh, repeat the calculation and fitting until the traveling wheel rotates one turn, then the profile curve of the meshing teeth of the traveling wheel can be fitted;

(6) During the continuous rotation of the road wheel, the cycle calculation and curve fitting are carried out, and the wear amount of the road wheel is obtained by comparing the contour curve of each revolution with the contour curve of the previous revolution;

(7) The data processing device uses BP neural network to classify different wear amounts as training data, and the output result of BP neural network is sent to the coal mining machine walking wheel alarm device as a control command. When the output result of BP neural network reaches the preset When the pre-alarm value in the coal mining machine walking wheel alarm device is reached, the coal mining machine traveling wheel alarm device sends out a pre-alarm signal; when the output result of the BP neural network exceeds the alarm value preset in the coal mining machine traveling wheel alarm device , the alarm device sends out an alarm signal.

The invention has simple configuration and simple detection process; it does not require human participation, and is suitable for working in harsh mines, and can detect various kinematic and dynamic parameters of the traveling wheel during operation through the MEMS sensor on the output shaft of the idler wheel; It can get the correct wear amount data and make a real-time response, timely and correctly judge whether the road wheel is in normal working condition and make a response, which can avoid the occurrence of serious production accidents.

Description of drawings

Fig. 1 is the structural coordination schematic diagram of the present invention;

Fig. 2 is the whole side schematic diagram of the walking box of the coal mining machine of the present invention;

Fig. 3 is a connection diagram of the wear detection device;

Fig. 4 flow chart of the method for detecting the amount of wear of the traveling wheel;

Fig. 5 is a schematic diagram of calculating the amount of wear according to the fitting curve after zooming in at A in Fig. 1;

Fig. 6 is a schematic diagram of the relationship between the carrier coordinate system and the shearer;

In the figure, 1. Travel wheel; 2. MEMS sensor; 3. Idler wheel; 4. Idler wheel output shaft; 5. Idler wheel end cover; 6. Travel wheel end cover; 7. Traction motor; 8. Numerical control box; 9 , Pin rail teeth.

detailed description

The present invention will be described in further detail below in conjunction with the accompanying drawings.

As shown in the figure, a coal mining machine walking wheel wear detection device includes a MEMS sensor 2, the MEMS sensor 2 is arranged in the installation groove on the end face of the output shaft 4 of the idler wheel, and the idler wheel is installed on the output shaft 4 of the idler wheel 3, the idler 3 is provided with an idler end cover 5, the MEMS sensor 2 is provided with a memory card, and the MEMS sensor 2 is connected to the input end of a data receiving device through a cable, and the output end of the data receiving device is connected to A data processing device, the data receiving device and the data processing device are arranged in the numerical control box 8; the idler wheel 3 meshes with the coal mining machine travel wheel 1, and the travel wheel 1 covers the travel wheel end cover 6, and the travel wheel 1 meshes with the pin track teeth 9 of the scraper conveyor.

Specific principle: MEMS sensor 2 is installed on the output shaft 4 of the idler gear meshed with the road wheel 1, because the gear meshing transmission is a rigid transmission, so the MEMS sensor 2 installed on the output shaft 4 of the idler wheel can be used to detect whether the road wheel 1 is in the Various kinematic and dynamic parameters during operation. After the MEMS sensor 2 collects and stores the data, it transmits the data to the data receiving device through the cable. After the data reception is completed, the data receiving device no longer receives data, and imports the data into the data processing device, and through algorithm processing, the acceleration data of the road wheel 1 and the displacement information after two integrations are obtained, and the operating conditions and the displacement information of the road wheel 1 are obtained. L. The data processing unit compares the amount of wear with the extreme value of the amount of wear preset in the data processing unit, and judges whether the amount of wear exceeds the extreme value, and does not exceed the extreme value. It works normally and continues to detect; otherwise, it reminds people to repair or replace the traveling wheel 1 .

Further, it also includes an alarm device for traveling wheels of the coal mining machine, and the output end of the data processing device is connected to the alarm device for traveling wheels of the coal mining machine through a cable. After the data processing device obtains the amount of wear, it transmits the data to the alarm device of the traveling wheel of the coal mining machine. If the amount of wear exceeds the alarm value preset in the alarm device of the traveling wheel of the coal mining machine, the alarm device sends out an alarm signal.

Preferably, in order to protect the sensor from damage, the MEMS sensor 2 is provided with a metal cover.

Preferably, the MEMS sensor 2 is an IMU inertial measurement unit. The IMU inertial measurement unit is a sensing component composed of a three-axis acceleration sensor and a three-axis gyroscope sensor, which can accurately detect various kinematic and dynamic parameters of the traveling wheel 1 during operation.

Preferably, the MEMS sensor 2 is powered by a battery placed in the numerical control box 8 through cables.

Preferably, the memory card is a UFS2.0 or above high-speed memory card, and the memory card is connected to the memory card interface of the MEMS sensor 2, which can store a large amount of data quickly.

Preferably, in order to facilitate the connection between the data receiving device and the MEMS sensor 2, the data receiving device is a large-capacity memory with a cable interface.

Preferably, the data processing device is a microprocessor with high-speed processing capability.

Preferably, the numerical control box 8 is arranged on one side of the traction motor 7 .

Preferably, in order to ensure the safety of use, the cable is an underground communication cable, and after the underground communication cable is led out from the MEMS sensor 2, it is arranged along the edge of the traction motor 7 and connected to the numerical control box 8.

The method for detection and early warning of the amount of wear of the traveling wheel of the coal mining machine comprises the following steps:

(1) The wear amount parameter of the road wheel 1 is determined by the acceleration data, and the acquisition of the acceleration needs to be determined by the relationship between the various coordinate systems.

Define the coordinate system needed to calculate the wear amount of the traveling wheel, the navigation coordinate system O _n x _n y _n z _n is the reference coordinate system for calculation, the origin is at the end of the shearer, and the x _n axis is parallel to the working face of the shearer Pointing horizontally to the right side of the fuselage, the y _n axis points to the hydraulic support and is perpendicular to the working face of the shearer, the z _n axis points upward along the vertical line, and the navigation coordinate system is recorded as n system; the traveling wheel coordinate system O _b x _b y _b z _b is fixedly connected with the traveling wheel, the origin is at the center of the idler shaft of the traveling wheel, the x _b axis points to the right side of the machine body along the horizontal direction of the working face of the shearer, and the y _b axis points to the hydraulic pressure along the direction perpendicular to the machine body Bracket, z _b is upward along the vertical axis of the shearer; the coordinate system of the traveling wheel is counted as the carrier coordinate system b;

(2) MEMS sensor 2 detects and obtains the acceleration data a(t), stores the acceleration data and transmits it to the data receiving device through the downhole communication cable, after the data receiving is completed, the data is imported into the data processing device, and after Kalman filter processing, For the direction cosine matrix obtained by MEMS sensor 2 For attitude calculation, the acceleration defined in the carrier coordinate system left multiplied by the direction cosine Get the acceleration a ⁿ in the navigation coordinate system;

(3) Carry out frequency domain integration on a ⁿ , and frequency domain integration will reduce the integration error caused by time accumulation. The basic principle is that the signal to be integrated is first Fourier transformed, and then the transformed result is carried out in the frequency domain Integral operation, and finally the integrated time domain signal is obtained by inverse Fourier transform:

According to the Fourier transform formula, the Fourier component of the acceleration signal at any frequency can be expressed as

a(t)=Ae ^jωt ,

In the formula: a(t) is the Fourier component of the acceleration signal at frequency ω, A is the coefficient corresponding to a(t), and j is an imaginary number;

When the initial velocity component is 0, the velocity signal component can be obtained by time integrating the acceleration signal component, namely

In the formula: v(t) is the Fourier component of the velocity signal at frequency ω, and V is the coefficient corresponding to v(t);

Since an integral is in the frequency domain, the relational expression is:

When the initial velocity and initial displacement components are both 0, the Fourier component of the acceleration signal can be integrated twice to obtain the displacement component:

In the formula: s(t) is the Fourier component of the displacement signal at frequency ω, and S is the coefficient corresponding to s(t).

(4) The displacement information after the two integrations is inversely transformed by Fourier to obtain the displacement information in the time domain, that is, the position information of the meshing point of the road wheel 1; it can be known from the carrier coordinate system that the amount of wear that needs to be detected and calculated occurs In the x _b z _b plane, so the displacement in the y _b direction can be neglected. In the meshing time of a pair of teeth, s(t) is recorded as s ₁ (x ₁ , z ₁ ), s ₂ (x ₂ , z ₂ ), s ₃ (x ₃ , z ₃ )...s _n (x _n , z _n ), according to these n points, the profile of the meshing teeth of the road wheel is fitted by the least square method polynomial curve fitting principle and method curve;

(5) When the next pair of teeth mesh, repeat the calculation and fitting until the traveling wheel rotates one turn, then the profile curve of the meshing teeth of the traveling wheel can be fitted;

(6) During the continuous rotation of the road wheel, the cycle calculation and curve fitting are carried out, and the wear amount of the road wheel 1 is obtained by comparing the contour curve of each revolution with the contour curve of the previous revolution;

(7) The data processing device uses BP neural network to classify different wear amounts as training data, and the output result of BP neural network is sent to the coal mining machine walking wheel alarm device as a control command. When the output result of BP neural network reaches the preset When the pre-alarm value in the coal mining machine walking wheel alarm device is reached, the coal mining machine traveling wheel alarm device sends out a pre-alarm signal; when the output result of the BP neural network exceeds the alarm value preset in the coal mining machine traveling wheel alarm device , the alarm device sends out an alarm signal.

Claims (10)

1. A shearer traveling wheel wear amount detection device is characterized in that, comprises a MEMS sensor, and described MEMS sensor is arranged in the mounting groove of idler output shaft end face, and described MEMS sensor is provided with memory card, and The MEMS sensor is connected to the input end of a data receiving device through a cable, and the output end of the data receiving device is connected to a data processing device, and the data receiving device and the data processing device are arranged in the numerical control box; The walking wheels of the machine are meshed, and the walking wheels are meshed with the pin rail teeth of the scraper conveyor. 2. The detection device for the amount of wear of the coal mining machine traveling wheel according to claim 1, further comprising a coal mining machine traveling wheel alarm device, the output end of the data processing device is connected to the coal mining machine through a cable Walking wheel alarm device. 3 . The detection device for the amount of wear of the traveling wheel of the coal mining machine according to claim 1 , wherein a metal cover is provided outside the MEMS sensor. 4 . 4. The coal mining machine walking wheel wear detection device according to claim 1, wherein the MEMS sensor is an IMU inertial measurement unit, and the IMU inertial measurement unit is composed of a three-axis gyroscope sensor and a three-axis acceleration sensor composition. 5. The detection device for the amount of wear of the traveling wheel of the coal mining machine according to claim 1, wherein the MEMS sensor is powered by a battery placed in the numerical control box through a cable. 6. The detection device for the amount of wear of the traveling wheel of the coal mining machine according to claim 1, wherein the data receiving device is a large-capacity memory with a cable interface. 7. The detection device for the wear amount of the traveling wheel of the coal mining machine according to claim 1, wherein the data processing device is a microprocessor with high-speed processing capability. 8. The detection device for the wear amount of the traveling wheel of the coal mining machine according to claim 1, wherein the numerical control box is arranged on the side of the traction motor. 9. The detection device for the amount of wear of the traveling wheel of a coal mining machine according to claim 8, wherein the cable is an underground communication cable, and after the underground communication cable is drawn out from the MEMS sensor, it is arranged along the edge of the traction motor, connected to into the CNC box. 10. A method for detection and early warning of the amount of wear of the traveling wheel of a coal mining machine, characterized in that it comprises the following steps: (1) Define the coordinate system needed to calculate the wear amount of the traveling wheel. The navigation coordinate system O _n x _n y _n z _n is the reference coordinate system for calculation. The origin is at the end of the shearer, and the x _n axis is parallel to the shearer The working face of the shearer points to the right side of the fuselage horizontally, the y _n axis points to the hydraulic support and is perpendicular to the working face of the coal mining machine, the z _n axis points upward along the vertical line of the ground, and the navigation coordinate system is denoted as n system; the traveling wheel coordinate system O _b x _b y _b z _b is fixedly connected with the road wheel, the origin is at the center of the idler shaft of the road wheel, the x _b axis points to the right side of the machine body along the horizontal direction of the working face of the shearer, and the y _b axis is perpendicular to the machine body The direction points to the hydraulic support, z _b is upward along the vertical axis of the shearer; the coordinate system of the traveling wheel is counted as the carrier coordinate system b; (2) Acceleration data a(t) is obtained by MEMS sensor detection, and the acceleration data is stored and transmitted to the data receiving device through the downhole communication cable. After the data is received, the data is imported into the data processing device. Direction cosine matrix obtained with MEMS sensor For attitude calculation, the acceleration a ^b defined in the carrier coordinate system is multiplied by the direction cosine to the left Get the acceleration a ⁿ in the navigation coordinate system; (3) Perform frequency domain integration on a ⁿ , and frequency domain integration will reduce the integration error due to time accumulation. According to the formula of Fourier transform, the Fourier component of the acceleration signal at any frequency can be expressed as a(t)=Ae ^jωt , In the formula: a(t) is the Fourier component of the acceleration signal at frequency ω, A is the coefficient corresponding to a(t), and j is an imaginary number; When the initial velocity component is 0, the velocity signal component can be obtained by time integrating the acceleration signal component, namely $\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; Ae</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; \&tau;</span>}}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; z</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Ve</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; ,</span>$ In the formula: v(t) is the Fourier component of the velocity signal at frequency ω, and V is the coefficient corresponding to v(t); Since an integral is in the frequency domain, the relational expression is: $\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; ,</span>$ When the initial velocity and initial displacement components are both 0, the displacement component can be obtained by integrating the Fourier component of the acceleration signal twice: $\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&lsqb;</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; Ve</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; \&tau;</span>}}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; V</span>}}{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; A</span>}}{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; Se</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; ;</span>$ In the formula: s(t) is the Fourier component of the displacement signal at frequency ω, and S is the coefficient corresponding to s(t); (4) Perform Fourier inverse transform on the displacement information after two integrations to obtain the displacement information in the time domain, that is, the position information of the meshing point of the road wheel; it can be known from the carrier coordinate system that the amount of wear that needs to be detected and calculated occurs at x _b z _b plane, so the displacement in the direction of y _b can be neglected. In the meshing time of a pair of teeth, s(t) is recorded as s ₁ (x ₁ , z ₁ ), s ₂ (x ₂ , z ₂ ), s ₃ (x ₃ , z ₃ )...s _n (x _n , z _n ), according to these n points, the profile curve of the meshing teeth of the road wheel is fitted by the least square method polynomial curve fitting principle and method ; (5) When the next pair of teeth mesh, repeat the calculation and fitting until the traveling wheel rotates one turn, then the profile curve of the meshing teeth of the traveling wheel can be fitted; (6) During the continuous rotation of the road wheel, the cycle calculation and curve fitting are carried out, and the wear amount of the road wheel is obtained by comparing the contour curve of each revolution with the contour curve of the previous revolution; (7) The data processing device uses BP neural network to classify different wear amounts as training data, and the output result of BP neural network is sent to the coal mining machine walking wheel alarm device as a control command. When the output result of BP neural network reaches the preset When the pre-alarm value in the coal mining machine walking wheel alarm device is reached, the coal mining machine traveling wheel alarm device sends out a pre-alarm signal; when the output result of the BP neural network exceeds the alarm value preset in the coal mining machine traveling wheel alarm device , the alarm device sends out an alarm signal.