Equipment detection method based on data analysis

Abstract

The invention discloses an equipment detection method based on data analysis, and belongs to the technical field of data processing, and the method comprises the following steps: S1, collecting motion parameters of a motor; s2, generating a driving load coefficient of the motor according to the motion parameters of the motor, and determining a motion model between the motor and the main shaft by using the driving load coefficient of the motor and the dynamic coefficient of the main shaft; and S3, based on the motion model between the motor and the main shaft, determining whether the main shaft works abnormally. The state of the spindle of the numerical control machine tool machining equipment can be monitored, and normal work of the equipment is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a device detection method based on data analysis. Background Art

[0002] In precision CNC machine tool processing, there is a large error between the target speed value set by the spindle motor and the actual speed value of the spindle motor. The spindle motor speed is unstable, which seriously affects the surface quality of the parts. Traditional speed monitoring methods often use contact sensors, which require additional components to be installed on the spindle, which not only increases the manufacturing cost, but also may affect the rotation balance of the spindle. In addition, for some processing equipment with special structures, contact sensors may not be installed or difficult to measure accurately. Therefore, a method for high-precision and real-time monitoring of the spindle speed is needed. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a device detection method based on data analysis.

[0004] The technical solution of the present invention is: a device detection method based on data analysis comprises the following steps:

[0005] S1, collecting the motion parameters of the motor;

[0006] S2. Generate a driving load coefficient of the motor according to the motion parameters of the motor, and determine a motion model between the motor and the spindle using the driving load coefficient of the motor and the dynamic coefficient of the spindle;

[0007] S3. Based on the motion model between the motor and the spindle, determine whether there is any abnormality in the operation of the spindle.

[0008] Furthermore, in S1, the motion parameters of the motor include the torque of the motor at each moment.

[0009] Furthermore, S2 includes the following sub-steps:

[0010] S21, splitting the torque of the motor at each moment into an X-direction torque component, a Y-direction torque component and a Z-direction torque component;

[0011] S22, determining a lateral influence equation, a longitudinal influence equation, and a vertical influence equation of the motor according to the X-direction torque component, the Y-direction torque component, and the Z-direction torque component of the motor at each moment;

[0012] S23, determining a driving load coefficient of the motor according to a lateral influence equation, a longitudinal influence equation, and a vertical influence equation of the motor;

[0013] S24, calculating the dynamic coefficient of the main shaft according to the torque of the main shaft at different times;

[0014] S25. Construct a motion model between the motor and the spindle according to the driving load coefficient of the motor and the dynamic coefficient of the spindle.

[0015] The beneficial effect of the above further scheme is: in the present invention, the torque causes the shaft of the motor to start rotating, thereby driving the load. The size and direction of the motor torque determine the ability and manner in which the motor can drive the load. The present invention needs to decompose the torque of the motor into components in multiple directions in order to more accurately describe and analyze the motion state of the spindle. The drive load coefficient of the motor is determined by the torque components of the motor in three directions, and then the drive load coefficient of the motor and the dynamic coefficient of the spindle are combined to construct a matrix as a motion model between the motor and the spindle to participate in the prediction of the spindle speed.

[0016] Furthermore, in S22, the expression of the lateral influence equation U of the motor is: ; In the formula, α represents the overshoot parameter, x i represents the X-direction torque component of the motor at the i-th moment, x i-1 represents the X-direction torque component of the motor at the i-1th moment, x1 represents the X-direction torque component of the motor at the 1st moment, and x I represents the X-direction torque component of the motor at the Ith moment, max(·) represents the maximum value operation, min(·) represents the minimum value operation, and I represents all moments;

[0017] In S22, the expression of the longitudinal influence equation V of the motor is: ; In the formula, y i represents the Y-direction torque component of the motor at the i-th moment, y i-1 represents the Y-direction torque component of the motor at the i-1th moment, y1 represents the Y-direction torque component of the motor at the 1st moment, and y I represents the Y-direction torque component of the motor at the Ith moment;

[0018] In S22, the expression of the vertical influence equation W of the motor is: ; In the formula, z i represents the Z-direction torque component of the motor at the i-th moment, z i-1 represents the Z-direction torque component of the motor at the i-1th moment, z1 represents the Z-direction torque component of the motor at the 1st moment, and z I Represents the Z-direction torque component of the motor at the Ith moment.

[0019] Furthermore, in S23, the calculation formula of the driving load H of the motor is: ; In the formula, β represents the step angle of the motor, U represents the lateral influence equation of the motor, V represents the longitudinal influence equation of the motor, and W represents the vertical influence equation of the motor.

[0020] Furthermore, in S24, the calculation formula of the dynamic coefficient M of the main axis is: ; In the formula, R represents the feed coefficient, exp(·) represents the exponential function, and f i represents the torque of the spindle at the i-th moment, and I represents all moments.

[0021] Furthermore, in S25, the expression of the motion model Q between the motor and the spindle is: ; In the formula, M represents the dynamic coefficient of the spindle, and H represents the driving load of the motor.

[0022] Furthermore, S3 includes the following sub-steps:

[0023] S31, calculating the average speed of the main shaft at all times;

[0024] S32, determining the speed of the spindle at the next moment according to the average speed of the spindle at all moments and the motion model between the motor and the spindle;

[0025] S33, judging whether the rotation speed of the spindle at the next moment is greater than or equal to the critical rotation speed of the spindle, if so, there is an abnormality in the operation of the spindle, otherwise, the operation of the spindle is normal.

[0026] The beneficial effect of the above further scheme is: in the present invention, the speed at the I+1th moment (i.e., the next moment) is determined based on the average speed of the spindle at moment I and the motion model, and when the speed at the next moment exceeds the critical speed, the spindle working abnormality is determined in time, which is convenient for the staff to grasp the working condition of the spindle in advance.

[0027] Further, in S32, the calculation formula of the spindle speed V at the next moment is: ; In the formula, P represents the power of the motor, x I represents the torque of the motor at the first moment, v ave It represents the average speed of the spindle at all times, and Q represents the motion model between the motor and the spindle.

[0028] The beneficial effects of the present invention are as follows: the present invention discloses an equipment detection method based on data analysis, which utilizes the torque conditions of the motor in three directions and the torque of the spindle to generate a motion model between the motor and the spindle; based on the motion model in matrix form, the spindle speed is predicted, and timely intervention is performed when the spindle speed is abnormal; therefore, the present invention can perform status monitoring on the spindle of CNC machine tool processing equipment to ensure the normal operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure is a flow chart of a device detection method based on data analysis. DETAILED DESCRIPTION

[0030] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0031] like Figure 1 As shown, the present invention provides a device detection method based on data analysis, comprising the following steps:

[0032] S1, collecting the motion parameters of the motor;

[0033] S2. Generate a driving load coefficient of the motor according to the motion parameters of the motor, and determine a motion model between the motor and the spindle using the driving load coefficient of the motor and the dynamic coefficient of the spindle;

[0034] S3. Based on the motion model between the motor and the spindle, determine whether there is any abnormality in the operation of the spindle.

[0035] In the embodiment of the present invention, in S1, the motion parameters of the motor include the torque of the motor at each moment.

[0036] In this embodiment of the present invention, S2 includes the following sub-steps:

[0037] S21, splitting the torque of the motor at each moment into an X-direction torque component, a Y-direction torque component and a Z-direction torque component;

[0038] S22, determining a lateral influence equation, a longitudinal influence equation, and a vertical influence equation of the motor according to the X-direction torque component, the Y-direction torque component, and the Z-direction torque component of the motor at each moment;

[0039] S23, determining a driving load coefficient of the motor according to a lateral influence equation, a longitudinal influence equation, and a vertical influence equation of the motor;

[0040] S24, calculating the dynamic coefficient of the main shaft according to the torque of the main shaft at different times;

[0041] S25. Construct a motion model between the motor and the spindle according to the driving load coefficient of the motor and the dynamic coefficient of the spindle.

[0042] In the present invention, the torque causes the shaft of the motor to start rotating, thereby driving the load. The magnitude and direction of the motor torque determine the ability and manner in which the motor can drive the load. The present invention requires decomposing the torque of the motor into components in multiple directions in order to more accurately describe and analyze the motion state of the spindle. The drive load coefficient of the motor is determined by the torque components of the motor in three directions, and then a matrix is ​​constructed by combining the drive load coefficient of the motor and the dynamic coefficient of the spindle as a motion model between the motor and the spindle to participate in the prediction of the spindle speed.

[0043] In the embodiment of the present invention, in S22, the expression of the lateral influence equation U of the motor is: ; In the formula, α represents the overshoot parameter, x i represents the X-direction torque component of the motor at the i-th moment, x i-1 represents the X-direction torque component of the motor at the i-1th moment, x1 represents the X-direction torque component of the motor at the 1st moment, and x I represents the X-direction torque component of the motor at the Ith moment, max(·) represents the maximum value operation, min(·) represents the minimum value operation, and I represents all moments;

[0044] In S22, the expression of the longitudinal influence equation V of the motor is: ; In the formula, y i represents the Y-direction torque component of the motor at the i-th moment, y i-1 represents the Y-direction torque component of the motor at the i-1th moment, y1 represents the Y-direction torque component of the motor at the 1st moment, and y I represents the Y-direction torque component of the motor at the Ith moment;

[0045] In S22, the expression of the vertical influence equation W of the motor is: ; In the formula, z i represents the Z-direction torque component of the motor at the i-th moment, z i-1 represents the Z-direction torque component of the motor at the i-1th moment, z1 represents the Z-direction torque component of the motor at the 1st moment, and z I Represents the Z-direction torque component of the motor at the Ith moment.

[0046] In the embodiment of the present invention, in S23, the calculation formula of the driving load H of the motor is: ; In the formula, β represents the step angle of the motor, U represents the lateral influence equation of the motor, V represents the longitudinal influence equation of the motor, and W represents the vertical influence equation of the motor.

[0047] In the embodiment of the present invention, in S24, the calculation formula of the dynamic coefficient M of the main shaft is: ; In the formula, R represents the feed coefficient, exp(·) represents the exponential function, and f i represents the torque of the spindle at the i-th moment, and I represents all moments.

[0048] In the embodiment of the present invention, in S25, the expression of the motion model Q between the motor and the spindle is: ; In the formula, M represents the dynamic coefficient of the spindle, and H represents the driving load of the motor.

[0049] In this embodiment of the present invention, S3 includes the following sub-steps:

[0050] S31, calculating the average speed of the main shaft at all times;

[0051] S32, determining the speed of the spindle at the next moment according to the average speed of the spindle at all moments and the motion model between the motor and the spindle;

[0052] S33, judging whether the rotation speed of the spindle at the next moment is greater than or equal to the critical rotation speed of the spindle, if so, there is an abnormality in the operation of the spindle, otherwise, the operation of the spindle is normal.

[0053] In the present invention, the speed at the I+1th moment (i.e., the next moment) is determined based on the average speed of the spindle at moment I and the motion model. When the speed at the next moment exceeds the critical speed, the spindle operating abnormality is determined in time, which is convenient for the staff to grasp the working condition of the spindle in advance.

[0054] In the embodiment of the present invention, in S32, the calculation formula of the rotation speed V of the spindle at the next moment is: ; In the formula, P represents the power of the motor, x I represents the torque of the motor at the first moment, v ave It represents the average speed of the spindle at all times, and Q represents the motion model between the motor and the spindle.

[0055] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A device detection method based on data analysis, characterized in that: The following steps are involved: S1, collecting the motion parameters of the motor; S2. Generate a driving load coefficient of the motor according to the motion parameters of the motor, and determine a motion model between the motor and the spindle using the driving load coefficient of the motor and the dynamic coefficient of the spindle; S3. Based on the motion model between the motor and the spindle, determine whether there is any abnormality in the operation of the spindle.

2. The device detection method based on data analysis according to claim 1, characterized in that: In S1, the motion parameters of the motor include the torque of the motor at each moment.

3. The device detection method based on data analysis according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21, splitting the torque of the motor at each moment into an X-direction torque component, a Y-direction torque component and a Z-direction torque component; S22, determining a lateral influence equation, a longitudinal influence equation, and a vertical influence equation of the motor according to the X-direction torque component, the Y-direction torque component, and the Z-direction torque component of the motor at each moment; S23, determining a driving load coefficient of the motor according to a lateral influence equation, a longitudinal influence equation, and a vertical influence equation of the motor; S24, calculating the dynamic coefficient of the main shaft according to the torque of the main shaft at different times; S25. Construct a motion model between the motor and the spindle according to the driving load coefficient of the motor and the dynamic coefficient of the spindle.

4. The device detection method based on data analysis according to claim 3 is characterized in that: In S22, the expression of the lateral influence equation U of the motor is: ; In the formula, α represents the overshoot parameter, x i represents the X-direction torque component of the motor at the i-th moment, x i-1 represents the X-direction torque component of the motor at the i-1th moment, x1 represents the X-direction torque component of the motor at the 1st moment, and x I represents the X-direction torque component of the motor at the Ith moment, max(·) represents the maximum value operation, min(·) represents the minimum value operation, and I represents all moments; In S22, the expression of the longitudinal influence equation V of the motor is: ; In the formula, y i represents the Y-direction torque component of the motor at the i-th moment, y i-1 represents the Y-direction torque component of the motor at the i-1th moment, y1 represents the Y-direction torque component of the motor at the 1st moment, and y I represents the Y-direction torque component of the motor at the Ith moment; In S22, the expression of the vertical influence equation W of the motor is: ; In the formula, z i represents the Z-direction torque component of the motor at the i-th moment, z i-1 represents the Z-direction torque component of the motor at the i-1th moment, z1 represents the Z-direction torque component of the motor at the 1st moment, and z I Represents the Z-direction torque component of the motor at the Ith moment.

5. The device detection method based on data analysis according to claim 3 is characterized in that: In S23, the calculation formula of the driving load H of the motor is: ; In the formula, β represents the step angle of the motor, U represents the lateral influence equation of the motor, V represents the longitudinal influence equation of the motor, and W represents the vertical influence equation of the motor.

6. The device detection method based on data analysis according to claim 3 is characterized in that: In S24, the calculation formula of the dynamic coefficient M of the main shaft is: ; In the formula, R represents the feed coefficient, exp(·) represents the exponential function, and f i represents the torque of the spindle at the i-th moment, and I represents all moments.

7. The device detection method based on data analysis according to claim 3 is characterized in that: In S25, the expression of the motion model Q between the motor and the spindle is: ; In the formula, M represents the dynamic coefficient of the spindle, and H represents the driving load of the motor.

8. The device detection method based on data analysis according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31, calculating the average speed of the main shaft at all times; S32, determining the speed of the spindle at the next moment according to the average speed of the spindle at all moments and the motion model between the motor and the spindle; S33, judging whether the rotation speed of the spindle at the next moment is greater than or equal to the critical rotation speed of the spindle, if so, there is an abnormality in the operation of the spindle, otherwise, the operation of the spindle is normal.

9. The device detection method based on data analysis according to claim 8, characterized in that: In S32, the calculation formula of the spindle speed V at the next moment is: ; In the formula, P represents the power of the motor, x I represents the torque of the motor at the first moment, v ave It represents the average speed of the spindle at all times, and Q represents the motion model between the motor and the spindle.

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