阅读说明：本技术 滚珠螺杆预压衰退判定方法 (Method for judging precompression recession of ball screw ) 是由 林育新 于 2020-06-08 设计创作，主要内容包括：一种滚珠螺杆预压衰退判定方法,借由一讯号连接一第一感测单元的计算机装置来实施,所述第一感测单元周期性地传送一相关于所述循环配件中之滚珠振动的振动讯号至所述计算机装置,并包含：(A)根据所接收到的至少一振动讯号,获得一振动时域数据；(B)根据所述振动时域数据,获得至少一笔对应所述振动时域数据的振动子频域数据；(C)对于每一振动子频域数据,获得一相关于所述振动子频域数据的振动特征向量；(D)根据所述振动特征向量、多个振动参考向量及一预压检测范围,获得一预压判定结果；(E)根据所述预压判定结果,判定所述滚珠螺杆之预压是否已衰退。(A method for determining a pre-compression failure of a ball screw, implemented by a computer device in signal communication with a first sensing unit that periodically transmits a vibration signal to the computer device related to vibration of balls in the circulation fitting, comprising: (A) obtaining a vibration time domain data according to the received at least one vibration signal; (B) obtaining at least one set of vibrator frequency domain data corresponding to the vibration time domain data according to the vibration time domain data; (C) for each vibrator frequency domain data, obtaining a vibration feature vector related to the vibrator frequency domain data; (D) obtaining a pre-pressing judgment result according to the vibration characteristic vector, the plurality of vibration reference vectors and a pre-pressing detection range; (E) and judging whether the pre-pressure of the ball screw is declined or not according to the pre-pressure judgment result.)

1. A method for determining a deterioration of a ball screw preload by a computer device signally connected to a first sensing unit mounted to a nut of a ball screw and adjacent to a circulation member of the ball screw and periodically transmitting a vibration signal related to a vibration of balls in the circulation member to the computer device, the method comprising: the method for judging the precompression recession of the ball screw comprises the following steps:

(A) obtaining, by the computer device, vibration time domain data corresponding to the at least one vibration signal according to the received at least one vibration signal;

(B) obtaining at least one set of vibrator frequency domain data corresponding to the vibration time domain data according to the vibration time domain data by the computer device;

(C) for each vibrator frequency domain data, obtaining, by the computer device, a vibration feature vector associated with the vibrator frequency domain data according to the vibrator frequency domain data;

(D) obtaining a pre-pressing judgment result according to the vibration characteristic vector, a plurality of vibration reference vectors and a pre-pressing detection range by the computer device; and

(E) and judging whether the pre-pressure of the ball screw is declined or not by the computer device according to the pre-pressure judgment result.

2. The method for determining a preload failure of a ball screw according to claim 1, wherein: the step (B) further comprises the following steps:

(B-1) performing envelope processing on the vibration time domain data by means of the computer device to obtain processed vibration time domain data;

(B-2) obtaining, by the computer device, a target vibration time domain data associated with a uniform velocity period while the ball screw is moving, based on the processed vibration time domain data;

(B-3) obtaining, by the computer device, the at least one vibrator frequency domain data according to the target vibration time domain data.

3. The method for determining a preload failure of a ball screw according to claim 2, wherein: the step (B-3) further comprises the steps of:

(B-3-1) obtaining, by the computer device, at least one vibrator time domain data from the target vibration time domain data; and

(B-3-2) for each vibrator time domain data, performing Fourier transform by the computer device according to the vibrator time domain data to obtain the vibrator frequency domain data corresponding to the vibrator time domain data.

4. The method for determining a preload failure of a ball screw according to claim 1, wherein: the step (D) comprises the following steps:

(D-1) calculating, for each vibration feature vector, a second vibration candidate distance between the vibration feature vector and each vibration reference vector by the computer device;

(D-2) for each vibration feature vector for which the second vibration candidate distance has been obtained, obtaining, by the computer device, a corresponding second vibration target distance having the shortest distance from the second vibration candidate distances corresponding to the vibration feature vector; and

(D-3) obtaining the pre-compaction determination result according to the second vibration target distance and the pre-compaction detection range by the computer device.

5. The method for determining a preload failure of a ball screw according to claim 1, wherein: in step (C), each of the vibration feature vectors includes at least one of a kurtosis feature vector indicating kurtosis of the corresponding vibrator frequency domain data, a maximum frequency domain peak feature vector indicating a maximum peak of the corresponding vibrator frequency domain data, and a total energy feature vector indicating a total energy of the corresponding vibrator frequency domain data.

6. The method for determining a preload failure of a ball screw according to claim 1, wherein: the computer device stores a plurality of first training vibration feature vectors and a plurality of second training vibration feature vectors, wherein before step (D), the method further comprises the following steps:

(F) obtaining, by the computer device, the vibration reference vector in the corresponding data space using an unsupervised algorithm based on the first training vibration feature vector; and

(G) and obtaining the pre-pressing detection range according to the vibration reference vector and the second training vibration feature vector by the computer device.

7. The method for determining a preload failure of a ball screw according to claim 5, wherein: in step (F), the unsupervised algorithm comprises a clustering algorithm, and the vibration reference vector comprises a vector of centers corresponding to each of a plurality of vibration clusters obtained by the clustering algorithm.

8. The method for determining a preload failure of a ball screw according to claim 5, wherein: in step (F), the unsupervised algorithm comprises a self-organizing map algorithm, and the vibration reference vector comprises vectors corresponding to all neurons with update times greater than a preset time obtained by the self-organizing map algorithm.

9. The method for determining a preload failure of a ball screw according to claim 5, wherein: step (G) further comprises the steps of:

(G-1) for each second training vibration feature vector, calculating, by the computer device, a first vibration candidate distance between the second training vibration feature vector and each vibration reference vector, respectively;

(G-2) for each second training vibration feature vector for which the first vibration candidate distance has been obtained, obtaining, by the computer device, a first vibration target distance corresponding to a shortest distance from the first vibration candidate distances corresponding to the second training vibration feature vector; and

(G-3) obtaining the pre-pressure detection range according to the first vibration target distance by means of the computer device.

10. The method for determining a preload failure of a ball screw according to claim 1, wherein: the computer device further signally connected to a second sensing unit mounted to the nut and periodically transmitting an inertial force signal related to an inertial force of the nut with respect to a direction of movement of a screw shaft of the ball screw to the computer device, wherein after step (E), further comprising the steps of:

(H) when the pre-pressure of the ball screw is determined to have declined, obtaining at least one inertia force time domain data corresponding to at least one inertia force signal according to the received at least one inertia force signal by the computer device;

(I) for each inertia force time domain data, obtaining an inertia force feature vector related to the inertia force time domain data according to the inertia force time domain data by the computer device;

(J) obtaining a backlash judgment result according to the inertia force characteristic vector, a plurality of inertia force reference vectors and a backlash detection range by the computer device; and

(K) and judging whether the ball screw generates the backlash or not according to the backlash judgment result by the computer device.

Technical Field

The present invention relates to a method for testing, and more particularly to a method for testing preload of a ball screw.

Background

In the related art, a ball screw is widely used in a machine tool requiring precise positioning, wherein the ball screw is formed by screwing a nut to a screw shaft through balls capable of rolling, and the nut is linearly moved along the screw shaft by rolling of the nut and the balls in a circulation fitting (a return fitting).

However, as the service life of the ball screw is longer, the pre-pressing of the ball screw is gradually reduced, and the insufficient pre-pressing can make the nut easily vibrate back and forth during the reciprocating movement process, resulting in a reduction in precision, and when the pre-pressing is decayed to a certain degree, the ball screw even can be caused to generate backlash.

Refer to taiwan patent No. TW I653410B, which aims to detect whether the ball screw has generated backlash (no preload), and cannot know whether the ball screw has only preload decay but has not generated backlash.

Disclosure of Invention

The invention aims to provide a method for judging whether the prepressing of a ball screw only declines and a back clearance is not generated.

The method for determining the pre-pressing recession of the ball screw according to the present invention is implemented by a computer device, wherein the computer device is in signal connection with a first sensing unit, the first sensing unit is installed on a nut of the ball screw and is adjacent to a circulating fitting of the ball screw and periodically transmits a vibration signal related to the vibration of the balls in the circulating fitting to the computer device, and the method for determining the pre-pressing recession of the ball screw comprises the following steps.

And (A) obtaining vibration time domain data corresponding to the at least one vibration signal by the computer device according to the received at least one vibration signal.

And (B) obtaining at least one set of vibrator frequency domain data corresponding to the vibration time domain data according to the vibration time domain data by the computer device.

And (C) for each vibrator frequency domain data, obtaining a vibration feature vector related to the vibrator frequency domain data according to the vibrator frequency domain data by the computer device.

And (D) obtaining a pre-pressing judgment result according to the vibration characteristic vector, the plurality of vibration reference vectors and a pre-pressing detection range by the computer device.

And (E) judging whether the pre-pressure of the ball screw is declined or not according to the pre-pressure judgment result by the computer device.

The method for determining the precompression fade of the ball screw according to the present invention, wherein the step (B) further comprises the steps of:

(B-1) performing envelope processing on the vibration time domain data by means of the computer device to obtain processed vibration time domain data;

(B-2) obtaining, by the computer device, a target vibration time domain data associated with a uniform velocity period while the ball screw is moving, based on the processed vibration time domain data;

(B-3) obtaining, by the computer device, the at least one vibrator frequency domain data according to the target vibration time domain data.

The method for determining the preload recession of the ball screw according to the present invention, wherein the step (B-3) further comprises the steps of:

(B-3-1) obtaining, by the computer device, at least one vibrator time domain data from the target vibration time domain data; and

(B-3-2) for each vibrator time domain data, performing Fourier transform by the computer device according to the vibrator time domain data to obtain the vibrator frequency domain data corresponding to the vibrator time domain data.

The method for determining the precompression recession of a ball screw according to the present invention, wherein the step (D) comprises the steps of:

(D-1) calculating, for each vibration feature vector, a second vibration candidate distance between the vibration feature vector and each vibration reference vector by the computer device;

(D-2) for each vibration feature vector for which the second vibration candidate distance has been obtained, obtaining, by the computer device, a corresponding second vibration target distance having the shortest distance from the second vibration candidate distances corresponding to the vibration feature vector; and

(D-3) obtaining the pre-compaction determination result according to the second vibration target distance and the pre-compaction detection range by the computer device.

In the step (C), each vibration feature vector includes at least one of a kurtosis feature vector indicating the kurtosis of the corresponding vibrator frequency domain data, a maximum frequency domain peak feature vector indicating the maximum peak of the corresponding vibrator frequency domain data, and a total energy feature vector indicating the total energy of the corresponding vibrator frequency domain data.

The method for determining the precompression degradation of the ball screw according to the present invention, wherein the computer device stores a plurality of first training vibration feature vectors and a plurality of second training vibration feature vectors, and before the step (D), further comprises the steps of:

(F) obtaining, by the computer device, the vibration reference vector in the corresponding data space using an unsupervised algorithm based on the first training vibration feature vector; and

(G) and obtaining the pre-pressing detection range according to the vibration reference vector and the second training vibration feature vector by the computer device.

In the method for determining the preload and recession of the ball screw according to the present invention, in the step (F), the unsupervised algorithm includes a clustering algorithm, and the vibration reference vector includes vectors of centers corresponding to a plurality of vibration clusters obtained by the clustering algorithm, respectively.

In the step (F), the unsupervised algorithm includes a self-organizing mapping algorithm, and the vibration reference vector includes vectors corresponding to all neurons whose update times obtained by the self-organizing mapping algorithm are greater than a preset time.

The method for determining the precompression fading of a ball screw according to the present invention, wherein the step (G) further comprises the steps of:

(G-1) for each second training vibration feature vector, calculating, by the computer device, a first vibration candidate distance between the second training vibration feature vector and each vibration reference vector, respectively;

(G-2) for each second training vibration feature vector for which the first vibration candidate distance has been obtained, obtaining, by the computer device, a first vibration target distance corresponding to a shortest distance from the first vibration candidate distances corresponding to the second training vibration feature vector; and

(G-3) obtaining the pre-pressure detection range according to the first vibration target distance by means of the computer device.

In the method for determining the pre-pressing recession of the ball screw according to the present invention, the computer device further signal-connects a second sensing unit installed on the nut and periodically transmitting an inertial force signal related to an inertial force of the nut with respect to a moving direction of a screw shaft of the ball screw to the computer device, and after the step (E), the method further comprises the steps of:

(H) when the pre-pressure of the ball screw is determined to have declined, obtaining at least one inertia force time domain data corresponding to at least one inertia force signal according to the received at least one inertia force signal by the computer device;

(I) for each inertia force time domain data, obtaining an inertia force feature vector related to the inertia force time domain data according to the inertia force time domain data by the computer device;

(J) obtaining a backlash judgment result according to the inertia force characteristic vector, a plurality of inertia force reference vectors and a backlash detection range by the computer device; and

(K) and judging whether the ball screw generates the backlash or not according to the backlash judgment result by the computer device.

The invention has the beneficial effects that: by means of the at least one vibration characteristic vector, the vibration reference vector and the pre-pressing detection range obtained according to the at least one vibration signal, the pre-pressing determination result for determining whether the pre-pressing of the ball screw is decayed is obtained, and the situation that the ball screw only has pre-pressing decay and does not generate back clearance can be detected.

Drawings

Other features and effects of the present invention will become apparent from the following detailed description of the embodiments with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a ball screw preload fade determination system implementing one embodiment of the ball screw preload fade determination method of the present invention;

FIG. 2 is a schematic view illustrating a first sensing unit, a second sensing unit and a ball screw according to the embodiment;

FIG. 3 is a flow chart illustrating a precompression training procedure of the embodiment;

FIG. 4 is a flow chart illustrating a backlash training procedure of the described embodiment;

FIG. 5 is a flowchart illustrating steps 70-77 of a pre-pressure determination procedure according to the embodiment;

FIG. 6 is a flowchart illustrating steps 78-85 of the pre-pressure determination procedure according to the embodiment;

FIG. 7 is a flowchart illustrating how the pre-stress determination procedure of the embodiment obtains vibrator frequency domain data; and

fig. 8 is a flowchart illustrating how the preload determination routine of the embodiment obtains the inertia force feature vector.

Detailed Description

Before the present invention is described in detail, it should be noted that in the following description, similar components are denoted by the same reference numerals.

Referring to fig. 1, an embodiment of the method for determining the pre-pressing recession of the ball screw according to the present invention is implemented by a system 100 for determining the pre-pressing recession of the ball screw, wherein the system 100 for determining the pre-pressing recession of the ball screw comprises a computer device 1, a first sensing unit 2 in signal connection with the computer device 1, and a second sensing unit 3 in signal connection with the computer device 1.

Referring to fig. 1 and 2, the first sensing unit 2 is mounted on a nut 41 of a ball screw 4 and is adjacent to a circulation member 43 of the ball screw 4 and periodically transmits a vibration signal related to the vibration of balls 44 in the circulation member 43 to the computer device 1. The second sensing unit 3 is mounted on the nut 41 and periodically transmits an inertial force signal of an inertial force with respect to a moving direction of the nut 41 with respect to a screw shaft 42 of the ball screw to the computer device 1.

It should be noted that, according to different types of ball screws (fig. 2 only discloses an external circulation type ball screw), the user can install the first sensing unit 2 and the second sensing unit 3 at the positions corresponding to the respective types of ball screws 4 (indicating that it is also possible to install the first sensing unit 2 and the second sensing unit 3 at the same position according to different types of ball screws 4) to obtain the vibration signal related to the vibration of the balls 44 in the circulation fitting 43 and the inertial force signal related to the inertial force of the nut 41 relative to the moving direction of the screw shaft of the ball screw 42.

The computer device 1 comprises a communication module 11 in signal connection with the first sensing unit 2 and the second sensing unit 3, a storage module 12, a display module 13, and a processing module 14 electrically connected with the communication module 11, the storage module 12 and the display module 13.

In this embodiment, the implementation of the computer device 1 is, for example, a personal computer, a server, or a cloud host, but not limited thereto.

In the present embodiment, the first sensing unit 2 and the second sensing unit 3 are implemented by an Accelerometer (Accelerometer), but not limited thereto, and may be a displacement meter or a speedometer in other embodiments. Furthermore, the effective bandwidth of the first sensing unit 2 is required to cover 0.1 to 5Hz, and the effective bandwidth of the second sensing unit 3 is required to cover 10 times of the rotation frequency of the screw shaft 42 (e.g., 0.1 to 250Hz), and the digital resolution is 20 bit.

The storage module 12 stores a plurality of first training vibration feature vectors, a plurality of second training vibration feature vectors, a plurality of first training inertia force feature vectors, and a plurality of second training inertia force feature vectors. Wherein each training vibration feature vector includes at least one of a training Kurtosis feature vector indicating Kurtosis (Kurtosis) of frequency domain data corresponding to the training vibration feature vector, a training maximum frequency domain peak feature vector indicating a maximum peak of the frequency domain data corresponding to the training vibration feature vector, and a training total energy feature vector indicating a total energy of the frequency domain data corresponding to the training vibration feature vector, but not limited thereto. Wherein each training inertia force feature vector includes at least one of a training peak-to-peak feature vector indicating a peak-to-peak value (peak-to-peak) of the time domain data corresponding to the training inertia force feature vector, a training maximum time domain peak feature vector indicating a maximum peak of the time domain data corresponding to the training inertia force feature vector, and a training average peak feature vector indicating that a sum of an absolute value of the maximum peak and an absolute value of the minimum peak of the time domain data corresponding to the training inertia force feature vector is averaged, but not limited to the above.

The details of the operations of the components of the computer device 1, the first sensing unit 2, and the second sensing unit 3 of the ball screw preload failure determination system 100 will be described below with reference to the embodiment of the ball screw preload failure determination method according to the present invention, which includes a preload training procedure, a backlash training procedure, and a preload determination procedure.

Referring to fig. 3, the pre-compression training procedure includes steps 50 to 53.

In step 50, the processing module 14 obtains a plurality of vibration reference vectors in the corresponding data space by using an unsupervised algorithm according to the first training vibration feature vector. Further, when the unsupervised algorithm includes a clustering algorithm (e.g., K-mean), the vibration reference vector includes vectors of centers corresponding to a plurality of vibration clusters obtained by the clustering algorithm, respectively; when the unsupervised algorithm includes a Self-Organizing mapping (SOM), the vibration reference vector includes vectors corresponding to all neurons with update times greater than a predetermined time obtained by the Self-Organizing mapping algorithm, but not limited to the above example.

In step 51, for each second training vibration feature vector, the processing module 14 calculates a first vibration candidate distance between the second training vibration feature vector and each vibration reference vector. Further, the euclidean distance is used for calculating each first vibration candidate distance, but not limited thereto.

In step 52, for each second training vibration feature vector having obtained the first vibration candidate distance, the processing module 14 obtains a first vibration target distance corresponding to the shortest distance from the first vibration candidate distances corresponding to the second training vibration feature vector.

In step 53, the processing module 14 obtains a pre-pressure detection range according to the first vibration target distance. In this embodiment, the first vibration target distance is used as a Normal Distribution (Normal Distribution), and a 95% Confidence Interval (CI) corresponding to the first vibration target distance is used as the pre-pressure detection range, but not limited thereto.

Referring to fig. 4, the backlash training procedure includes steps 60 to 63.

In step 60, the processing module 14 obtains a plurality of reference vectors of inertial force in the corresponding data space by using another unsupervised algorithm according to the first training inertial force feature vector. Further, when the other unsupervised algorithm comprises a clustering algorithm, the reference vector of the inertial force comprises vectors of centers respectively corresponding to a plurality of clusters of the inertial force obtained by the clustering algorithm; when the other unsupervised algorithm includes the self-organizing map algorithm, the inertial force reference vector includes vectors corresponding to all neurons with update times obtained by the self-organizing map algorithm being greater than another preset time, but not limited to the above example.

In step 61, for each second training inertia force feature vector, the processing module 14 calculates a first inertia force candidate distance between the second training inertia force feature vector and each inertia force reference vector. Further, the euclidean distance is used for calculating each first inertia force candidate distance, but not limited thereto.

In step 62, for each second training inertia force feature vector having obtained the first inertia force candidate distance, the processing module 14 obtains a first inertia force target distance corresponding to the shortest distance from the first inertia force candidate distances corresponding to the second training inertia force feature vector.

In step 63, the processing module 14 obtains a backlash detection range according to the first inertial force target distance. In addition, in this embodiment, the first inertia force target distance is taken as a normal distribution, and the 95% confidence interval corresponding to the first inertia force target distance is taken as the backlash detection range, but not limited thereto.

Referring to fig. 5 and 6, the pre-pressure determination routine includes steps 70 to 85.

In step 70, the processing module 14 obtains a vibration time domain data corresponding to the at least one vibration signal according to the received at least one vibration signal.

In step 71, the processing module 14 obtains at least one set of vibrator frequency domain data corresponding to the vibration time domain data according to the vibration time domain data.

Referring to fig. 7, it is worth particularly describing that step 71 further includes steps 710 to 714.

In step 710, the processing module 14 performs Envelope processing (Envelope) according to the vibration time domain data to obtain the processed vibration time domain data. The envelope processing is not the focus of the prior art and is not described in detail.

In step 711, the processing module 14 obtains a target vibration time domain data related to the uniform velocity period when the ball screw 4 moves according to the processed vibration time domain data. Further, the period of uniform speed during the movement of the ball screw 4 can also be obtained from the preset rotation speed of the motor controlling the movement of the ball screw 4.

In step 712, the processing module 14 obtains at least one vibrator time domain data according to the target vibration time domain data. Further, the processing module 14 cuts the target time domain data of the vibrator according to a preset time interval with the same length to obtain the time domain data of the vibrator, where each time domain data of the vibrator corresponds to the time interval with the same length. In other embodiments, on the contrary, the respective uniform velocity segments may be obtained according to different processed vibration time domain data to obtain a plurality of target vibration time domain data, and the processing module 14 may also combine the target vibration time domain data according to a preset another time interval with the same length according to the plurality of target vibration time domain data to obtain at least one other vibrator time domain data, where each other vibrator time domain data corresponds to another time interval with the same length.

In step 713, for each vibrator time domain data, the processing module 14 performs Band Pass filtering (Band Pass Filter) according to the vibrator time domain data to obtain the filtered vibrator time domain data. Further, the filtered time domain data of the vibrator has a frequency range within 10 times of the rotational frequency of the screw shaft 42, so that when the frequency range sensed by the first sensing unit 2 just covers 10 times of the rotational frequency of the screw shaft 42, the step 714 is directly performed without performing the band-pass filtering described in step 713.

In step 714, for each of the filtered vibrator time domain data, the processing module 14 performs Fourier Transform (Fourier Transform) according to the filtered vibrator time domain data to obtain the vibrator frequency domain data corresponding to the filtered vibrator time domain data.

In step 72, for each vibrator frequency domain data, the processing module 14 obtains a vibration feature vector associated with the vibrator frequency domain data according to the vibrator frequency domain data. Wherein each vibration feature vector includes at least one of a kurtosis feature vector indicating kurtosis of the vibrator frequency domain data corresponding to the vibration feature vector, a maximum frequency domain peak feature vector indicating a maximum peak of the vibrator frequency domain data corresponding to the vibration feature vector, and a total energy feature vector indicating a total energy of the vibrator frequency domain data corresponding to the vibration feature vector, but not limited to the above.

In step 73, for each vibration feature vector, the processing module 14 calculates a second vibration candidate distance between the vibration feature vector and each vibration reference vector.

In step 74, for each vibration feature vector having obtained the second vibration candidate distance, the processing module 14 obtains a corresponding second vibration target distance with the shortest distance from the second vibration candidate distances corresponding to the vibration feature vectors.

In step 75, the processing module 14 obtains the pre-pressure determination result according to the second vibration target distance and the pre-pressure detection range for determining whether the vibration feature vector indicates that the pre-pressure of the ball screw 4 has already decreased. For example, the processing module 14 averages the second vibration target distance to obtain a vibration average, and determines whether the vibration average is within the pre-pressure detection range as the pre-pressure determination result; or, a mode is selected according to the second vibration target distance to obtain a vibration mode, and whether the vibration mode is within the pre-pressure detection range is determined as the pre-pressure determination result, but not limited to the above algorithm.

In step 76, the processing module 14 determines whether the preload of the ball screw 4 has been degraded according to the preload determination result. When it is determined that the preload of the ball screw 4 is not degraded (for example, the vibration average number is within the preload detection range), the flow proceeds to step 77; when it is determined that the preload of the ball screw 4 has decayed (e.g., the vibration mean number is outside the preload detection range), the process proceeds to step 78.

In step 77, the processing module 14 generates a pre-pressure non-recession message indicating that the pre-pressure of the ball screw 4 is not recession, and displays the pre-pressure non-recession message on the display module 13.

In step 78, the processing module 14 obtains at least one inertial force time domain data corresponding to the at least one inertial force signal according to the received at least one inertial force signal. Each time domain data of the inertia force corresponds to a reciprocating period of the nut 41 of the ball screw 4 relative to the screw shaft 42, and each reciprocating period refers to a period of the nut 41 moving from an initial position to an end position of the screw shaft 42 and then returning from the end position to the initial position. Preferably, each inertia force time domain data may also only cover the acceleration (deceleration) period of the corresponding round trip cycle of the ball screw 4.

In step 79, for each time domain data of the inertial force, the processing module 14 obtains an inertial force feature vector associated with the time domain data of the inertial force according to the time domain data of the inertial force. Each inertia force feature vector includes at least one of a peak-to-peak feature vector indicating a peak-to-peak value of the inertia force time domain data corresponding to the inertia force feature vector, a maximum time domain peak feature vector indicating a maximum peak value of the inertia force time domain data corresponding to the inertia force feature vector, and an average peak feature vector indicating an average of a sum of an absolute value of the maximum peak value and an absolute value of the minimum peak value of the inertia force time domain data corresponding to the inertia force feature vector, but not limited to the above.

Referring to FIG. 8, it is worth noting that step 79 further comprises steps 790-792.

In step 790, for each inertia force time domain data, the processing module 14 performs envelope processing according to the inertia force time domain data to obtain the processed inertia force time domain data.

In step 791, for each processed time domain data of the inertial force, the processing module 14 performs Low-pass filtering (Low-pass Filter) according to the processed time domain data of the inertial force, so as to obtain a filtered time domain data of the inertial force corresponding to the processed time domain data of the inertial force. Further, the frequency range reserved by the filtered inertia force time domain data is 0.1 to 5Hz bandwidth, so that when the frequency range sensed by the second sensing unit 3 just covers 0.1 to 5Hz bandwidth, the step 792 is directly performed without performing the low-pass filtering described in the step 791.

In step 792, for each filtered time domain data of the inertial force, the processing module 14 obtains the feature vector of the inertial force corresponding to the filtered time domain data of the inertial force according to the filtered time domain data of the inertial force.

In step 80, for each inertia force feature vector, the processing module 14 calculates a second inertia force candidate distance between the inertia force feature vector and each inertia force reference vector.

In step 81, for each inertia force feature vector having obtained the second inertia force candidate distance, the processing module 14 obtains a second inertia force target distance corresponding to the shortest distance from the inertia force candidate distances corresponding to the inertia force feature vector.

In step 82, the processing module 14 obtains the backlash determination result according to the second inertia force target distance and the backlash detection range for determining whether the inertia force characteristic vector indicates that the ball screw 4 has generated backlash. For example, the processing module 14 averages the second inertia force target distance to obtain an inertia force average, and determines whether the inertia force average is within the backlash detection range as the backlash determination result; or, a mode is selected according to the second inertia force target distance to obtain an inertia force mode, and whether the inertia force mode is located in the back clearance detection range is determined as the back clearance determination result, but not limited to the above algorithm.

In step 83, the processing module 14 determines whether the ball screw 4 has generated backlash according to the backlash determination result. When it is determined that the ball screw 4 does not generate backlash (e.g., the average number of the inertial forces is within the backlash detection range), the process proceeds to step 84; when it is determined that the ball screw 4 has generated backlash (e.g., the inertia force average number is outside the backlash detection range), the flow proceeds to step 85.

In step 84, the processing module 14 generates a preload-only recession message indicating that the preload of the ball screw 4 has been degraded but no backlash has occurred, and displays the preload-only recession message on the display module 13.

In step 85, the processing module 14 generates a backlash occurred message indicating that backlash has occurred in the ball screw 4, and displays the backlash occurred message on the display module 13.

In summary, the method for determining the pre-pressure recession of the ball screw according to the present invention can detect three situations, i.e., the pre-pressure of the ball screw 4 is not recession, is only recession but not generating a backlash, or the backlash is generated, by the pre-pressure determination result for determining whether the pre-pressure of the ball screw 4 has been recession or not and the backlash determination result for determining whether the backlash has been generated or not, so that the object of the present invention can be achieved.

However, the above description is only an example of the present invention, and the scope of the present invention should not be limited thereby, and all simple equivalent changes and modifications made according to the claims and the contents of the patent specification are still included in the scope of the present invention.