A motorized spindle health state evaluation method, system and computer device

Abstract

The application discloses a kind of electric spindle health state evaluation method, system and computer equipment, it is related to the technical field of the failure prediction of mechanical equipment, the method comprises: the vibration data of the electric spindle is collected, and whether the electric spindle enters idle according to the vibration data is determined;When the electric spindle is in idle state, whether the electric spindle is abnormal according to the vibration data is determined;When the electric spindle is abnormal, the temperature data of the electric spindle is acquired, and evaluation index is generated according to the vibration data and the temperature data;The evaluation index is input into the preset health state evaluation model, and the health state of the electric spindle is obtained.The application has the effect of improving the evaluation precision of the health state of electric spindle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault prediction of mechanical equipment, and in particular to a motorized spindle health state evaluation method and system and computer equipment. BACKGROUND

[0002] The main shaft generally refers to the shaft that drives the workpiece or tool to rotate on the machine tool, and is usually composed of a main shaft, a bearing and a transmission part (a gear or a pulley) to form a main shaft component. In the machine, the main shaft is mainly used to support transmission parts such as gears and pulleys. The motorized spindle, as a core functional component of the numerical control machine tool, has a great influence on the machining precision, machining efficiency and production cost of the numerical control machine tool.

[0003] In the life cycle of the numerical control machine tool, the health state of the motorized spindle needs to be monitored. In the related technology, when the health state of the motorized spindle needs to be evaluated, the machine tool is usually stopped, and then the current data of the motorized spindle is collected. The collected motorized spindle data is analyzed according to the past motorized spindle degradation data and fault data to obtain the current health state of the motorized spindle. However, the motorized spindle degradation data is relatively complex, and the motorized spindle degradation data and fault data are difficult to accurately obtain, resulting in low evaluation accuracy and inaccurate evaluation results. In addition, during the evaluation process, the machine tool is difficult to perform production work, thereby reducing the production efficiency. SUMMARY

[0004] In order to improve the evaluation accuracy of the health state of the motorized spindle, the present application provides a motorized spindle health state evaluation method, system and computer equipment.

[0005] In a first aspect, the present application provides a motorized spindle health state evaluation method, which adopts the following technical solution:

[0006] A motorized spindle health state evaluation method, comprising:

[0007] Collecting vibration data of the motorized spindle, and determining whether the motorized spindle enters an idle state according to the vibration data;

[0008] When the motorized spindle is in the idle state, determining whether the motorized spindle has an abnormality according to the vibration data;

[0009] When the motorized spindle has an abnormality, acquiring temperature data of the motorized spindle, and generating an evaluation index according to the vibration data and the temperature data;

[0010] Inputting the evaluation index into a preset health state evaluation model to obtain the health state of the motorized spindle.

[0011] By adopting the above technical solution, vibration data of the electric spindle is collected during its rotation. Based on this vibration data, it is determined whether the electric spindle has entered an idling state. If the electric spindle is determined to be in an idling state, the vibration data is used to determine whether an abnormality has occurred. When an abnormality occurs, the temperature data of the electric spindle is acquired, and an evaluation index is generated based on the vibration and temperature data. This evaluation index is then input into a preset health status evaluation model to obtain the health status of the electric spindle. Compared to the evaluation methods in the prior art, this application can collect vibration and temperature data in real time. The data acquisition method is easier and the data reliability is higher. Furthermore, using vibration and temperature data from the electric spindle in an idling state to evaluate the health status of the electric spindle reduces the inaccuracy of data acquired during operation, thereby improving evaluation accuracy. The machine tool can still perform production work during the evaluation process, improving production efficiency.

[0012] Optionally, determining whether the electric spindle has entered idle mode based on the vibration data specifically includes:

[0013] The vibration data is subjected to spectral analysis to obtain first spectral data and second spectral data; the first spectral data is the spectral data from the 1st to the (t-1st)th sampling time, and the second spectral data is the spectral data from the 1st to the tth sampling time.

[0014] D is determined based on the first spectrum data. max (t-1), D is determined based on the second spectrum data. max (t); the D max (t-1) represents the maximum amplitude value among the sampling times from the 1st to the (t-1st)th time, where D... max (t) represents the maximum amplitude value from the 1st to the tth sampling time;

[0015] D(t-1) is determined based on the first spectrum data, and D(t) is determined based on the second spectrum data; D(t-1) is the amplitude of the preset optimal switching frequency at the first to the (t-1)th sampling time, and D(t) is the amplitude of the preset optimal switching frequency at the first to the tth sampling time.

[0016] Determine whether D(t-1) is greater than D max (t-1); if so,

[0017] Then determine whether D(t) is greater than D max (t), if so, then the electric spindle is determined to be idling.

[0018] By adopting the technical scheme, in order to determine whether the motorized spindle enters idle running, first, the vibration data of the motorized spindle is subjected to frequency spectrum analysis to obtain first frequency spectrum data and second frequency spectrum data, the first frequency spectrum data is frequency spectrum data of the 1st to (t-1)th sampling time, and the second frequency spectrum data is frequency spectrum data of the 1st to (t)th sampling time; then, D(t-1) is determined according to the first frequency spectrum data, and D(t) is determined according to the second frequency spectrum data, D(t-1) is a maximum amplitude in the 1st to (t-1)th sampling time, and D(t) is a maximum amplitude in the 1st to (t)th sampling time; then, D(t-1) is determined according to the first frequency spectrum data, and D(t) is determined according to the second frequency spectrum data, D(t-1) is a maximum amplitude in the 1st to (t-1)th sampling time, and D(t) is a maximum amplitude in the 1st to (t)th sampling time; then, it is judged whether D(t-1) is greater than D(t-1), when D(t-1) is greater than D(t-1), it is continued to be judged whether D(t) is greater than D(t), if D(t) is greater than D(t), it is determined that the motorized spindle enters idle running. max max max max max max max max

[0019] Optionally, the determining whether the motorized spindle is abnormal according to the vibration data comprises:

[0020] calculating RMS(t), RMS(t-1), RMS(t-2), Kurtosis(t), Kurtosis(t-1), Kurtosis(t-2) according to the vibration data, RMS(t) is a vibration effective value of the tth sampling time, and Kurtosis(t) is kurtosis of the tth sampling time;

[0021] constructing a matrix RMS=[RMS(1), RMS(2), …, RMS(t)] and a matrix Kurtosis=[Kurtosis(1), Kurtosis(2), …, Kurtosis(t)];

[0022] calculating an average value u1, a standard deviation σ1 of the matrix RMS and an average value u2, a standard deviation σ2 of the matrix Kurtosis;

[0023] calculating a normal value range [μ1-3σ1, μ1+3σ1] of the matrix RMS and a normal value range [μ2-3σ2, μ2+3σ2] of the matrix Kurtosis according to the u1, the σ1, the u2 and the σ2;

[0024] ​​​​​​​​determining whether the RMS(t-2), the RMS(t-1) and the RMS(t) are all within the [μ1-3σ1, μ1+3σ1]; if not,

[0025] then determining whether the Kurtosis(t-2), the Kurtosis(t-1) and the Kurtosis(t) are within the [μ2-3σ2, μ2+3σ2]; if not, determining that the electric spindle is abnormal, and taking the tth sampling time as the starting point of the health state evaluation of the electric spindle.

[0026] By adopting the technical scheme, in order to determine whether the electric spindle is abnormal, RMS(t), RMS(t-1), RMS(t-2), Kurtosis(t), Kurtosis(t-1) and Kurtosis(t-2) are calculated according to the vibration data of the electric spindle, and a matrix RMS and a matrix Kurtosis are constructed, then the average value u1 and the standard deviation σ1 of the matrix RMS and the average value u2 and the standard deviation σ2 of the matrix Kurtosis are calculated, and then the normal value range [μ1-3σ1, μ1+3σ1] of the matrix RMS and the normal value range [μ2-3σ2, μ2+3σ2] of the matrix Kurtosis are calculated, and finally it is determined whether the RMS(t-2), the RMS(t-1) and the RMS(t) are all within the [μ1-3σ1, μ1+3σ1] and whether the Kurtosis(t-2), the Kurtosis(t-1) and the Kurtosis(t) are within the [μ2-3σ2, μ2+3σ2], if not within the corresponding normal value range, it is determined that the electric spindle is abnormal, and the tth sampling time is taken as the starting point of the health state evaluation of the electric spindle.

[0027] Optionally, the evaluation index is generated according to the vibration data and the temperature data, specifically including:

[0028] inputting the vibration data into a preset vibration evaluation model to obtain a first evaluation value;

[0029] inputting the temperature data into a preset temperature evaluation model to obtain a second evaluation value;

[0030] inputting the first evaluation value and the second evaluation value into a preset evaluation index generation model to obtain the evaluation index.

[0031] By adopting the technical scheme, in order to generate the evaluation index, the vibration data of the motorized spindle is input into a preset vibration evaluation model to obtain a first evaluation value, and the temperature data is input into a preset temperature evaluation model to obtain a second evaluation value, and the first evaluation value and the second evaluation value are input into a preset evaluation index generation model to obtain the evaluation index. The size of the evaluation index can reflect the degradation and wear of the motorized spindle, and compared with a single temperature index or vibration index, the current health status of the motorized spindle can be more accurately reflected, so that the subsequent evaluation of the health status of the motorized spindle is more accurate.

[0032] Optionally, the preset vibration evaluation model is:

[0033] y vib = ax vib + bx vib + c 2 vib vib vib

[0034] wherein x vib is an RMS value at any moment;

[0035] The preset temperature evaluation model is:

[0036] y tem = ax tem + bx tem + c 2 tem tem tem

[0037] wherein x tem is a temperature at any moment;

[0038] The preset evaluation index generation model is:

[0039] HI = x1y vib + x2y vib

[0040] wherein x1 and x2 are weights.

[0041] Optionally, the health status evaluation model is:

[0042]

[0043] wherein HI (minor abnormality) = x1y vib + x2y vib , and HI (t) represents the evaluation index at the tth sampling moment.

[0044] ​​​​Optionally, before the vibration data of the motorized spindle is collected and it is determined whether the motorized spindle enters idle running according to the vibration data, the method further comprises:

[0045] A set of common rotating speeds of the motorized spindle is obtained, and the set of common rotating speeds is input into a preset idle running rotating speed selection model to obtain an optimal idle running rotating speed.

[0046] The optimal idle running rotating speed is used to determine the optimal rotating frequency.

[0047] By using the above technical solution, in order to determine the optimal rotating frequency, a set of common rotating speeds of the motorized spindle is obtained, and the set of common rotating speeds is input into a preset idle running rotating speed selection model to obtain an optimal idle running rotating speed, and then the optimal idle running rotating speed is used to determine the optimal rotating frequency.

[0048] In a second aspect, the application further provides a motorized spindle health state evaluation system, which adopts the following technical solution:

[0049] A motorized spindle health state evaluation system comprises:

[0050] An idle running judgment module is configured to collect vibration data of the motorized spindle and determine whether the motorized spindle enters idle running according to the vibration data.

[0051] An abnormality detection module is configured to determine whether the motorized spindle is abnormal according to the vibration data when the motorized spindle is in an idle running state.

[0052] An evaluation index generation module is configured to obtain temperature data of the motorized spindle when the motorized spindle is abnormal, and generate an evaluation index according to the vibration data and the temperature data.

[0053] A health state generation module is configured to input the evaluation index into a preset health state evaluation model to obtain a health state of the motorized spindle.

[0054] In a third aspect, the application further provides a computer device, which adopts the following technical solution:

[0055] A computer device comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor executes the computer program to perform any one of the above methods.

[0056] In summary, the present application at least has the following beneficial technical effects: in the process of rotating the motorized spindle, the vibration data of the motorized spindle is collected, and whether the motorized spindle enters idle state is determined according to the vibration data of the motorized spindle, if the motorized spindle is determined to be in idle state, whether the motorized spindle is abnormal is determined according to the vibration data of the motorized spindle, when the motorized spindle is abnormal, the temperature data of the motorized spindle is obtained, and the evaluation index is generated according to the vibration data and the temperature data of the motorized spindle, then the evaluation index is input into the preset health state evaluation model, so as to obtain the health state of the motorized spindle; compared with the evaluation method in the background art, the present application can collect vibration data and temperature data in real time, the data acquisition method is easier and the data reliability is higher, and the vibration data and the temperature data of the motorized spindle in idle state are used to evaluate the health state of the motorized spindle, which reduces the inaccuracy of the data obtained in the working state of the motorized spindle, thereby improving the evaluation accuracy, and the machine tool can still work in production during the evaluation process, thereby improving the production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a whole flowchart of the method of the embodiment of the present application.

[0058] Figure 2 is a flowchart of determining whether the motorized spindle enters idle state.

[0059] Figure 3 is a flowchart of determining whether the motorized spindle is abnormal.

[0060] Figure 4 is a flowchart of obtaining the evaluation index.

[0061] Figure 5 is a flowchart of determining the preset optimal rotation frequency.

[0062] Figure 6 is a whole structure diagram of the system of the embodiment of the present application.

[0063] Figure 7 is a structure block diagram of the computer device of the embodiment of the present application.

[0064] BRIEF DESCRIPTION OF DRAWINGS: 61, idle state determining module; 62, abnormality detecting module; 63, evaluation index generating module; 64, health state generating module; 71, memory; 72, processor. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the present application more clear, the following will combine the attached drawings to make the specific description. Figures 1-7The application is further described in detail with reference to the embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the application and are not intended to limit the application.

[0066] The application discloses a health state evaluation method of an electric spindle.

[0067] With reference to Figure 1 The application discloses a health state evaluation method of an electric spindle.

[0068] In step S11, vibration data of the electric spindle is collected, and whether the electric spindle enters an idle state is determined according to the vibration data.

[0069] Specifically, the vibration data of the electric spindle is collected in real time, and whether the electric spindle enters an idle state is determined according to the vibration data of the electric spindle. If the electric spindle enters the idle state, step S12 is performed; if the electric spindle does not enter the idle state, the collection is continued.

[0070] It should be noted that the vibration data of the electric spindle is usually collected by installing a vibration sensor on the electric spindle. In the process of active rotation, the front end shaft of the electric spindle usually bears greater force than the rear end shaft of the electric spindle, so the vibration data of the front end shaft of the electric spindle can better reflect the current state of the electric spindle. Therefore, in the application, the vibration sensor is installed on the front end shaft of the electric spindle, and then the analog signal collected by the vibration sensor is converted into a digital signal by a collection card, and the digital signal is forwarded to a data processing device.

[0071] It can be understood that the working state of the electric spindle usually includes two states of cutting and idling. The cutting state is a state in which a cutter driven by the electric spindle cuts a part, and the idling state is a state in which the cutter driven by the electric spindle is in idling. When the electric spindle is in the cutting state, the radial force borne by the electric spindle changes according to the changes of cutting parameters such as cutting depth and feed rate, so the vibration parameters of the electric spindle in the cutting state are difficult to accurately reflect the degradation of the electric spindle relative to the idling state.

[0072] In step S12, whether the electric spindle is abnormal is determined according to the vibration data when the electric spindle is in the idling state.

[0073] Specifically, when the electric spindle is in the idling state, whether the electric spindle is abnormal is determined according to the vibration data of the electric spindle. If the electric spindle is abnormal, step S13 is performed; if the electric spindle is not abnormal, step S11 is performed.

[0074] It can be understood that when the rotation of the electric spindle is abnormal, the vibration parameters of the electric spindle change, and the change of the vibration parameters of the electric spindle can determine whether the rotation of the electric spindle is abnormal.

[0075] Step S13, when the rotation of the motorized spindle is abnormal, the temperature data of the motorized spindle is acquired, and the evaluation index is generated according to the vibration data and the temperature data.

[0076] Specifically, when the rotation of the motorized spindle is abnormal, the temperature data of the motorized spindle is acquired, and the evaluation index is generated according to the vibration data of the motorized spindle and the temperature data of the motorized spindle.

[0077] It can be understood that when the rotation of the motorized spindle is abnormal, the temperature and vibration parameters of the motorized spindle will generally be abnormal, for example, the temperature of the motorized spindle increases, the amplitude of the vibration of the motorized spindle decreases, etc.

[0078] Step S14, inputting the evaluation index into a preset health state evaluation model to obtain the health state of the motorized spindle.

[0079] It should be noted that in the present application, the health state of the motorized spindle includes three states of health, slight abnormality and serious abnormality.

[0080] In the above embodiment, during the rotation of the motorized spindle, the vibration data of the motorized spindle is collected, and whether the motorized spindle enters idle state is determined according to the vibration data of the motorized spindle. If it is determined that the motorized spindle is in idle state, whether the motorized spindle is abnormal is determined according to the vibration data of the motorized spindle. When the motorized spindle is abnormal, the temperature data of the motorized spindle is acquired, and the evaluation index is generated according to the vibration data and the temperature data of the motorized spindle. Then the evaluation index is input into a preset health state evaluation model, so as to obtain the health state of the motorized spindle. Compared with the evaluation method in the background art, the vibration data and the temperature data can be collected in real time in the present application, the data acquisition method is easier and the data reliability is higher. Moreover, the vibration data and the temperature data of the motorized spindle in idle state are used to evaluate the health state of the motorized spindle, which reduces the inaccuracy of the data acquired in the working state of the motorized spindle, thereby improving the evaluation accuracy. Moreover, the machine tool can still work during the evaluation process, thereby improving the production efficiency.

[0081] Reference Figure 2 As a further embodiment of step 12, whether the motorized spindle enters idle state is determined according to the vibration data, specifically including the following steps:

[0082] Step S21, performing frequency spectrum analysis on the vibration data to obtain first frequency spectrum data and second frequency spectrum data.

[0083] Among them, the first frequency spectrum data is the frequency spectrum data of the first to the t-1 sampling time, and the second frequency spectrum data is the frequency spectrum data of the first to the t sampling time.

[0084] It can be understood that the spectral analysis is a technique of decomposing a complex signal into simpler signals, and a physical signal can be expressed as a sum of many different frequency simple signals, and the method of finding the information (such as amplitude, power, intensity or phase) of a signal at different frequencies is spectral analysis.

[0085] It should be noted that the time length between each adjacent two sampling moments should be consistent; assuming that each 0.5s is a sampling moment, if the current moment is 2s, the 0s, 0.5s, 1s, 1.5s and 2s are all sampling moments.

[0086] Step S22, determining D max (t-1) according to the first spectral data and determining D max (t) according to the second spectral data.

[0087] Wherein, D max (t-1) is the maximum amplitude in the first to the t-1th sampling moment, and D max (t) is the maximum amplitude in the first to the tth sampling moment.

[0088] It can be understood that assuming that t is 5 and each sampling moment is separated by 0.5s, the first sampling moment, the second sampling moment, the third sampling moment, the fourth sampling moment and the fifth sampling moment correspond to the 0s, the 0.5s, the 1s, the 1.5s and the 2s respectively, D max (t-1) is the maximum amplitude of all amplitudes corresponding to the first sampling moment to the fourth sampling moment, and D max (t) is the maximum amplitude of all amplitudes corresponding to the first sampling moment to the fifth sampling moment.

[0089] Step S23, determining D(t-1) according to the first spectral data and determining D(t) according to the second spectral data.

[0090] Wherein, D(t-1) is the amplitude corresponding to the preset optimal frequency in the first to the t-1th sampling moment, and D(t) is the amplitude corresponding to the preset optimal frequency in the first to the tth sampling moment.

[0091] It should be noted that the optimal frequency is the frequency corresponding to the optimal idle speed determined according to the common speed of the motorized spindle, and under the optimal frequency, the recognition accuracy of the motorized spindle idle state is higher.

[0092] Step S24, judging whether D(t-1) is greater than D max (t-1); if yes.

[0093] Specifically, judging whether D(t-1) is greater than D max (t-1), if D(t-1) is greater than Dmax If (t-1), then proceed to step S25.

[0094] Step S25, determine whether D(t) is greater than D max If (t), then the electric spindle is determined to be idling.

[0095] It should be noted that in actual production, due to a slight deviation between the actual rotational speed of the electric spindle and the optimal idle speed set by the command, the actual rotational frequency of the spindle deviates from the theoretical rotational frequency (i.e., the optimal rotational frequency). Therefore, in steps S23, S24, and S25, the rotational frequency error f should be considered. & For example, the frequency shift error can be set to 10Hz.

[0096] In the above embodiment, to determine whether the electric spindle has entered idle mode, the vibration data of the electric spindle is first subjected to spectral analysis to obtain first spectral data and second spectral data. The first spectral data is the spectral data from the 1st to the (t-1th)th sampling time, and the second spectral data is the spectral data from the 1st to the tth sampling time. Then, D is determined based on the first spectral data. max (t-1), and determine D based on the second spectrum data. max (t), D max (t-1) represents the maximum amplitude value from the 1st to the (t-1th)th sampling time, D max (t) represents the maximum amplitude value from the 1st to the tth sampling time; then, D(t-1) is determined based on the first spectrum data, and D(t) is determined based on the second spectrum data. D(t-1) is the amplitude value corresponding to the preset optimal switching frequency from the 1st to the (t-1)th sampling time, and D(t) is the amplitude value corresponding to the preset optimal switching frequency from the 1st to the tth sampling time; then, it is determined whether D(t-1) is greater than D max (t-1), when D(t-1) is greater than D max When (t-1), continue to check whether D(t) is greater than D. max (t), if D(t) is greater than D max If (t), then the electric spindle is determined to be idling.

[0097] refer to Figure 3 As a further implementation of step S12, determining whether the electric spindle is malfunctioning based on vibration data specifically includes the following steps:

[0098] Step S31: Calculate RMS(t), RMS(t-1), RMS(t-2), Kurtosis(t), Kurtosis(t-1), and Kurtosis(t-2) based on the vibration data.

[0099] Wherein, RMS(t) is the effective value of vibration at the tth sampling time, Kurtosis(t) is the kurtosis at the tth sampling time.

[0100] It can be understood that RMS(t-1) is the effective value of vibration at the (t-1)th sampling time, RMS(t-2) is the effective value of vibration at the (t-2)th sampling time; Kurtosis(t-1) is the kurtosis at the tth sampling time, Kurtosis(t-2) is the kurtosis at the tth sampling time.

[0101] It should be noted that the effective value of vibration (i.e. RMS value) is used to represent the average energy or intensity of the vibration signal, and the kurtosis (i.e. Kurtosis) is used to detect whether the vibration signal deviates from the normal distribution, and the running state and possible fault type of the motorized spindle can be understood through the effective value of vibration and the kurtosis; in the present application, the abnormal state monitoring model will calculate the effective value of vibration and the kurtosis of the motorized spindle at the above-mentioned various sampling times according to the vibration data of the motorized spindle, and then judge whether the effective value of vibration and the kurtosis at the tth, (t-1)th and (t-2)th sampling times are all within the interval of 3σ according to the Relyda criterion, if the effective value of vibration and the kurtosis at the tth, (t-1)th and (t-2)th sampling times are not all within the interval of 3σ, it indicates that the rotation of the motorized spindle at the tth sampling time is abnormal, i.e. the rotation of the motorized spindle at the current time is abnormal.

[0102] Step S32, constructing a matrix RMS=[RMS(1), RMS(2), …, RMS(t)] and a matrix Kurtosis=[Kurtosis(1), Kurtosis(2), …, Kurtosis(t)].

[0103] Step S33, calculating the average value u1, the standard deviation σ1 of the matrix RMS and the average value u2, the standard deviation σ2 of the matrix Kurtosis.

[0104] Step S34, calculating the normal value range [μ1-3σ1, μ1+3σ1] of the matrix RMS and the normal value range [μ2-3σ2, μ2+3σ2] of the matrix Kurtosis according to u1, σ1, u2 and σ2.

[0105] Step S35, judging whether RMS(t-2), RMS(t-1) and RMS(t) are all within [μ1-3σ1, μ1+3σ1]; if not.

[0106] Specifically, it is judged whether RMS(t-2), RMS(t-1) and RMS(t) are all within [μ1-3σ1, μ1+3σ1]; if RMS(t-2), RMS(t-1) and RMS(t) are not all within [μ1-3σ1, μ1+3σ1], step S36 is executed.

[0107] Step S36, then determine whether Kurtosis(t-2), Kurtosis(t-1) and Kurtosis(t) are within [μ2-3σ2, μ2+3σ2], if not, determine that the electric spindle is abnormal, and take the tth sampling time as the starting point of the health state evaluation of the electric spindle.

[0108] In the above embodiment, in order to determine whether the electric spindle is abnormal, RMS(t), RMS(t-1), RMS(t-2), Kurtosis(t), Kurtosis(t-1), Kurtosis(t-2) are calculated according to the vibration data of the electric spindle, and matrix RMS and matrix Kurtosis are constructed, then the average value u1, the standard deviation σ1 of matrix RMS and the average value u2, the standard deviation σ2 of matrix Kurtosis are calculated, and then the normal value range [μ1-3σ1, μ1+3σ1] of matrix RMS and the normal value range [μ2-3σ2, μ2+3σ2] of matrix Kurtosis are calculated, and finally it is determined whether RMS(t-2), RMS(t-1) and RMS(t) are within [μ1-3σ1, μ1+3σ1] and whether Kurtosis(t-2), Kurtosis(t-1) and Kurtosis(t) are within [μ2-3σ2, μ2+3σ2], if not within the corresponding normal value range, it is determined that the electric spindle is abnormal, and the tth sampling time is taken as the starting point of the health state evaluation of the electric spindle.

[0109] Reference Figure 4 As a further embodiment of the electric spindle health state evaluation method, evaluation indexes are generated according to vibration data and temperature data, specifically including the following steps:

[0110] Step S41, input the vibration data into the preset vibration evaluation model to obtain a first evaluation value.

[0111] It should be noted that in actual production, the health of the electric spindle and the strength of the vibration of the electric spindle conform to the change trend of the quadratic function, and the health of the electric spindle and the temperature of the electric spindle conform to the change trend of the quadratic function, therefore the vibration evaluation model and the temperature evaluation model in step S32 are both established by quadratic functions.

[0112] It should be further noted that in the present application, the vibration evaluation model is:

[0113] y vib = a vib x vib 2 + b vib x vib + cvib

[0114] wherein y vib represents the first evaluation value, x vib is the RMS value at any moment; a vib , b vib , c vib are obtained by the ployfit function of MATLAB, a vib , b vib , c vib The parameters input into the ployfit function are (1, u1), (0.7, 3u1), (0, 6u1) respectively, and u1 is the average value of the matrix [RMS(1), RMS(2), …, RMS(t-2)].

[0115] In step S42, the temperature data is input into the preset temperature evaluation model to obtain a second evaluation value.

[0116] It should be noted that in the present application, the vibration evaluation model is:

[0117] y tem = a tem x tem 2 + b tem x tem + c tem

[0118] wherein y tem is the second evaluation value, x tem is the temperature at any moment; a tem , b tem , c tem are obtained by the ployfit function of MATLAB, a tem , b tem , c tem The parameters input into the ployfit function are (1, u2), (0.7, 3u2), (0, 6u2) respectively, and u2 is the average value of the matrix [Kurtosis(1), Kurtosis(2), …, Kurtosis(t-2)].

[0119] In step S43, the first evaluation value and the second evaluation value are input into the preset evaluation index generation model to obtain an evaluation index.

[0120] It should be noted that in the present application, the evaluation index generation model is:

[0121] HI = x1y vib + x2y tem

[0122] Wherein, HI represents the evaluation index, x1 and x2 are weights; x1 and x2 can be set according to actual conditions, for example, x1 and x2 are both set to 0.5.

[0123] In the above embodiment, in order to generate the evaluation index, the vibration data of the electric spindle is first input into the preset vibration evaluation model to obtain a first evaluation value, and the temperature data is input into the preset temperature evaluation model to obtain a second evaluation value, and the first evaluation value and the second evaluation value are input into the preset evaluation index generation model to obtain the evaluation index. The size of the evaluation index can reflect the degradation and wear of the electric spindle, and compared with a single temperature index or vibration index, the current health status of the electric spindle can be more accurately reflected, so that the subsequent evaluation of the health status of the electric spindle is more accurate.

[0124] Reference Figure 5 , as a further embodiment of the electric spindle health status evaluation method, before collecting the vibration data of the electric spindle and determining whether the electric spindle enters idle according to the vibration data, the method further comprises the following steps:

[0125] Step S51, obtaining a set of common speeds of the electric spindle, and inputting the set of common speeds into a preset idle speed selection model to obtain an optimal idle speed.

[0126] It should be noted that a type of machine tool can generally machine multiple types of parts, and the cutting parameters of the same type of part are different under different steps, so the common speeds of the electric spindle of the same type of machine tool are usually multiple, and each element in the set of common speeds represents the common speed of the electric spindle under a certain state, for example, the set of common speeds n nor = [n nor (1), n nor (2), …, n nor (n)], n nor (1), n nor (2), …, n nor (n) all represent the common speed of the electric spindle, and n represents the number of common speeds.

[0127] It should be further noted that in this embodiment, in order to more accurately identify whether the electric spindle is in an idle state, the optimal idle speed should have the maximum sum E of the differences from all common speeds, that is, the preset idle speed selection model is:

[0128] E = max(sum |n p -n nor (i)|), i = 1, 2, …, n

[0129] The model constraint condition is:

[0130]

[0131] wherein n p represents the rotational speed, n nor (i) represents an element in the common rotational speed set, f p represents the rotational speed n p corresponding frequency, n max represents the maximum rotational speed of the electric spindle.

[0132] Step S52, determining the preset optimal rotation frequency according to the optimal idle rotational speed.

[0133] It should be noted that after obtaining the optimal idle rotational speed, the spindle idle rotational speed, the tool, the idle time need to be solidified, and the spindle idle NC program is written:

[0134] T = "WARMUP";

[0135] M06;

[0136] M03Snp;

[0137] G4F180;

[0138] T0;

[0139] M06;

[0140] M17;

[0141] Wherein, WARMUP represents preheating the tool, Snp represents the spindle speed, F180 represents the running time of 180 seconds, M06, M03, G4, M17 and T0 are Siemens numerical control system 840Dsl instructions; This program is used as the last NC program of each part, and the idle program is automatically executed after the cutting operation is completed.

[0142] In the above embodiment, in order to determine the optimal rotation frequency, the common rotational speed set of the electric spindle is first obtained, and then the common rotational speed set is input into the preset idle rotational speed selection model to obtain the optimal idle rotational speed, and then the optimal idle rotational speed is used to determine the optimal rotation frequency.

[0143] The application also discloses an electric spindle health state evaluation system.

[0144] Reference Figure 6 An electric spindle health state evaluation system comprises:

[0145] An idle judgment module 61 is used for collecting vibration data of the electric spindle, and determining whether the electric spindle enters idle according to the vibration data;

[0146] An abnormality detection module 62 is used for determining whether the electric spindle is abnormal according to the vibration data when the electric spindle is in the idle state.

[0147] The evaluation index generation module 63 is configured to acquire temperature data of the motorized spindle when the motorized spindle is abnormal, and generate an evaluation index according to the vibration data and the temperature data;

[0148] The health state generation module 64 is configured to input the evaluation index into a preset health state evaluation model to obtain a health state of the motorized spindle.

[0149] The motorized spindle health state evaluation system of the present application can implement any one of the motorized spindle health state evaluation methods, and the specific working process of the motorized spindle health state evaluation system of the present application can refer to the corresponding process of the above motorized spindle health state evaluation methods.

[0150] The present application further discloses a computer device.

[0151] Reference Figure 7 A computer device includes a memory 71 and a processor 72, the memory 71 stores a computer program capable of running on the processor 72, and the processor 72 implements any one of the above motorized spindle health state evaluation methods when executing the computer program.

[0152] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application, any feature disclosed in the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features unless specifically described, that is, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for evaluating the health state of an electric spindle, characterized in that, The method comprises the following steps: Collecting vibration data of the motorized spindle and determining whether the motorized spindle enters idle state according to the vibration data; When the motorized spindle is in idle state, determining whether the motorized spindle is abnormal according to the vibration data; When the motorized spindle is abnormal, acquiring temperature data of the motorized spindle and generating an evaluation index according to the vibration data and the temperature data; Inputting the evaluation index into a preset health state evaluation model to obtain the health state of the motorized spindle; The determination of whether the motorized spindle enters idle state according to the vibration data comprises the following steps: Performing frequency spectrum analysis on the vibration data to obtain first frequency spectrum data and second frequency spectrum data; the first frequency spectrum data is the frequency spectrum data at the 1st to (t-1)th sampling time, and the second frequency spectrum data is the frequency spectrum data at the 1st to (t)th sampling time; D is determined based on the first spectrum data. max (t-1), D is determined based on the second spectrum data. max (t); the D max (t-1) represents the maximum amplitude value among the sampling times from the 1st to the (t-1st)th time, where D... max (t) represents the maximum amplitude value from the 1st to the tth sampling time; Determining D(t-1) according to the first frequency spectrum data and determining D(t) according to the second frequency spectrum data; D(t-1) is the amplitude corresponding to the preset optimal rotation frequency at the 1st to (t-1)th sampling time, and D(t) is the amplitude corresponding to the preset optimal rotation frequency at the 1st to (t)th sampling time; determining whether the D(t-1) is greater than the D(t-2); if so, max (t-1); if so, then determining whether the D(t) is greater than the D max (t), if so, determining that the electric spindle enters idling; The determination of whether the motorized spindle is abnormal according to the vibration data comprises the following steps: Calculating RMS(t), RMS(t-1), RMS(t-2), Kurtosis(t), Kurtosis(t-1), and Kurtosis(t-2) according to the vibration data; RMS(t) is the effective value of vibration at the (t)th sampling time, and Kurtosis(t) is the kurtosis at the (t)th sampling time; Constructing matrix RMS=[RMS(1), RMS(2), …, RMS(t)] and matrix Kurtosis=[Kurtosis(1), Kurtosis(2), …, Kurtosis(t)]; Calculating the average value u1 and the standard deviation σ1 of the matrix RMS and the average value u2 and the standard deviation σ2 of the matrix Kurtosis; Calculating the normal value range [μ1-3σ1, μ1+3σ1] of the matrix RMS and the normal value range [μ2-3σ2, μ2+3σ2] of the matrix Kurtosis according to the u1, the σ1, the u2, and the σ2; Determining whether the RMS(t-2), the RMS(t-1), and the RMS(t) are all within the [μ1-3σ1, μ1+3σ1]; if not, Determining whether the Kurtosis(t-2), the Kurtosis(t-1), and the Kurtosis(t) are all within the [μ2-3σ2, μ2+3σ2]; if not, it is determined that the motorized spindle is abnormal, and the (t)th sampling time is taken as the starting point of the health state evaluation of the motorized spindle; The generation of the evaluation index according to the vibration data and the temperature data comprises the following steps: Inputting the vibration data into a preset vibration evaluation model to obtain a first evaluation value; inputting the temperature data into a preset temperature evaluation model to obtain a second evaluation value; inputting the first evaluation value and the second evaluation value into a preset evaluation index generation model to obtain the evaluation index; the preset vibration evaluation model is: wherein, represents the first evaluation value, is the RMS value at any time; , , obtained by the ployfit function of MATLAB, , , The parameters input into the ployfit function are (1, u1), (0.7, 3u1), (0, 6u1) respectively, and u1 is the average value of the matrix [RMS(1), RMS(2), …, RMS(t-2)]. the preset temperature evaluation model is: wherein, is the second evaluation value, is the temperature at any time; , , obtained by the ployfit function of MATLAB, , , The parameters input into the ployfit function are (1, u2), (0.7, 3u2), (0, 6u2) respectively, and u2 is the average value of the matrix [Kurtosis(1), Kurtosis(2), …, Kurtosis(t-2)]. the preset evaluation index generation model is: wherein , and are weights; the health state evaluation model is: wherein , represents the evaluation index at the tth sampling time point; before the collecting the vibration data of the electric spindle and determining whether the electric spindle enters idle running according to the vibration data, the method further comprises: obtaining a common speed set of the electric spindle, inputting the common speed set into a preset idle running speed selection model to obtain an optimal idle running speed; determining the preset optimal frequency according to the optimal idle running speed; the preset idle running speed selection model is: the model constraint condition is: wherein denotes the rotational speed, denotes an element of a common rotational speed set, denotes the rotational speed the corresponding frequency, denotes the maximum rotational speed of the electric spindle.

2. An electrospindle health condition assessment system, characterized by, a device for implementing the method of claim 1, comprising: an idle running judgment module (61) for collecting the vibration data of the electric spindle and determining whether the electric spindle enters idle running according to the vibration data; an abnormality detection module (62) for determining whether the electric spindle is abnormal according to the vibration data when the electric spindle is in idle running state; an evaluation index generation module (63) for obtaining the temperature data of the electric spindle when the electric spindle is abnormal and generating an evaluation index according to the vibration data and the temperature data; a health state generation module (64) for inputting the evaluation index into a preset health state evaluation model to obtain the health state of the electric spindle.

3. A computer device, comprising: a device comprising a memory (71) and a processor (72), the memory (71) storing a computer program capable of running on the processor (72), and the processor (72) implements the method of claim 1 when executing the computer program.

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