Numerical control equipment state analysis method and device, electronic equipment and storage medium

Abstract

The application provides a numerical control equipment state analysis method and device, an electronic device and a storage medium, and relates to the technical field of equipment state monitoring. In the application, first, an equipment vibration state monitoring threshold is determined based on sample vibration characteristic values of sample spindle vibration data; second, a tool state monitoring threshold is determined based on sample tooth frequency characteristic values of the sample spindle vibration data; then, target vibration characteristic values of target spindle vibration data are compared and analyzed with the equipment vibration state monitoring threshold to obtain a first comparison and analysis result; further, if the first comparison and analysis result reflects that the state of the numerical control equipment to be analyzed is abnormal, target tooth frequency characteristic values of the target spindle vibration data are determined; finally, the target tooth frequency characteristic values are compared and analyzed with the tool state monitoring threshold to obtain a second comparison and analysis result. Based on the above, the problem that it is difficult to locate the abnormal cause of the equipment vibration abnormality of the numerical control equipment in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the field of equipment condition monitoring technology, and more specifically, to a method and apparatus for analyzing the condition of CNC equipment, electronic equipment, and storage medium. Background Technology

[0002] During the cutting of metal parts, significant equipment vibration can easily lead to surface chatter marks, affecting the surface quality of the parts. Abnormal spindle and tool conditions can both cause abnormal equipment vibration. Identifying and locating these vibration anomalies is crucial for mitigating the risk to part surface quality. However, current technologies primarily focus on monitoring vibration anomalies without addressing their location, making it difficult to pinpoint the root cause of surface quality problems and implement appropriate optimization measures. Therefore, there is an urgent need for a CNC equipment condition analysis method to promptly identify and locate abnormal vibration conditions, preventing part quality issues caused by excessive equipment vibration and improving equipment maintenance efficiency. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method and apparatus for analyzing the condition of CNC equipment, an electronic device and a storage medium, so as to improve the problem in the prior art that it is difficult to locate the cause of abnormal vibration of CNC equipment.

[0004] To achieve the above objectives, this application adopts the following technical solution: A method for analyzing the condition of CNC equipment, comprising: The sample vibration characteristic values ​​of the acquired sample spindle vibration data are determined, and based on the sample vibration characteristic values, the equipment vibration state monitoring threshold is determined, wherein the sample spindle vibration data is obtained by monitoring the spindle in normal state using a vibration acceleration sensor; The sample tooth frequency characteristic value of the sample spindle vibration data is determined, and the tool condition monitoring threshold is determined based on the sample tooth frequency characteristic value. The target vibration characteristic value of the acquired target spindle vibration data is determined, and the target vibration characteristic value is compared and analyzed with the equipment vibration state monitoring threshold to obtain a first comparison and analysis result. The target spindle vibration data is obtained by monitoring the spindle of the CNC equipment to be analyzed through a vibration acceleration sensor. If the first comparative analysis result reflects that the state of the CNC equipment to be analyzed is abnormal, then the target tooth frequency characteristic value of the target spindle vibration data is determined; The target tooth frequency feature value and the tool condition monitoring threshold are compared and analyzed to obtain a second comparison analysis result. The second comparison analysis result is used to reflect whether the abnormality of the CNC equipment to be analyzed is due to tool condition abnormality or spindle condition abnormality.

[0005] In a preferred embodiment of this application, the steps of determining the sample vibration characteristic values ​​of the acquired sample spindle vibration data and determining the equipment vibration state monitoring threshold based on the sample vibration characteristic values ​​in the above-described CNC equipment condition analysis method include: Multiple sample spindle vibration data were acquired; For each sample spindle vibration data in the plurality of sample spindle vibration data, the sample vibration characteristic value of the sample spindle vibration data is determined based on the number of vibration signals included in the sample spindle vibration data and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data. Based on the distribution of multiple sample vibration characteristic values ​​of the multiple sample spindle vibration data, the equipment vibration status monitoring threshold is determined.

[0006] In a preferred embodiment of this application, the step of acquiring multiple sample spindle vibration data in the above-mentioned CNC equipment condition analysis method includes: Multiple spindle vibration data were acquired. These multiple spindle vibration data were collected multiple times under the premise that the spindle vibration state was normal and under the same cutting conditions. The same cutting conditions refer to the same parts, tools, spindle speed, feed rate, cutting width and cutting depth in all conditions. Based on the multiple spindle vibration data, multiple sample spindle vibration data were determined.

[0007] In a preferred embodiment of this application, in the above-described CNC equipment condition analysis method, the step of determining the sample vibration characteristic value of each sample spindle vibration data point in the plurality of sample spindle vibration data points, based on the number of vibration signals included in the sample spindle vibration data point and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data point, includes: For each sample spindle vibration data in the plurality of sample spindle vibration data, based on the acquisition frequency and acquisition time corresponding to the spindle vibration data, the number of vibration signals included in the spindle vibration data is determined, and the squares of the signal values ​​of each vibration signal included in the spindle vibration data are summed to form the characterization parameters of the vibration energy corresponding to the sample spindle vibration data. The ratio between the number of vibration signals included in the spindle vibration data and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data is determined, and the ratio is exponentially calculated to obtain the sample vibration characteristic value of the sample spindle vibration data.

[0008] In a preferred embodiment of this application, the step of determining the equipment vibration status monitoring threshold based on the distribution of multiple sample vibration characteristic values ​​of the multiple sample spindle vibration data in the above-mentioned CNC equipment status analysis method includes: The mean value of the multiple sample vibration feature values ​​of the multiple sample spindle vibration data is calculated to obtain the sample vibration feature mean value. The standard deviation of the sample vibration feature value is calculated based on the multiple sample vibration feature values ​​and the sample vibration feature mean value to obtain the sample vibration feature standard deviation value. Based on the mean and standard deviation of the sample vibration characteristics, a monitoring threshold for equipment vibration status is determined. The monitoring threshold for equipment vibration status has a positive correlation with the mean of the sample vibration characteristics, and a negative correlation with the standard deviation of the sample vibration characteristics.

[0009] In a preferred embodiment of this application, the steps of determining the sample tooth frequency characteristic value of the sample spindle vibration data and determining the tool condition monitoring threshold based on the sample tooth frequency characteristic value in the above-described CNC equipment condition analysis method include: Based on the number of teeth of the tool corresponding to the sample spindle vibration data and the corresponding spindle rotation speed, the tooth frequency of the sample spindle vibration data is determined. Based on the fluctuation range of the tooth frequency in the sample spindle vibration data, a set of neighboring tooth frequencies is determined, wherein each frequency in the set of neighboring tooth frequencies is an integer; Based on the maximum amplitude value of each frequency in the set of adjacent frequencies of the tooth frequency in the sample spindle vibration data, the sample tooth frequency characteristic value of the sample spindle vibration data is obtained. Based on the sample tooth frequency feature values, a tool condition monitoring threshold is determined. When there are multiple sample spindle vibration data, there is a positive correlation between the tool condition monitoring threshold and the mean of the multiple sample tooth frequency feature values ​​corresponding to the multiple sample spindle vibration data.

[0010] In a preferred embodiment of this application, in the above-described CNC equipment condition analysis method, the step of obtaining the sample tooth frequency characteristic value of the sample spindle vibration data based on the maximum amplitude of each frequency in the set of adjacent tooth frequencies in the sample spindle vibration data includes: The sample principal axis vibration data is subjected to Fourier transform to form a sample principal axis vibration spectrum, wherein the sample principal axis vibration spectrum is used to reflect the amplitude at different frequencies; For each frequency in the set of adjacent frequencies of the tooth frequency, the amplitude corresponding to that frequency is determined in the spectrum diagram of the sample principal axis; Among the amplitudes corresponding to each frequency in the set of frequencies adjacent to the tooth frequency, the amplitude with the maximum value is determined, and this amplitude is used as the sample tooth frequency characteristic value of the sample spindle vibration data.

[0011] This application also provides a CNC equipment status analysis device, including: The first threshold determination module is used to determine the sample vibration characteristic value of the acquired sample spindle vibration data, and to determine the equipment vibration state monitoring threshold based on the sample vibration characteristic value, wherein the sample spindle vibration data is obtained by monitoring the spindle in normal state using a vibration acceleration sensor; The second threshold determination module is used to determine the sample tooth frequency characteristic value of the sample spindle vibration data, and to determine the tool condition monitoring threshold based on the sample tooth frequency characteristic value. The first comparative analysis module is used to determine the target vibration characteristic value of the acquired target spindle vibration data, and to compare and analyze the target vibration characteristic value with the equipment vibration state monitoring threshold to obtain a first comparative analysis result. The target spindle vibration data is obtained by monitoring the spindle of the CNC equipment to be analyzed through a vibration acceleration sensor. The tooth frequency characteristic determination module is used to determine the target tooth frequency characteristic value of the target spindle vibration data when the first comparison analysis result reflects an abnormal state of the CNC equipment to be analyzed. The second comparison and analysis module is used to compare and analyze the target tooth frequency feature value and the tool condition monitoring threshold to obtain a second comparison and analysis result. The second comparison and analysis result is used to reflect whether the abnormality of the CNC equipment to be analyzed is due to tool condition abnormality or spindle condition abnormality.

[0012] Based on the above, this application also provides an electronic device, including: Memory, used to store computer programs; A processor connected to the memory is used to execute the computer program stored in the memory to implement the above-described CNC equipment status analysis method.

[0013] Based on the above, this application also provides a computer-readable storage medium storing a computer program that executes the various steps of the above-described CNC equipment status analysis method when the computer program is run.

[0014] The CNC equipment condition analysis method, apparatus, electronic device, and storage medium provided in this application first determine the equipment vibration condition monitoring threshold based on the sample vibration characteristic values ​​of the sample spindle vibration data; secondly, determine the tool condition monitoring threshold based on the sample tooth frequency characteristic values ​​of the sample spindle vibration data; then, compare and analyze the target vibration characteristic values ​​of the target spindle vibration data with the equipment vibration condition monitoring thresholds to obtain a first comparison analysis result; further, if the first comparison analysis result reflects an abnormal state of the CNC equipment to be analyzed, then determine the target tooth frequency characteristic value of the target spindle vibration data; finally, compare and analyze the target tooth frequency characteristic value with the tool condition monitoring thresholds to obtain a second comparison analysis result. Based on the above, by comparing and analyzing the data in these two dimensions—vibration characteristic values ​​and tooth frequency characteristic values—the cause of the abnormal equipment vibration can be determined, and the cause of the abnormality can be located. Therefore, it can improve the problem of difficulty in locating the cause of abnormal equipment vibration in existing technologies. Attached Figure Description

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0016] Figure 1 A structural block diagram of an electronic device provided in an embodiment of this application.

[0017] Figure 2 This is a flowchart illustrating the CNC equipment status analysis method provided in an embodiment of this application.

[0018] Figure 3 A trend diagram showing the change of vibration characteristic values ​​of the part processed 17 times in the application embodiment.

[0019] Figure 4 The graph shows the trend of the maximum amplitude of the tooth frequency during 17 machining operations of the part provided in the application embodiment.

[0020] Figure 5 The spectrum diagram of vibration data collected during the first part processing provided in the application embodiment.

[0021] Figure 6 The spectrum diagram of vibration data collected during the second part processing provided in the application embodiment.

[0022] Figure 7 The spectrum diagram of vibration data collected during the third part processing provided in the application embodiment.

[0023] Figure 8 The spectrum of vibration data collected during the 11th part processing step is provided for the application embodiment.

[0024] Figure 9 A block diagram of the CNC equipment status analysis device provided in the embodiments of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] like Figure 1 As shown in the figure, this application provides an electronic device. The electronic device may include a memory, a processor, and a numerical control equipment status analysis device.

[0028] Specifically, the memory and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, the memory and the processor can be electrically connected via one or more communication buses or signal lines. The CNC equipment status analysis device includes at least one software functional module stored in the memory in the form of software or firmware. The processor is used to execute executable computer programs stored in the memory, such as the software functional modules and computer programs included in the CNC equipment status analysis device, to implement the CNC equipment status analysis method provided in this application embodiment.

[0029] Optionally, the memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0030] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0031] Understandable. Figure 1 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown may include, for example, a communication unit for exchanging information with other devices, such as sensors.

[0032] Combination Figure 2 This application also provides a CNC equipment status analysis method applicable to the aforementioned electronic equipment. The method steps defined in the process flow of the CNC equipment status analysis method can be implemented by the electronic equipment.

[0033] The following will be about Figure 2 The specific process shown will be explained in detail.

[0034] Step S110: Determine the sample vibration characteristic value of the acquired sample spindle vibration data, and determine the equipment vibration status monitoring threshold based on the sample vibration characteristic value.

[0035] In this embodiment, the electronic device can determine the sample vibration characteristic values ​​of the acquired sample spindle vibration data (i.e., reference spindle vibration data), and, based on the sample vibration characteristic values, determine the device vibration state monitoring threshold. The sample spindle vibration data can be obtained by monitoring the spindle in a normal state using a vibration acceleration sensor. For example, the vibration acceleration sensor can be placed near the front bearing of the spindle, and the output data of the vibration acceleration sensor, i.e., the sample spindle vibration data, can be acquired in real time using a data acquisition card. In other words, the state monitoring threshold for the first dimension can be determined based on the data from the normal state.

[0036] Step S120: Determine the sample tooth frequency characteristic value of the sample spindle vibration data, and determine the tool condition monitoring threshold based on the sample tooth frequency characteristic value.

[0037] In this embodiment, the electronic device can determine the sample tooth frequency characteristic value of the sample spindle vibration data, and, based on the sample tooth frequency characteristic value, determine the tool condition monitoring threshold. In other words, the second-dimensional condition monitoring threshold can be determined based on data from a normal state.

[0038] Step S130: Determine the target vibration characteristic value of the acquired target spindle vibration data, and compare and analyze the target vibration characteristic value with the equipment vibration state monitoring threshold to obtain the first comparison and analysis result.

[0039] In this embodiment, after obtaining the equipment vibration state monitoring threshold, the electronic device can determine the target vibration characteristic value of the acquired target spindle vibration data, and compare the target vibration characteristic value with the equipment vibration state monitoring threshold (for example, the magnitude relationship between the target vibration characteristic value and the equipment vibration state monitoring threshold can be determined) to obtain a first comparison analysis result. The target spindle vibration data is obtained by monitoring the spindle of the CNC equipment to be analyzed using a vibration acceleration sensor.

[0040] Step S140: If the first comparison analysis result reflects that the state of the CNC equipment to be analyzed is abnormal, then the target spindle vibration data is determined to have a target tooth frequency characteristic value.

[0041] In this embodiment, after obtaining the first comparative analysis result, if the first comparative analysis result reflects an abnormal state of the CNC equipment to be analyzed, the electronic device can determine the target tooth frequency characteristic value of the target spindle vibration data. It should be noted that if the first comparative analysis result reflects a normal state of the CNC equipment to be analyzed, the spindle of the CNC equipment to be analyzed can be monitored again using a vibration acceleration sensor to obtain new target spindle vibration data, and then step S130 can be executed again based on the new target spindle vibration data. In this way, continuous monitoring of the state of the CNC equipment to be analyzed can be achieved.

[0042] Step S150: Compare and analyze the target tooth frequency feature value and the tool condition monitoring threshold to obtain a second comparison and analysis result.

[0043] In this embodiment, after obtaining the target tooth frequency characteristic value, the electronic device can compare and analyze the target tooth frequency characteristic value and the tool condition monitoring threshold (for example, it can determine the magnitude relationship between the target tooth frequency characteristic value and the tool condition monitoring threshold) to obtain a second comparison analysis result. The second comparison analysis result reflects whether the abnormality of the CNC equipment to be analyzed is due to a tool condition abnormality or a spindle condition abnormality. That is, when it is determined that the state of the CNC equipment to be analyzed is abnormal (i.e., vibration abnormality) based on the target vibration characteristic value and the equipment vibration condition monitoring threshold, the specific cause of the abnormality can be further determined by relying on the target tooth frequency characteristic value and the tool condition monitoring threshold.

[0044] Based on the above, by comparing and analyzing the data from the two dimensions of vibration characteristic value and tooth frequency characteristic value, the cause of abnormal equipment vibration can be determined and the cause of the abnormality can be located. Therefore, this can improve the problem of difficulty in locating the cause of abnormal equipment vibration in CNC equipment in the existing technology.

[0045] Firstly, regarding step S110, it should be noted that the specific method for determining the equipment vibration status monitoring threshold is not limited and can be selected according to actual needs.

[0046] For example, in an alternative implementation, the equipment vibration status monitoring threshold can be determined based on the sample vibration characteristic value of a sample spindle vibration data. For example, the sample vibration characteristic value can be determined as the equipment vibration status monitoring threshold.

[0047] For example, in another alternative implementation, in order to improve the reliability of the determined equipment vibration state monitoring threshold and make the subsequent state analysis based on the equipment vibration state monitoring threshold more reliable, the above step S110 may further include steps S111, S112 and S113, the specific contents of each step are as follows.

[0048] Step S111: Obtain vibration data of multiple sample spindles.

[0049] In this embodiment, multiple sample spindle vibration data can be obtained. It should be noted that the multiple sample spindle vibration data can be obtained by monitoring the spindle of a single CNC machine, or by monitoring the spindles of multiple CNC machines.

[0050] Step S112: For each sample spindle vibration data in the plurality of sample spindle vibration data, based on the number of vibration signals included in the sample spindle vibration data and the characterization parameters of the vibration energy corresponding to the sample spindle vibration data, determine the sample vibration characteristic value of the sample spindle vibration data.

[0051] In this embodiment, after obtaining the plurality of sample spindle vibration data, for each sample spindle vibration data, a sample vibration characteristic value can be determined based on the number of vibration signals included in the sample spindle vibration data and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data. In other words, the corresponding characteristic value can be obtained from the two dimensions of the number of vibration signals and the vibration energy.

[0052] Step S113: Based on the distribution of multiple sample vibration characteristic values ​​of the multiple sample spindle vibration data, determine the equipment vibration status monitoring threshold.

[0053] In this embodiment, after determining the sample vibration characteristic values, the equipment vibration status monitoring threshold can be determined based on the distribution of the multiple sample vibration characteristic values ​​among the multiple sample spindle vibration data. That is, the multiple sample vibration characteristic values ​​can be fused to characterize the overall vibration characteristics of the multiple sample spindle vibration data, thereby obtaining a more reliable equipment vibration status monitoring threshold.

[0054] It is understood that the specific method for obtaining multiple sample spindle vibration data in step S111 above is not limited. For example, in an alternative implementation, to avoid introducing too many interference factors, step S111 above may further include the following: First, multiple spindle vibration data can be obtained. These multiple spindle vibration data are vibration data collected multiple times under the premise that the spindle vibration state is normal and under the same cutting conditions. The same cutting conditions mean that the parts, tools, spindle speed, feed rate, cutting width and cutting depth are the same in all conditions. Secondly, multiple sample spindle vibration data can be determined based on the multiple spindle vibration data. For example, the multiple spindle vibration data can be directly used as multiple sample spindle vibration data.

[0055] For example, a vibration acceleration sensor can be installed near the front bearing of the spindle, and a data acquisition card can be used to acquire spindle vibration data in real time. The vibration data acquisition frequency is F (Hz), meaning that F vibration data points are acquired per second. Furthermore, under the premise that the spindle vibration is normal, vibration data can be acquired three times under the same cutting conditions. The cutting conditions include the workpiece, spindle speed, tool, feed rate, cutting width, and depth of cut. Thus, the same cutting conditions can refer to all conditions where the workpiece, tool, spindle speed, feed rate, cutting width, and depth of cut are identical. Each continuous acquisition time is T seconds. Therefore, three vibration datasets, Vib1, Vib2, and Vib3, can be constructed, with the number of samples (number of vibration signal values) in each dataset being S = F × T.

[0056] It is understood that the specific method for determining the sample vibration characteristic values ​​in step S112 above is not limited. For example, in an alternative implementation, in order to ensure that the determined sample vibration characteristic values ​​can effectively characterize the vibration of the equipment, step S112 above may further include the following: First, for each sample of spindle vibration data in the plurality of sample spindle vibration data, the number of vibration signals included in the spindle vibration data can be determined based on the acquisition frequency and acquisition time corresponding to the spindle vibration data (as mentioned above, S=F×T). Then, the squares of the signal values ​​of each vibration signal included in the spindle vibration data are summed to form the characterization parameters of the vibration energy corresponding to the sample spindle vibration data, such as... , , , where x 1,i Let x represent the signal value of the vibration signal at the i-th sampling time of the vibration dataset Vib1. 2,i Let x represent the signal value of the vibration signal at the i-th sampling time of the vibration dataset Vib2. 3,i This represents the signal value of the vibration signal at the i-th sampling time of the vibration dataset Vib3; Secondly, the ratio between the number of vibration signals included in the spindle vibration data and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data can be determined, and the ratio can be exponentially calculated to obtain the sample vibration characteristic value of the sample spindle vibration data.

[0057] For example, for the vibration dataset Vib1 (the first sample of principal axis vibration data), the sample vibration feature values ​​can be: .

[0058] For example, for the vibration dataset Vib2 (the second sample of principal axis vibration data), the sample vibration feature values ​​can be: .

[0059] For example, for the vibration dataset Vib3 (the third sample of principal axis vibration data), the sample vibration feature values ​​can be: .

[0060] It is understood that the specific method for determining the equipment vibration status monitoring threshold in step S113 above is not limited. For example, in an alternative implementation, in order to fully fuse the vibration characteristics of multiple sample spindle vibration data and make the reliability of the obtained equipment vibration status monitoring threshold higher, step S113 above may further include the following: First, the mean of multiple sample vibration feature values ​​of the multiple sample spindle vibration data can be calculated to obtain the sample vibration feature mean. Then, the standard deviation can be calculated based on the multiple sample vibration feature values ​​and the sample vibration feature mean to obtain the sample vibration feature standard deviation. Secondly, the equipment vibration status monitoring threshold can be determined based on the mean value and standard deviation of the sample vibration characteristics. The equipment vibration status monitoring threshold has a positive correlation with the mean value of the sample vibration characteristics, and a negative correlation with the standard deviation of the sample vibration characteristics.

[0061] For example, the mean u and standard deviation of the sample vibration characteristic values ​​R1, R2, and R3 can be calculated. The formula for calculating the average value u can be: ; The formula for calculating the standard deviation δ is as follows: ; Based on this, the equipment vibration status monitoring threshold can be: VTh= - .

[0062] Secondly, regarding step S120, it should be noted that the method for determining the tool condition monitoring threshold is not limited and can be selected according to actual needs.

[0063] For example, in an alternative implementation, in order to ensure that the determined tool condition monitoring threshold has high reliability, the above step S120 may further include steps S121, S122, S123 and S124, the specific contents of each step are as follows.

[0064] Step S121: Based on the number of teeth of the tool corresponding to the sample spindle vibration data and the corresponding spindle rotation speed, determine the tooth frequency of the sample spindle vibration data.

[0065] In this embodiment, the tooth frequency of the sample spindle vibration data can be determined based on the number of teeth of the cutting tool corresponding to the sample spindle vibration data and the corresponding spindle speed. For example, if the number of teeth of the cutting tool is C and the spindle speed is S, then the tooth frequency CF can be: .

[0066] Step S122: Based on the fluctuation range of the tooth frequency in the sample spindle vibration data, determine the set of adjacent frequencies of the tooth frequency.

[0067] In this embodiment, after obtaining the tooth frequency of the sample spindle vibration data, a set of neighboring frequencies of the tooth frequency can be determined based on the fluctuation range of the tooth frequency. Each frequency in the set of neighboring frequencies is an integer. For example, a set of neighboring frequencies NF for the tooth frequency CF can be constructed. Since the amplitude of the tooth frequency may be smaller than the amplitude of its neighboring frequencies due to noise in the original signal, ±10% can be taken as the normal fluctuation range of the tooth frequency. And since each element in NF can be an integer, the minimum value of NF is NFmin = The maximum value is NFmax= ,in, This represents the smallest integer that is taken down from D. This represents the largest integer obtained by taking D upwards. Therefore, the nearest frequency set NF is: NF = [NFmin, NFmin+1, NFmin+2, ..., CF, ..., NFmax-2, NFmax-1, NFmax].

[0068] Step S123: Based on the maximum amplitude value of each frequency in the set of adjacent frequencies of the tooth frequency in the sample spindle vibration data, obtain the sample tooth frequency characteristic value of the sample spindle vibration data.

[0069] In this embodiment, after forming the set of neighboring frequencies of the tooth frequency, the sample tooth frequency characteristic value of the sample principal shaft vibration data can be obtained based on the maximum amplitude value corresponding to each frequency in the set of neighboring frequencies in the sample principal shaft vibration data. For example, Fourier transform is a method to convert a time-domain signal into a frequency-domain signal. The result of the Fourier transform can be represented by a spectrum graph, which is a graph that plots frequency and amplitude on a coordinate axis. The larger the amplitude, the stronger the energy at that frequency. By finding the main frequency components of the signal in the frequency domain through the spectrum graph, the maximum amplitude value can be determined, thereby obtaining the sample tooth frequency characteristic value.

[0070] Step S124: Determine the tool status monitoring threshold based on the sample tooth frequency feature value.

[0071] In this embodiment, after obtaining the sample tooth frequency feature values, a tool condition monitoring threshold can be determined based on these feature values. When multiple sample spindle vibration data are available, there is a positive correlation between the tool condition monitoring threshold and the mean values ​​of the multiple sample tooth frequency feature values ​​corresponding to the multiple sample spindle vibration data. For example, the average value of the maximum amplitude values ​​MAX(Vib1), MAX(Vib2), and MAX(Vib3) corresponding to the vibration datasets Vib1, Vib2, and Vib3 can be calculated, such as Average(CF): Average(CF) = mean(MAX(Vib1), MAX(Vib2), MAX(Vib3)), where mean(E) represents the average value of all elements in set E. The tool condition monitoring threshold is T. Th :T Th =2 Average (CF). Additionally, for example, when a sample spindle vibration data point is available, twice the value of the sample tooth frequency characteristic corresponding to that sample spindle vibration data point can be determined as the tool condition monitoring threshold.

[0072] It is understood that the specific method for obtaining the sample tooth frequency characteristic value of the sample spindle vibration data in step S123 above is not limited. For example, in an alternative embodiment, in order to determine reliable sample tooth frequency characteristic values, step S123 above may further include the following: First, the sample principal axis vibration data can be subjected to Fourier transform to form a sample principal axis vibration spectrum, wherein the sample principal axis vibration spectrum is used to reflect the amplitude at different frequencies; Secondly, for each frequency in the set of adjacent frequencies of the tooth frequency, the amplitude corresponding to that frequency can be determined in the spectrum diagram of the sample principal axis; Finally, the amplitude with the maximum value can be determined from the amplitude corresponding to each frequency in the set of adjacent frequencies of the tooth frequency, and this amplitude can be used as the sample tooth frequency characteristic value of the sample spindle vibration data.

[0073] For example, performing a Fourier transform on the vibration dataset Vib1 generates a spectrum, yielding the amplitudes at different frequencies. A set of amplitudes A(f) corresponding to the nearest frequency set is then constructed. i ) Vib1 Where A represents the frequency f on the spectrum of the vibration dataset Vib1. i The corresponding amplitude, i.e., A(NF). Vib1 =[A(NF min ) Vib1 A(NF) min+1 ) Vib1 A(NF) min+2 ) Vib1 , ..., A(CF) Vib1 , ..., A(NF max-2 ) Vib1 A(NF) max-1 ) Vib1 A(NF) max ) Vib1 The maximum amplitude of the set of adjacent frequencies of the tooth frequency of the vibration dataset Vib1 is calculated as MAX(Vib1): MAX(Vib1) = max(A(NF)Vib1), where max(E) represents the maximum value of all elements in set E.

[0074] For example, performing a Fourier transform on the vibration dataset Vib2 generates a spectrum, yielding the amplitudes at different frequencies. A set of amplitudes A(f) corresponding to the nearest frequency set is then constructed. i ) Vib2 , namely A(NF) Vib2 =[A(NF min ) Vib2， A(NF min+1 ) Vib2 A(NF) min+2 ) Vib2 , ..., A(CF) Vib2 , ..., A(NF max-2 ) Vib2 A(NF) max-1 )Vib2 A(NF) max ) Vib 2]. The maximum amplitude of the set of adjacent frequencies of the tooth frequency of the vibration dataset Vib2 is calculated as MAX(Vib2): MAX(Vib2) = max(A(NF)Vib2).

[0075] For example, performing a Fourier transform on the vibration dataset Vib3 generates a spectrum, yielding the amplitudes at different frequencies. A set of amplitudes A(f) corresponding to the nearest frequency set is then constructed. i ) Vib3 , namely A(NF) Vib3 =[A(NF min ) Vib3 A(NF) min+1 ) Vib3 A(NF) min+ 2) Vib3 , ..., A(CF) Vib3 , ..., A(NF max-2 ) Vib3 A(NF) max-1 ) Vib3 A(NF) max ) Vib3 The maximum amplitude of the set of adjacent frequencies of the tooth frequency in the vibration dataset Vib3 is calculated as MAX(Vib3): MAX(Vib3) = max(A(NF)Vib3).

[0076] Thirdly, regarding step S130, it should be noted that the specific method for determining the target vibration characteristic value of the acquired target spindle vibration data can refer to the method for determining the sample vibration characteristic value of the acquired sample spindle vibration data in step S110, for example: Vibration data (i.e., target spindle vibration data) under the same cutting conditions is collected in real time. Each acquisition takes T seconds, constructing a vibration dataset Vib. Then, the vibration characteristic value R of Vib is calculated. The number of samples in Vib is S = F × T, and the vibration characteristic value R (i.e., the target vibration characteristic value) can be: ; Where, x i This represents the vibration signal value at the i-th sampling time in the vibration dataset.

[0077] Furthermore, when comparing the target vibration characteristic value with the equipment vibration state monitoring threshold, if the target vibration characteristic value is less than the equipment vibration state monitoring threshold, a first comparative analysis result is obtained to characterize that the state is abnormal. If the target vibration characteristic value is greater than or equal to the equipment vibration state monitoring threshold, a first comparative analysis result is obtained to characterize that the state is not abnormal (i.e., normal state).

[0078] Fourthly, regarding step S140, it should be noted that the specific method for determining the target tooth frequency characteristic value of the target spindle vibration data can refer to the method for determining the sample tooth frequency characteristic value of the acquired sample spindle vibration data in step S120, for example: Perform a Fourier transform on the vibration dataset Vib to generate a spectrum. Construct the amplitude set A(f) corresponding to the nearest frequency set. Vib , namely A(NF) Vib =[A(NF min ) Vib A(NF) min+1 ) Vib A(NF) min+2 ) Vib , ..., A(CF) Vib , ..., A(NF max-2 ) Vib A(NF) max-1 ) Vib A(NF) max ) Vib The maximum amplitude of the set of neighboring frequencies of the tooth frequency in the vibration dataset Vib is calculated as MAX(Vib): MAX(Vib) = max(A(NF)). Vib ).

[0079] Fifthly, regarding step S150, it should be noted that the magnitude relationship between the target tooth frequency characteristic value and the tool condition monitoring threshold can be determined, and based on this magnitude relationship, the second comparative analysis result can be determined. For example, if MAX(Vib) > T... Th If the condition is met, it is identified as: abnormal equipment vibration, caused by abnormal tool condition. Otherwise, it is identified as: abnormal equipment vibration, caused by abnormal spindle condition.

[0080] Based on this, by collecting the spindle vibration data of the equipment in real time, the vibration status of the equipment can be effectively identified. When the vibration status is abnormal, the equipment can be stopped in time to avoid further quality problems of parts. It can also locate the cause of the abnormal vibration status of the equipment and improve the efficiency of equipment operation and maintenance.

[0081] To facilitate understanding of the above-described CNC equipment status analysis method, a specific application example is provided. Combined with... Figures 3-8As shown, a vertical / horizontal machining center is equipped with a high-speed electric spindle with a maximum speed of 24,000 rpm. When machining a certain part, the spindle speed is 15,000 rpm, resulting in a rotational frequency of 250 Hz. Furthermore, the cutting tool used to machine this part is a two-tooth end mill, with a tooth frequency of 500 Hz. Vibration data is continuously collected for 10 seconds each time the part is machined. This part is machined 17 times, resulting in 17 vibration datasets. The calculated R values ​​for the 17 vibration datasets are: 1.0558, 1.0771, 1.0657, 1.0485, 1.0800, 1.0642, 1.0722, 1.0816, 1.0792, 1.0959, 0.87, 0.86, 0.87, 0.88, 0.85, 0.84, 0.86. Figure 3 As shown, the corresponding maximum amplitude of the adjacent frequency of the tooth frequency is MAX=[0.0196, 0.0203, 0.0218, 0.0199, 0.0209, 0.0211, 0.0206, 0.0204, 0.0214, 0.0211, 0.0199, 0.0205, 0.0207, 0.0213, 0.0204, 0.0199, 0.0207]. Figure 4 As shown.

[0082] Vibration data collected during the first three processing operations were used as Vib1, Vib2, and Vib3 vibration datasets. The calculated vibration characteristic values ​​R for the three datasets were 1.0558, 1.0771, and 1.0657, respectively, with an average of 1.0662 and a standard deviation of 0.0107. Therefore, the equipment vibration status monitoring threshold was 1.0342. Simultaneously, the maximum amplitude (MAX) of the adjacent frequencies of the tooth frequency in the three vibration datasets were 0.0196, 0.0203, and 0.0218, respectively. Figures 5 to 7 As shown, the average value is 0.0206, so the tool condition monitoring threshold is 0.0411.

[0083] When the part was machined for the 11th time, the vibration characteristic value R was 0.87, which was lower than the equipment vibration monitoring threshold of 1.0342, indicating an abnormal equipment vibration condition. The maximum amplitude of the nearest frequency to the tooth frequency observed during the 11th machining was 0.0199. Figure 8 As shown, the value is significantly lower than the tool condition monitoring threshold of 0.0411, indicating that the tool condition is normal. The result is that the equipment vibration condition is abnormal, and the cause of the abnormal equipment vibration condition is the abnormal spindle condition.

[0084] Combination Figure 9This application also provides a CNC equipment status analysis device applicable to the aforementioned electronic equipment. The CNC equipment status analysis device may include a first threshold determination module, a second threshold determination module, a first comparison analysis module, a tooth frequency feature determination module, and a second comparison analysis module.

[0085] The first threshold determination module is used to determine the sample vibration characteristic values ​​of the acquired sample spindle vibration data, and, based on the sample vibration characteristic values, determine a device vibration state monitoring threshold, wherein the sample spindle vibration data is obtained by monitoring the spindle in a normal state using a vibration acceleration sensor. In this embodiment, the first threshold determination module can be used to perform... Figure 2 For details regarding step S110 shown, please refer to the previous description of step S110 for information about the first threshold determination module.

[0086] The second threshold determination module is used to determine the sample tooth frequency characteristic value of the sample spindle vibration data, and, based on the sample tooth frequency characteristic value, determine the tool condition monitoring threshold. In this embodiment, the second threshold determination module can be used to perform... Figure 2 The relevant content regarding the second threshold determination module in step S120 shown can be found in the previous description of step S120.

[0087] The first comparison and analysis module is used to determine the target vibration characteristic value of the acquired target spindle vibration data, and to compare and analyze the target vibration characteristic value with the equipment vibration state monitoring threshold to obtain a first comparison and analysis result. The target spindle vibration data is obtained by monitoring the spindle of the CNC equipment to be analyzed using a vibration acceleration sensor. In this embodiment, the first comparison and analysis module can be used to perform... Figure 2 For details regarding step S130 shown, please refer to the previous description of step S130 for information about the first comparison and analysis module.

[0088] The tooth frequency characteristic determination module is used to determine the target tooth frequency characteristic value of the target spindle vibration data when the first comparative analysis result reflects an abnormal state of the CNC equipment to be analyzed. In this embodiment, the tooth frequency characteristic determination module can be used to perform... Figure 2 The relevant content regarding the tooth frequency characteristic determination module in step S140 shown can be found in the previous description of step S140.

[0089] The second comparison analysis module is used to compare and analyze the target tooth frequency feature value and the tool condition monitoring threshold to obtain a second comparison analysis result. This second comparison analysis result reflects whether the abnormality of the CNC equipment under analysis is due to a tool condition abnormality or a spindle condition abnormality. In this embodiment, the second comparison analysis module can be used to execute... Figure 2 For details regarding step S150 shown, please refer to the previous description of step S150 for information about the second comparison analysis module.

[0090] In this embodiment of the application, corresponding to the above-described numerical control equipment status analysis method applied to the electronic device, a computer-readable storage medium is also provided, which stores a computer program that executes the various steps of the numerical control equipment status analysis method when the computer program is run.

[0091] The steps executed by the aforementioned computer program during runtime will not be described in detail here, but can be found in the explanation of the CNC equipment status analysis method above.

[0092] In summary, the CNC equipment condition analysis method, apparatus, electronic device, and storage medium provided in this application first determine the equipment vibration condition monitoring threshold based on the sample vibration characteristic values ​​of the sample spindle vibration data; secondly, determine the tool condition monitoring threshold based on the sample tooth frequency characteristic values ​​of the sample spindle vibration data; then, compare and analyze the target vibration characteristic values ​​of the target spindle vibration data with the equipment vibration condition monitoring threshold to obtain a first comparison analysis result; further, if the first comparison analysis result reflects an abnormal state of the CNC equipment to be analyzed, then determine the target tooth frequency characteristic value of the target spindle vibration data; finally, compare and analyze the target tooth frequency characteristic value with the tool condition monitoring threshold to obtain a second comparison analysis result. Based on the above, by comparing and analyzing the data in these two dimensions—vibration characteristic values ​​and tooth frequency characteristic values—the cause of abnormal equipment vibration can be determined, and the cause of the abnormality can be located. Therefore, this can improve the problem of difficulty in locating the cause of abnormal equipment vibration in existing technologies.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0094] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0095] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0096] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A numerical control device state analysis method characterized by comprising: include: The sample vibration characteristic values ​​of the acquired sample spindle vibration data are determined, and based on the sample vibration characteristic values, the equipment vibration state monitoring threshold is determined, wherein the sample spindle vibration data is obtained by monitoring the spindle in normal state using a vibration acceleration sensor; Based on the number of teeth of the tool corresponding to the sample spindle vibration data and the corresponding spindle rotation speed, the tooth frequency of the sample spindle vibration data is determined; based on the fluctuation range of the tooth frequency of the sample spindle vibration data, a set of neighboring tooth frequencies is determined, wherein each frequency in the set of neighboring tooth frequencies is an integer; based on the maximum amplitude of each frequency in the set of neighboring tooth frequencies in the sample spindle vibration data, the sample tooth frequency characteristic value of the sample spindle vibration data is obtained; based on the sample tooth frequency characteristic value, a tool condition monitoring threshold is determined, wherein, when there are multiple sample spindle vibration data, the tool condition monitoring threshold has a positive correlation with the mean of the multiple sample tooth frequency characteristic values ​​corresponding to the multiple sample spindle vibration data; The target vibration characteristic value of the acquired target spindle vibration data is determined, and the target vibration characteristic value is compared and analyzed with the equipment vibration state monitoring threshold to obtain a first comparison and analysis result. The target spindle vibration data is obtained by monitoring the spindle of the CNC equipment to be analyzed through a vibration acceleration sensor. If the first comparative analysis result reflects that the state of the CNC equipment to be analyzed is abnormal, then the target tooth frequency characteristic value of the target spindle vibration data is determined; The target tooth frequency feature value and the tool condition monitoring threshold are compared and analyzed to obtain a second comparison analysis result. The second comparison analysis result is used to reflect whether the abnormality of the CNC equipment to be analyzed is due to tool condition abnormality or spindle condition abnormality.

2. The numerical control apparatus state analysis method according to claim 1, characterized by, The steps of determining the sample vibration characteristic values ​​of the acquired sample spindle vibration data, and determining the equipment vibration state monitoring threshold based on the sample vibration characteristic values, include: Multiple sample spindle vibration data were acquired; For each sample spindle vibration data in the plurality of sample spindle vibration data, the sample vibration characteristic value of the sample spindle vibration data is determined based on the number of vibration signals included in the sample spindle vibration data and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data. Based on the distribution of multiple sample vibration characteristic values ​​of the multiple sample spindle vibration data, the equipment vibration status monitoring threshold is determined.

3. The numerical control apparatus state analysis method according to claim 2, characterized by, The step of acquiring multiple sample spindle vibration data includes: Multiple spindle vibration data were acquired. These multiple spindle vibration data were collected multiple times under the premise that the spindle vibration state was normal and under the same cutting conditions. The same cutting conditions refer to the same parts, tools, spindle speed, feed rate, cutting width and cutting depth in all conditions. Based on the multiple spindle vibration data, multiple sample spindle vibration data were determined.

4. The numerical control apparatus state analysis method according to claim 2, characterized by, The step of determining the sample vibration characteristic value of each sample spindle vibration data point in the plurality of sample spindle vibration data points, based on the number of vibration signals included in the sample spindle vibration data point and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data point, includes: For each sample spindle vibration data in the plurality of sample spindle vibration data, based on the acquisition frequency and acquisition time corresponding to the spindle vibration data, the number of vibration signals included in the spindle vibration data is determined, and the squares of the signal values ​​of each vibration signal included in the spindle vibration data are summed to form the characterization parameters of the vibration energy corresponding to the sample spindle vibration data. The ratio between the number of vibration signals included in the spindle vibration data and the characterization parameter of the vibration energy corresponding to the sample spindle vibration data is determined, and the ratio is exponentially calculated to obtain the sample vibration characteristic value of the sample spindle vibration data.

5. The numerical control apparatus state analysis method according to claim 2, characterized by, The step of determining the equipment vibration status monitoring threshold based on the distribution of multiple sample vibration feature values ​​of the multiple sample spindle vibration data includes: The mean value of the multiple sample vibration feature values ​​of the multiple sample spindle vibration data is calculated to obtain the sample vibration feature mean value. The standard deviation of the sample vibration feature value is calculated based on the multiple sample vibration feature values ​​and the sample vibration feature mean value to obtain the sample vibration feature standard deviation value. Based on the mean and standard deviation of the sample vibration characteristics, a monitoring threshold for equipment vibration status is determined. The monitoring threshold for equipment vibration status has a positive correlation with the mean of the sample vibration characteristics, and a negative correlation with the standard deviation of the sample vibration characteristics.

6. The numerical control apparatus state analysis method according to claim 1, characterized by, The step of obtaining the sample tooth frequency characteristic value of the sample spindle vibration data based on the maximum amplitude of each frequency in the set of neighboring tooth frequencies in the sample spindle vibration data includes: The sample principal axis vibration data is subjected to Fourier transform to form a sample principal axis vibration spectrum, wherein the sample principal axis vibration spectrum is used to reflect the amplitude at different frequencies; For each frequency in the set of adjacent frequencies of the tooth frequency, the amplitude corresponding to that frequency is determined in the spectrum diagram of the sample principal axis; Among the amplitudes corresponding to each frequency in the set of frequencies adjacent to the tooth frequency, the amplitude with the maximum value is determined, and this amplitude is used as the sample tooth frequency characteristic value of the sample spindle vibration data.

7. A numerical control apparatus state analysis device characterized by comprising: include: The first threshold determination module is used to determine the sample vibration characteristic value of the acquired sample spindle vibration data, and to determine the equipment vibration state monitoring threshold based on the sample vibration characteristic value, wherein the sample spindle vibration data is obtained by monitoring the spindle in normal state using a vibration acceleration sensor; The second threshold determination module is used to determine the tooth frequency of the sample spindle vibration data based on the number of teeth of the tool corresponding to the sample spindle vibration data and the corresponding spindle rotation speed; determine the tooth frequency neighboring frequency set based on the fluctuation range of the tooth frequency of the sample spindle vibration data, wherein each frequency in the tooth frequency neighboring frequency set is an integer; obtain the sample tooth frequency characteristic value of the sample spindle vibration data based on the maximum amplitude of each frequency in the tooth frequency neighboring frequency set corresponding to the sample spindle vibration data; and determine the tool condition monitoring threshold based on the sample tooth frequency characteristic value, wherein, when there are multiple sample spindle vibration data, the tool condition monitoring threshold has a positive correlation with the mean of the multiple sample tooth frequency characteristic values ​​corresponding to the multiple sample spindle vibration data; The first comparative analysis module is used to determine the target vibration characteristic value of the acquired target spindle vibration data, and to compare and analyze the target vibration characteristic value with the equipment vibration state monitoring threshold to obtain a first comparative analysis result. The target spindle vibration data is obtained by monitoring the spindle of the CNC equipment to be analyzed through a vibration acceleration sensor. The tooth frequency characteristic determination module is used to determine the target tooth frequency characteristic value of the target spindle vibration data when the first comparison analysis result reflects an abnormal state of the CNC equipment to be analyzed. The second comparison and analysis module is used to compare and analyze the target tooth frequency feature value and the tool condition monitoring threshold to obtain a second comparison and analysis result. The second comparison and analysis result is used to reflect whether the abnormality of the CNC equipment to be analyzed is due to tool condition abnormality or spindle condition abnormality.

8. An electronic device, comprising: include: Memory, used to store computer programs; A processor connected to the memory is used to execute the computer program stored in the memory to implement the CNC equipment status analysis method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a computer program that, when executed, performs the CNC equipment status analysis method according to any one of claims 1-6.

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