Machine tool cutter state monitoring method and device and machine tool

Abstract

The invention discloses a machine tool cutter state monitoring method and device and a machine tool, and belongs to the technical field of machine tools. The machine tool cutter state monitoring method comprises the steps that a main shaft power curve of a machine tool in the machining process is obtained, and K speed modal signals corresponding to the main shaft power curve are obtained; acquiring a characteristic data segment of each speed modal signal in the K speed modal signals; obtaining a load mean value of each feature data segment in the K feature data segments in the N processing samples, and obtaining a load mean value sequence corresponding to each feature data segment in the K feature data segments; obtaining a load mean value sequence corresponding to each feature data segment in the K feature data segments and a Pearson's correlation coefficient corresponding to the tool life, and obtaining a Pearson's correlation coefficient sequence including K Pearson's correlation coefficient values; and determining a target feature data segment from the K feature data segments based on the Pearson's correlation coefficient sequence, and determining the cutter state of the machine tool based on the target feature data segment.

Description

Technical Field

[0001] The present application belongs to the technical field of machine tools, and specifically relates to a method and device for monitoring the status of machine tool cutters, and a machine tool. Background Art

[0002] During the machining process, the acoustic emission method is usually used to monitor the tool status to ensure the machining quality of parts. Since the acoustic emission signal comes directly from the cutting area, and there may be acoustic emission sources such as material breakage, tool breakage, chip breakage, and chip-workpiece collision during the cutting process, the signals emitted by the acoustic emission sources such as material breakage, chip breakage, and chip-workpiece collision are likely to interfere with the acoustic emission signals generated when the tool is damaged.

[0003] It can be seen that the monitoring scheme for tool status monitoring based on acoustic emission method in the related art has the problem of poor accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device and machine tool for monitoring the tool status of a machine tool, which can solve the problem of poor accuracy of the monitoring scheme for monitoring the tool status based on the acoustic emission method in the related art.

[0005] In order to solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring the state of a machine tool tool, the method comprising:

[0007] Acquire a spindle power curve of a machine tool during machining, and acquire a speed modal signal sequence corresponding to the spindle power curve, wherein the speed modal signal sequence includes K speed modal signals, where K is a positive integer;

[0008] Acquire a characteristic data segment of each of the K velocity modal signals;

[0009] Obtaining a load mean value of each of the K characteristic data segments in N processed samples, and obtaining a load mean value sequence corresponding to each of the K characteristic data segments, where N is a positive integer;

[0010] Obtaining the Pearson correlation coefficient of the load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments and the tool life, and obtaining a Pearson correlation coefficient sequence including K Pearson correlation coefficient values;

[0011] Based on the Pearson correlation coefficient sequence, a target feature data segment is determined from the K feature data segments, and the tool state of the machine tool is determined based on the target feature data segment.

[0012] In a second aspect, an embodiment of the present application provides a machine tool tool state monitoring device, the device comprising:

[0013] A first acquisition module is used to acquire a spindle power curve of a machine tool during a machining process, and acquire a speed modal signal sequence corresponding to the spindle power curve, wherein the speed modal signal sequence includes K speed modal signals, where K is a positive integer;

[0014] A second acquisition module is used to acquire a characteristic data segment of each velocity modal signal of the K velocity modal signals;

[0015] A third acquisition module is used to obtain the load mean of each characteristic data segment in the K characteristic data segments in the N processing samples, and obtain a load mean sequence corresponding to each characteristic data segment in the K characteristic data segments, where N is a positive integer;

[0016] A fourth acquisition module is used to obtain the Pearson correlation coefficient corresponding to the load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments and the tool life, and obtain a Pearson correlation coefficient sequence including K Pearson correlation coefficient values;

[0017] A determination module is used to determine a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence, and determine the tool state of the machine tool based on the target feature data segment.

[0018] In a third aspect, an embodiment of the present application provides a machine tool, comprising a processor and a memory, wherein the memory stores programs or instructions that can be executed on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0019] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0020] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0021] In a sixth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the method described in the first aspect.

[0022] In an embodiment of the present application, the spindle power curve of the machine tool during the machining process can be obtained, and the speed modal signal sequence corresponding to the spindle power curve can be obtained, so as to extract K characteristic data segments from the K speed modal signals included in the speed modal signal sequence, and the load mean sequence corresponding to the K characteristic data segments and the Pearson correlation coefficient corresponding to the tool life can be obtained to determine the target characteristic data segment that meets the requirements from the K characteristic data segments, so as to determine the tool state of the machine tool based on the target characteristic data segment. Compared with the monitoring scheme for monitoring the tool state based on the acoustic emission method, it can not only avoid the interference signals generated by the acoustic emission sources such as material breakage, chip breakage, and collision of chips with the workpiece, but also realize the automatic selection of the target characteristic data segment, thereby improving the accuracy of the monitoring results of the tool state. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a machine tool tool status monitoring method provided by an embodiment of the present application;

[0024] Figure 2 is a structural diagram of a machine tool tool status monitoring device provided in an embodiment of the present application;

[0025] Figure 3 It is a structural diagram of the machine tool provided in the embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0028] The following is a detailed description of the machine tool tool status monitoring method provided by the embodiment of the present application through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0029] See also Figure 1 , Figure 1 It is a flow chart of a machine tool tool status monitoring method provided in an embodiment of the present application.

[0030] like Figure 1 As shown, the machine tool tool status monitoring method can be applied to a machine tool and includes the following steps:

[0031] Step 101: Acquire a spindle power curve of a machine tool during a machining process, and acquire a speed modal signal sequence corresponding to the spindle power curve.

[0032] In this step, a processing sample of the machine tool machining the target part may be collected, and a spindle power curve corresponding to the processing sample may be determined as a reference curve for the machine tool machining the target part.

[0033] After the spindle power curve of the machine tool is determined, the corresponding speed modal signal sequence can be determined based on the spindle power curve of the machine tool.

[0034] The velocity modal signal sequence includes K velocity modal signals, and K is a positive integer.

[0035] In some embodiments, the speed mode signal sequence can be expressed as speed_mode:[x1,x2,x3,…,xk], where k represents the number of speed mode signals.

[0036] Step 102: Acquire a characteristic data segment of each of the K velocity modal signals.

[0037] In this step, the sampling points included in each of the K velocity modal signals may be cleaned to obtain sampling points that meet preset conditions, and corresponding feature data segments may be obtained based on the sampling points that meet the preset conditions.

[0038] Step 103: Obtain the load mean of each of the K characteristic data segments in the N processed samples, and obtain a load mean sequence corresponding to each of the K characteristic data segments.

[0039] In this step, the N processing samples can be understood as the processing parameters of the machine tool for the N target parts mentioned above, and the load mean of each feature data segment in the K feature data segments in the N processing samples can be understood as the load mean of each processing sample in the N processing samples in the corresponding feature data segment.

[0040] In some embodiments, the above K feature data segments can be expressed as section_i:[section_1,section_2,…,section_i,…,section_k]. Taking section_i as an example, assuming that the current number of samples is N_sample, the mean sequence of section_i can be calculated as: section_mean_i:[sm_1,sm_2,…sm_i,…sm_N], where N represents the number of samples, and sm_i represents the load mean in section_i corresponding to the i-th sample.

[0041] Wherein, N is a positive integer.

[0042] Step 104 , obtaining the Pearson correlation coefficient between the load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments and the tool life, and obtaining a Pearson correlation coefficient sequence including K Pearson correlation coefficient values.

[0043] The above Pearson correlation coefficient sequence can be expressed as person:[p_1,p_2,…,p_i,…,p_k], where p_i represents the Pearson correlation coefficient of the i-th feature data segment, and k represents the number of data segments.

[0044] Step 105: Determine a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence, and determine the tool state of the machine tool based on the target feature data segment.

[0045] In this step, a Pearson correlation coefficient value that meets the preset requirements can be selected from the Pearson correlation coefficient sequence, and the characteristic data segment corresponding to the Pearson correlation coefficient value that meets the preset requirements is determined as the target characteristic data segment.

[0046] In this embodiment, the spindle power curve of the machine tool during the machining process and the speed modal signal sequence corresponding to the spindle power curve can be obtained to extract K characteristic data segments from the K speed modal signals included in the speed modal signal sequence, and the load mean sequence corresponding to the K characteristic data segments and the Pearson correlation coefficient corresponding to the tool life can be obtained to determine the target characteristic data segment that meets the requirements from the K characteristic data segments, so as to determine the tool state of the machine tool based on the target characteristic data segment. Compared with the monitoring scheme for monitoring the tool state based on the acoustic emission method, it can not only avoid the interference signals generated by the acoustic emission sources such as material breakage, chip breakage, and collision of chips with the workpiece, but also realize the automatic selection of the target characteristic data segment, thereby improving the accuracy of the monitoring results of the tool state.

[0047] Moreover, by adopting the machine tool tool state monitoring method provided by the present application, since the target characteristic data segment that meets the requirements is determined directly based on the speed modal signal sequence corresponding to the spindle power curve, there is no need to distinguish the tool processing scenarios during the tool state monitoring process, thereby reducing the impact of the distinction and judgment of the processing scenarios on the tool state monitoring results.

[0048] In some embodiments, determining a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes:

[0049] When the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, determining the characteristic data segment corresponding to the target Pearson correlation coefficient value as the target characteristic data segment;

[0050] The target Pearson correlation coefficient value is the maximum value among the K Pearson correlation coefficient values.

[0051] For example, the maximum value in the Pearson correlation coefficient sequence can be calculated, that is, the maximum value among K Pearson correlation coefficient values ​​is determined as the target Pearson correlation coefficient value and represented by person_max; and if person_max is greater than or equal to a preset value, the feature data segment corresponding to person_max is determined as the target feature data segment.

[0052] Among them, the above preset value can be 0.6.

[0053] It is understandable that the above preset value may also be other values, such as 0.5 or 0.7, etc. The user can set the above preset value based on actual needs.

[0054] In some embodiments, determining a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes:

[0055] When the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtaining an enclosed area between a characteristic curve corresponding to each characteristic data segment in the K characteristic data segments and an X-axis, where the X-axis is an extension direction of the characteristic curve;

[0056] The feature data segment with the largest enclosed area among the K feature data segments is determined as the target feature data segment.

[0057] The above-mentioned target Pearson correlation coefficient value greater than or equal to the preset value is not included in the Pearson correlation coefficient sequence, which can be understood as the maximum value in the Pearson correlation coefficient sequence is less than the preset value, that is, the maximum value (person_max) of the K Pearson correlation coefficient values ​​is less than the preset value; at this time, the enclosed area of ​​the characteristic curve corresponding to each feature data segment in the K feature data segments and the X-axis can be obtained, and the feature data segment with the largest enclosed area among the K feature data segments is determined as the target feature data segment.

[0058] In some embodiments, the acquiring of a characteristic data segment of each of the K velocity modal signals comprises:

[0059] Preprocessing the data points corresponding to each velocity modal signal to obtain target data points that meet preset conditions and index information of the target data points;

[0060] Performing first-order difference processing on the index information of the target data point to obtain an index sequence;

[0061] Determine, based on the index sequence, an index sequence of the interval start point and an index sequence of the interval end point corresponding to the target data point, wherein the index information of each start point in the index sequence of the interval start point corresponds to the index information of each end point in the index sequence of the interval end point;

[0062] Based on the index sequence of the interval start point and the index sequence of the interval end point, a characteristic data segment of the velocity modal signal is determined.

[0063] In this embodiment, index information may be assigned to target data points that meet preset conditions so as to determine characteristic data segments of the velocity modal signal.

[0064] In some embodiments, determining the characteristic data segment of the velocity modal signal based on the index sequence of the interval start point and the index sequence of the interval end point includes:

[0065] Determine a plurality of data segments corresponding to the target data point based on index information of each starting point in the index sequence of the interval starting point and index information of each ending point in the index sequence of the interval ending point;

[0066] The integral value corresponding to each data segment in the plurality of data segments and the spindle no-load information of the machine tool is obtained, and the data segment with the largest integral value in the plurality of data segments is determined as a characteristic data segment of the velocity modal signal.

[0067] In this embodiment, by determining the data segment with the largest integral value among multiple data segments as the characteristic data segment of the velocity modal signal, that is, determining the data segment that best represents the velocity modal signal as the characteristic data segment, the accuracy of the tool status monitoring result can be improved.

[0068] In some embodiments, the base data segment of each velocity modal signal can be set as base_line:[x1,x2,x3,…,xn], where n is the number of sampling points in a processing cycle. The determination method of the above characteristic data segment includes the following steps:

[0069] Step 1, calculate the minimum value of the base data segment base_line and determine it as base_line_min;

[0070] Step 2, xecm_line: [x1, x2, x3, …, xn], the formula is as follows: xecm_line = (base_line-base_line_min) / base_line;

[0071] Step 3: Filter out the data points less than 0 in xecm_line, and set the filtered sequence as xecm_above0;

[0072] Step 4, calculate the median of the sequence xecm_above0 and obtain xecm_threshold;

[0073] Step 5, extract the data points in xecm_line that are greater than xecm_threshold, and obtain the indexes of these data points, that is, obtain the index information of the above target data points;

[0074] Step 6: Calculate the first-order difference of index index to obtain the sequence index_diff;

[0075] The purpose of first-order difference of the index is to find continuous index. When the value is 1, it means it is continuous. When the value is greater than 1, it means it is discontinuous.

[0076] Step 7: Extract the data points greater than 10 in index_diff, and set the extracted index sequence as index_diff_1. Note: 10 here is an empirical value and can be adjusted according to actual conditions.

[0077] Step 8. location_end={index[value==index_diff_1]}U{index[-1]}, location_start={index[0]}U{index[value==index_diff_1+1]};

[0078] Wherein, U represents a union, that is, location_end is equal to the union of {index[value==index_diff_1]} and {index[-1]}, and location_start is equal to the union of {index[0]} and {index[value==index_diff_1+1]}; index[-1] represents the last sampling point in the index sequence; index[0] represents the first sampling point in the index sequence;

[0079] Step 9, location_start is the index sequence of the interval starting point, location_end is the index sequence of the interval ending point, for each pair of starting point and ending point, calculate the integral value of the difference between the segment data and the no-load, and determine the interval segment with the largest integral as the monitoring interval to be selected, that is, determine the interval segment with the largest integral as the characteristic data segment of the velocity modal signal;

[0080] The location_start may be represented as an index sequence of the starting point of the interval, and the location_end may be represented as an index sequence of the ending point of the interval.

[0081] The machine tool tool state monitoring method of the embodiment of the present application is to obtain the spindle power curve of the machine tool during the processing, and obtain the speed modal signal sequence corresponding to the spindle power curve, wherein the speed modal signal sequence includes K speed modal signals, K is a positive integer; obtain the characteristic data segment of each speed modal signal in the K speed modal signals; obtain the load mean of each characteristic data segment in the K characteristic data segments in N processing samples, and obtain the load mean sequence corresponding to each characteristic data segment in the K characteristic data segments, N is a positive integer; obtain the Pearson correlation coefficient corresponding to the load mean sequence of each characteristic data segment in the K characteristic data segments and the tool life, and obtain the Pearson correlation coefficient sequence including K Pearson correlation coefficient values; based on the Pearson correlation coefficient sequence, determine the target characteristic data segment from the K characteristic data segments, and determine the tool state of the machine tool based on the target characteristic data segment. This improves the accuracy of the tool state monitoring result.

[0082] See also Figure 2 , Figure 2 is a structural diagram of a machine tool tool status monitoring device provided in an embodiment of the present application, such as Figure 2 As shown, the machine tool tool state monitoring device 200 includes:

[0083] The first acquisition module 201 is used to acquire a spindle power curve of a machine tool during machining, and acquire a speed modal signal sequence corresponding to the spindle power curve, wherein the speed modal signal sequence includes K speed modal signals, where K is a positive integer;

[0084] A second acquisition module 202 is used to acquire a characteristic data segment of each velocity modal signal in the K velocity modal signals;

[0085] The third acquisition module 203 is used to obtain the load mean value of each characteristic data segment in the K characteristic data segments in the N processing samples, and obtain the load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments, where N is a positive integer;

[0086] A fourth acquisition module 204 is used to acquire a Pearson correlation coefficient between a load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments and a tool life, and obtain a Pearson correlation coefficient sequence including K Pearson correlation coefficient values;

[0087] The determination module 205 is used to determine a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence, and determine the tool state of the machine tool based on the target feature data segment.

[0088] Optionally, the determining module 205 is specifically configured to:

[0089] When the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, determining the characteristic data segment corresponding to the target Pearson correlation coefficient value as the target characteristic data segment;

[0090] The target Pearson correlation coefficient value is the maximum value among the K Pearson correlation coefficient values.

[0091] Optionally, the determining module 205 is specifically configured to:

[0092] When the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtaining an enclosed area between a characteristic curve corresponding to each characteristic data segment in the K characteristic data segments and an X-axis, where the X-axis is an extension direction of the characteristic curve;

[0093] The feature data segment with the largest enclosed area among the K feature data segments is determined as the target feature data segment.

[0094] Optionally, the second acquisition module 202 is specifically configured to:

[0095] Preprocessing the data points corresponding to each velocity modal signal to obtain target data points that meet preset conditions and index information of the target data points;

[0096] Performing first-order difference processing on the index information of the target data point to obtain an index sequence;

[0097] Determine, based on the index sequence, an index sequence of the interval start point and an index sequence of the interval end point corresponding to the target data point, wherein the index information of each start point in the index sequence of the interval start point corresponds to the index information of each end point in the index sequence of the interval end point;

[0098] Based on the index sequence of the interval start point and the index sequence of the interval end point, a characteristic data segment of the velocity modal signal is determined.

[0099] Optionally, the second acquisition module 202 is specifically configured to:

[0100] Determine a plurality of data segments corresponding to the target data point based on index information of each starting point in the index sequence of the interval starting point and index information of each ending point in the index sequence of the interval ending point;

[0101] The integral value corresponding to each data segment in the plurality of data segments and the spindle no-load information of the machine tool is obtained, and the data segment with the largest integral value in the plurality of data segments is determined as a characteristic data segment of the velocity modal signal.

[0102] The machine tool tool status monitoring device 200 provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0103] Alternatively, if Figure 3 As shown, the embodiment of the present application also provides a machine tool 300, including a processor 301 and a memory 302, and the memory 302 stores programs or instructions that can be executed on the processor 301. When the program or instructions are executed by the processor 301, the various steps of the above-mentioned machine tool tool state monitoring method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0104] It should be noted that the machine tools in the embodiments of the present application include the mobile machine tools and non-mobile machine tools mentioned above.

[0105] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned machine tool tool status monitoring method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0106] The processor is the processor in the machine tool described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0107] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned machine tool tool status monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0108] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0109] An embodiment of the present application provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the above-mentioned machine tool tool status monitoring method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0110] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0111] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0112] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A machine tool tool status monitoring method, characterized in that: The method comprises: Acquire a spindle power curve of a machine tool during machining, and acquire a speed modal signal sequence corresponding to the spindle power curve, wherein the speed modal signal sequence includes K speed modal signals, where K is a positive integer; Acquire a characteristic data segment of each velocity modal signal of the K velocity modal signals; Obtaining a load mean value of each of the K characteristic data segments in N processed samples, and obtaining a load mean value sequence corresponding to each of the K characteristic data segments, where N is a positive integer; Obtaining the Pearson correlation coefficient of the load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments and the tool life, and obtaining a Pearson correlation coefficient sequence including K Pearson correlation coefficient values; Based on the Pearson correlation coefficient sequence, a target feature data segment is determined from the K feature data segments, and the tool state of the machine tool is determined based on the target feature data segment.

2. The method according to claim 1, characterized in that The step of determining a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes: When the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, determining the characteristic data segment corresponding to the target Pearson correlation coefficient value as the target characteristic data segment; The target Pearson correlation coefficient value is the maximum value among the K Pearson correlation coefficient values.

3. The method according to claim 1, characterized in that The step of determining a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes: When the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtaining an enclosed area between a characteristic curve corresponding to each characteristic data segment in the K characteristic data segments and an X-axis, where the X-axis is an extension direction of the characteristic curve; The feature data segment with the largest enclosed area among the K feature data segments is determined as the target feature data segment.

4. The method according to any one of claims 1 to 3, characterized in that The step of obtaining a characteristic data segment of each of the K velocity modal signals comprises: Preprocessing the data points corresponding to each velocity modal signal to obtain target data points that meet preset conditions and index information of the target data points; Performing first-order difference processing on the index information of the target data point to obtain an index sequence; Determine, based on the index sequence, an index sequence of the interval start point and an index sequence of the interval end point corresponding to the target data point, wherein the index information of each start point in the index sequence of the interval start point corresponds to the index information of each end point in the index sequence of the interval end point; Based on the index sequence of the interval start point and the index sequence of the interval end point, a characteristic data segment of the velocity modal signal is determined.

5. The method according to claim 4, characterized in that The determining of the characteristic data segment of the velocity modal signal based on the index sequence of the interval starting point and the index sequence of the interval ending point comprises: Determine a plurality of data segments corresponding to the target data point based on index information of each starting point in the index sequence of the interval starting point and index information of each ending point in the index sequence of the interval ending point; The integral value corresponding to each data segment in the plurality of data segments and the spindle no-load information of the machine tool is obtained, and the data segment with the largest integral value in the plurality of data segments is determined as a characteristic data segment of the velocity modal signal.

6. A machine tool tool status monitoring device, characterized in that: The device comprises: A first acquisition module is used to acquire a spindle power curve of a machine tool during a machining process, and acquire a speed modal signal sequence corresponding to the spindle power curve, wherein the speed modal signal sequence includes K speed modal signals, where K is a positive integer; A second acquisition module is used to acquire a characteristic data segment of each velocity modal signal of the K velocity modal signals; A third acquisition module is used to obtain the load mean of each characteristic data segment in the K characteristic data segments in the N processing samples, and obtain a load mean sequence corresponding to each characteristic data segment in the K characteristic data segments, where N is a positive integer; A fourth acquisition module is used to obtain the Pearson correlation coefficient corresponding to the load mean value sequence corresponding to each characteristic data segment in the K characteristic data segments and the tool life, and obtain a Pearson correlation coefficient sequence including K Pearson correlation coefficient values; A determination module is used to determine a target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence, and determine the tool state of the machine tool based on the target feature data segment.

7. The device according to claim 6, characterized in that The determining module is specifically used for: When the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, determining the characteristic data segment corresponding to the target Pearson correlation coefficient value as the target characteristic data segment; The target Pearson correlation coefficient value is the maximum value among the K Pearson correlation coefficient values.

8. The device according to claim 6, characterized in that The determining module is specifically used for: When the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtaining an enclosed area between a characteristic curve corresponding to each characteristic data segment in the K characteristic data segments and an X-axis, where the X-axis is an extension direction of the characteristic curve; The feature data segment with the largest enclosed area among the K feature data segments is determined as the target feature data segment.

9. The device according to any one of claims 6 to 8, characterized in that The second acquisition module is specifically used for: Preprocessing the data points corresponding to each velocity modal signal to obtain target data points that meet preset conditions and index information of the target data points; Performing first-order difference processing on the index information of the target data point to obtain an index sequence; Determine, based on the index sequence, an index sequence of the interval start point and an index sequence of the interval end point corresponding to the target data point, wherein the index information of each start point in the index sequence of the interval start point corresponds to the index information of each end point in the index sequence of the interval end point; Based on the index sequence of the interval start point and the index sequence of the interval end point, a characteristic data segment of the velocity modal signal is determined.

10. The device according to claim 9, characterized in that The second acquisition module is specifically used for: Determine a plurality of data segments corresponding to the target data point based on index information of each starting point in the index sequence of the interval starting point and index information of each ending point in the index sequence of the interval ending point; The integral value corresponding to each data segment in the plurality of data segments and the spindle no-load information of the machine tool is obtained, and the data segment with the largest integral value in the plurality of data segments is determined as a characteristic data segment of the velocity modal signal.

11. A machine tool, characterized in that: It comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the machine tool tool status monitoring method as described in any one of claims 1 to 5 are implemented.

12. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the steps of the machine tool tool status monitoring method according to any one of claims 1 to 5 are implemented.

13. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method for monitoring the condition of a machine tool tool according to any one of claims 1 to 5.

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