Machine tool tool state monitoring method, device and machine tool
By analyzing the machine tool spindle power curve and speed mode signal, and using the Pearson correlation coefficient to determine the target feature data segment, the problem of poor accuracy in acoustic emission monitoring is solved, and more accurate tool status monitoring is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING FANUC MECHATRONICS CO LTD
- Filing Date
- 2024-11-05
- Publication Date
- 2026-07-24
AI Technical Summary
Existing technologies based on acoustic emission for tool condition monitoring have poor accuracy and are easily affected by acoustic emission sources such as material fracture, chip breakage, and chip-workpiece impact.
By acquiring the spindle power curve during machine tool processing, analyzing the speed mode signal sequence, extracting feature data segments, and calculating the Pearson correlation coefficient, the target feature data segments are determined to monitor the tool status.
It improves the accuracy of tool status monitoring, avoids the influence of interference signals, and enables automatic selection of target feature data segments.
Smart Images
Figure CN119217148B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of machine tool technology, specifically relating to a machine tool tool status monitoring method, device, and machine tool. Background Technology
[0002] In machine tool processing, acoustic emission is typically used to monitor the tool condition to ensure the machining quality of parts. Since the acoustic emission signal originates directly from the cutting zone, and during cutting, there may be acoustic emission sources such as material fracture, tool breakage, chip breakage, and chip-workpiece impact, the signals emitted by these sources can easily interfere with the acoustic emission signal generated when the tool breaks.
[0003] It is evident that the monitoring scheme based on acoustic emission to monitor tool status in related technologies suffers from poor accuracy. Summary of the Invention
[0004] The purpose of this application is to provide a machine tool tool status monitoring method, device, and machine tool, which can solve the problem of poor accuracy in related technologies where the monitoring scheme based on acoustic emission method is used to monitor tool status.
[0005] To solve the above-mentioned technical problems, this application is implemented as follows:
[0006] In a first aspect, embodiments of this application provide a method for monitoring the status of machine tool cutting tools, the method comprising:
[0007] The spindle power curve of the machine tool during the machining process is obtained, and the speed mode signal sequence corresponding to the spindle power curve is obtained. The speed mode signal sequence includes K speed mode signals, where K is a positive integer.
[0008] Obtain the characteristic data segment of each velocity mode signal from the K velocity mode signals;
[0009] Obtain the load mean of each of the K feature data segments in N processing samples to obtain the load mean sequence corresponding to each of the K feature data segments, where N is a positive integer;
[0010] Obtain the Pearson correlation coefficient between the load mean sequence and the tool life corresponding to each of the K feature data segments, and obtain 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] Secondly, embodiments of this application provide a machine tool cutting tool status monitoring device, the device comprising:
[0013] The first acquisition module is used to acquire the spindle power curve of the machine tool during the machining process, and to acquire the speed mode signal sequence corresponding to the spindle power curve. The speed mode signal sequence includes K speed mode signals, where K is a positive integer.
[0014] The second acquisition module is used to acquire the characteristic data segment of each velocity mode signal among the K velocity mode signals;
[0015] The third acquisition module is used to acquire the load mean of each of the K feature data segments in N processing samples, and obtain the load mean sequence corresponding to each of the K feature data segments, where N is a positive integer;
[0016] The fourth acquisition module is used to acquire the load mean sequence and the Pearson correlation coefficient corresponding to the tool life of each of the K feature data segments, so as to obtain a Pearson correlation coefficient sequence including K Pearson correlation coefficient values.
[0017] The 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 to determine the tool state of the machine tool based on the target feature data segment.
[0018] Thirdly, embodiments of this application provide a machine tool including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.
[0019] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.
[0020] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.
[0021] In a sixth aspect, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the method described in the first aspect.
[0022] In this embodiment, the spindle power curve of the machine tool during the machining process can be obtained, and the speed mode signal sequence corresponding to the spindle power curve can be obtained. K feature data segments can be extracted from the K speed mode signals included in the speed mode signal sequence. By obtaining the load mean sequence corresponding to the K feature data segments and the Pearson correlation coefficient corresponding to the tool life, the target feature data segment that meets the requirements can be determined from the K feature data segments. Based on the target feature data segment, the tool state of the machine tool can be determined. Compared with the monitoring scheme based on acoustic emission method to monitor the tool state, it can not only avoid the interference signals generated by acoustic emission sources such as material fracture, chip breakage, and chip-workpiece impact, but also realize the automatic selection of target feature data segments, thus improving the accuracy of the monitoring results of the tool state. Attached Figure Description
[0023] Figure 1 This is a flowchart of the machine tool tool status monitoring method provided in the embodiments of this application;
[0024] Figure 2 This is a structural diagram of the machine tool tool status monitoring device provided in the embodiments of this application;
[0025] Figure 3 This is a structural diagram of the machine tool provided in the embodiments of this application. Detailed Implementation
[0026] 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0027] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0028] The machine tool status monitoring method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.
[0029] See Figure 1 , Figure 1 This is a flowchart of the machine tool status monitoring method provided in the embodiments of this application.
[0030] like Figure 1 As shown, this machine tool tool status monitoring method can be applied to machine tools and includes the following steps:
[0031] Step 101: Obtain the spindle power curve of the machine tool during the machining process, and obtain the speed mode signal sequence corresponding to the spindle power curve.
[0032] In this step, a machining sample of the machine tool machining the target part can be collected, and the spindle power curve corresponding to the machining sample can be determined as the reference curve for the machine tool to machine the target part.
[0033] After determining the spindle power curve of the machine tool, the corresponding speed mode signal sequence can be determined based on the spindle power curve.
[0034] The velocity mode signal sequence includes K velocity mode signals, where K is a positive integer.
[0035] In some embodiments, the velocity mode signal sequence can be represented as speed_mode:[x1,x2,x3,…,xk], where k represents the number of velocity mode signals.
[0036] Step 102: Obtain the characteristic data segment of each velocity mode signal among the K velocity mode signals.
[0037] In this step, the sampling points included in each of the K velocity mode signals can be cleaned to obtain sampling points that meet preset conditions, and the corresponding feature data segments can be obtained based on the sampling points that meet the preset conditions.
[0038] Step 103: Obtain the load mean of each of the K feature data segments in N processing samples, and obtain the load mean sequence corresponding to each of the K feature 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 average load of each of the K feature data segments in the N processing samples can be understood as the average load of each processing sample in the corresponding feature data segment in the N processing samples.
[0040] In some embodiments, the above K feature data segments can be represented as section_i:[section_1,section_2,…,section_i,…,section_k]. Taking section_i as an example, if the current number of samples is N_sample, then 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 of the i-th sample in the corresponding section_i.
[0041] Where N is a positive integer.
[0042] Step 104: Obtain the load mean sequence and the Pearson correlation coefficient corresponding to the tool life of each of the K feature data segments to obtain a Pearson correlation coefficient sequence including K Pearson correlation coefficient values.
[0043] The Pearson correlation coefficient sequence can be represented 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: Based on the Pearson correlation coefficient sequence, determine the target feature data segment from the K feature data segments, and determine the tool state of the machine tool based on the target feature data segment.
[0045] In this step, Pearson correlation coefficient values that meet preset requirements can be selected from the Pearson correlation coefficient sequence, and the feature data segment corresponding to the Pearson correlation coefficient values that meet preset requirements can be determined as the target feature data segment.
[0046] In this embodiment, the spindle power curve of the machine tool during the machining process can be acquired, and the speed mode signal sequence corresponding to the spindle power curve can be acquired. K feature data segments can be extracted from the K speed mode signals included in the speed mode signal sequence. By acquiring the load mean sequence corresponding to the K feature data segments and the Pearson correlation coefficient corresponding to the tool life, the target feature data segments that meet the requirements can be determined from the K feature data segments. Based on the target feature data segments, the tool state of the machine tool can be determined. Compared with the monitoring scheme based on acoustic emission method to monitor the tool state, it can not only avoid the interference signals generated by acoustic emission sources such as material fracture, chip breakage, and chip-workpiece impact, but also realize the automatic selection of target feature data segments, thus improving the accuracy of the monitoring results of the tool state.
[0047] Moreover, by adopting the machine tool condition monitoring method provided in this application, since the target feature data segment that meets the requirements is determined directly based on the speed mode signal sequence corresponding to the spindle power curve, there is no need to distinguish the machining scenario of the tool during the monitoring of the tool condition, which reduces the impact of the distinction and judgment of the machining scenario on the monitoring results of the tool condition.
[0048] In some embodiments, determining the target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes:
[0049] If the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, the feature data segment corresponding to the target Pearson correlation coefficient value is determined as the target feature data segment.
[0050] The target Pearson correlation coefficient 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 denoted 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] The preset value can be 0.6.
[0053] It is understandable that the above preset value can also be other values, such as 0.5 or 0.7, etc., and users can set the above preset value according to their actual needs.
[0054] In some embodiments, determining the target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes:
[0055] If the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtain the area enclosed by the feature curve and the X-axis for each of the K feature data segments, where the X-axis is the extension direction of the feature 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 that is not greater than or equal to the preset value in the Pearson correlation coefficient sequence can be understood as the maximum value in the Pearson correlation coefficient sequence being less than the preset value, that is, the maximum value (person_max) among the K Pearson correlation coefficient values is less than the preset value; at this time, the area enclosed by the feature curve corresponding to each of 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 can be determined as the target feature data segment.
[0058] In some embodiments, obtaining the feature data segment of each velocity mode signal among the K velocity mode signals includes:
[0059] The data points corresponding to each velocity mode signal are preprocessed to obtain target data points that meet preset conditions and the index information of the target data points;
[0060] The index information of the target data points is subjected to first-order difference processing to obtain an index sequence;
[0061] Based on the index sequence, determine the index sequence of the interval start point and the index sequence of the interval end point corresponding to the target data point, and 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 starting point of the interval and the index sequence of the ending point of the interval, the characteristic data segment of the velocity mode signal is determined.
[0063] In this embodiment, index information can be assigned to target data points that meet preset conditions in order to obtain the characteristic data segments of the velocity mode signal.
[0064] In some embodiments, determining the characteristic data segment of the velocity mode signal based on the index sequence of the interval start point and the index sequence of the interval end point includes:
[0065] Based on the index information of each starting point in the index sequence of the starting points of the interval and the index information of each ending point in the index sequence of the ending points of the interval, multiple data segments corresponding to the target data point are determined;
[0066] Obtain the integral value of each data segment in the plurality of data segments corresponding to the spindle no-load information of the machine tool, and determine the data segment with the largest integral value in the plurality of data segments as the characteristic data segment of the speed mode 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 mode signal, that is, determining the data segment that best represents the velocity mode signal as the characteristic data segment, the accuracy of the tool status monitoring results can be improved.
[0068] In some embodiments, the reference data segment for each velocity mode signal can be set as base_line:[x1,x2,x3,…,xn], where n is the number of sampling points in one processing cycle. The determination of the above-mentioned feature data segment includes the following steps:
[0069] Step 1: Calculate the minimum value of the baseline 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 data points less than 0 in xecm_line, and let the filtered sequence be xecm_above0;
[0072] Step 4: Calculate the median of the sequence xecm_above0 to obtain xecm_threshold;
[0073] Step 5: Extract the data points in xecm_line that are greater than xecm_threshold, and obtain the index of these data points, that is, obtain the index information of the target data points mentioned above;
[0074] Step 6: Calculate the first difference of the index to obtain the sequence index_diff;
[0075] The purpose of performing first-order difference on the index is to find continuous indices. When the value is 1, it means that the index is continuous, and when the value is greater than 1, it means that the index is not continuous.
[0076] Step 7: Extract data points greater than 10 from index_diff. Let the extracted index column be index_diff_1. Note: 10 here is an empirical value and can be adjusted according to the actual situation.
[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] In the formula, U represents the union, that is, the union of location_end and {index[value==index_diff_1]} and {index[-1]}, and the union of location_start and {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 starting point of the interval, and location_end is the index sequence of the ending point of the interval. For each pair of starting and ending points, calculate the integral value of the difference between the data segment 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 speed mode signal.
[0080] The above location_start can be represented as the index sequence of the starting point of the above interval, and the above location_end can be represented as the index sequence of the ending point of the above interval.
[0081] The machine tool condition monitoring method of this application embodiment acquires the spindle power curve of the machine tool during the machining process and acquires the speed mode signal sequence corresponding to the spindle power curve. The speed mode signal sequence includes K speed mode signals, where K is a positive integer. It acquires feature data segments for each of the K speed mode signals; acquires the load mean of each of the K feature data segments in N machining samples, obtaining a load mean sequence corresponding to each of the K feature data segments, where N is a positive integer; acquires the Pearson correlation coefficient between the load mean sequence corresponding to each of the K feature data segments and the tool life, obtaining a Pearson correlation coefficient sequence including K Pearson correlation coefficient values; and, based on the Pearson correlation coefficient sequence, determines a target feature data segment from the K feature data segments, and determines the machine tool condition based on the target feature data segment. This improves the accuracy of the tool condition monitoring results.
[0082] See Figure 2 , Figure 2 This is a structural diagram of the machine tool tool status monitoring device provided in the embodiments of this application, as shown below. Figure 2 As shown, the machine tool tool status monitoring device 200 includes:
[0083] The first acquisition module 201 is used to acquire the spindle power curve of the machine tool during the machining process, and to acquire the speed mode signal sequence corresponding to the spindle power curve. The speed mode signal sequence includes K speed mode signals, where K is a positive integer.
[0084] The second acquisition module 202 is used to acquire the characteristic data segment of each velocity mode signal among the K velocity mode signals;
[0085] The third acquisition module 203 is used to acquire the load mean of each of the K feature data segments in N processing samples, and obtain the load mean sequence corresponding to each of the K feature data segments, where N is a positive integer;
[0086] The fourth acquisition module 204 is used to acquire the load mean sequence and the Pearson correlation coefficient corresponding to the tool life of each of the K feature data segments, so as to 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 to determine the tool state of the machine tool based on the target feature data segment.
[0088] Optionally, the determining module 205 is specifically used for:
[0089] If the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, the feature data segment corresponding to the target Pearson correlation coefficient value is determined as the target feature data segment.
[0090] The target Pearson correlation coefficient is the maximum value among the K Pearson correlation coefficient values.
[0091] Optionally, the determining module 205 is specifically used for:
[0092] If the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtain the area enclosed by the feature curve and the X-axis for each of the K feature data segments, where the X-axis is the extension direction of the feature 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 used for:
[0095] The data points corresponding to each velocity mode signal are preprocessed to obtain target data points that meet preset conditions and the index information of the target data points;
[0096] The index information of the target data points is subjected to first-order difference processing to obtain an index sequence;
[0097] Based on the index sequence, determine the index sequence of the interval start point and the index sequence of the interval end point corresponding to the target data point, and 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 starting point of the interval and the index sequence of the ending point of the interval, the characteristic data segment of the velocity mode signal is determined.
[0099] Optionally, the second acquisition module 202 is specifically used for:
[0100] Based on the index information of each starting point in the index sequence of the starting points of the interval and the index information of each ending point in the index sequence of the ending points of the interval, multiple data segments corresponding to the target data point are determined;
[0101] Obtain the integral value of each data segment in the plurality of data segments corresponding to the spindle no-load information of the machine tool, and determine the data segment with the largest integral value in the plurality of data segments as the characteristic data segment of the speed mode signal.
[0102] The machine tool tool condition monitoring device 200 provided in this application embodiment can achieve... Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.
[0103] Optionally, such as Figure 3 As shown, this application embodiment also provides a machine tool 300, including a processor 301 and a memory 302. The memory 302 stores a program or instructions that can be executed on the processor 301. When the program or instructions are executed by the processor 301, they implement the various steps of the above-described machine tool tool status monitoring method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.
[0104] It should be noted that the machine tools in the embodiments of this application include the mobile machine tools and non-mobile machine tools described above.
[0105] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described machine tool tool status monitoring method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.
[0106] The processor mentioned above is the processor in the machine tool described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.
[0107] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described machine tool tool status monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0108] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.
[0109] This application provides a computer program product, including computer instructions. When these computer instructions are executed by a processor, they implement the various processes of the above-described machine tool tool status monitoring method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.
[0110] 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. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0111] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0112] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. A method for monitoring the status of machine tool cutting tools, characterized in that, The method includes: The spindle power curve of the machine tool during the machining process is obtained, and the speed mode signal sequence corresponding to the spindle power curve is obtained. The speed mode signal sequence includes K speed mode signals, where K is a positive integer. Obtain the characteristic data segment of each velocity mode signal from the K velocity mode signals; Obtain the load mean of each of the K feature data segments in N processing samples to obtain the load mean sequence corresponding to each of the K feature data segments, where N is a positive integer; Obtain the Pearson correlation coefficient between the load mean sequence and the tool life corresponding to each of the K feature data segments, and get 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; The step of obtaining the feature data segment of each velocity mode signal among the K velocity mode signals includes: The data points corresponding to each velocity mode signal are preprocessed to obtain target data points that meet preset conditions and the index information of the target data points; The index information of the target data points is subjected to first-order difference processing to obtain an index sequence; Based on the index sequence, determine the index sequence of the interval start point and the index sequence of the interval end point corresponding to the target data point, and 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 starting point and the index sequence of the ending point of the interval, the characteristic data segment of the velocity mode signal is determined; the determination of the characteristic data segment of the velocity mode signal based on the index sequence of the starting point and the index sequence of the ending point of the interval includes: Based on the index information of each starting point in the index sequence of the starting points of the interval and the index information of each ending point in the index sequence of the ending points of the interval, multiple data segments corresponding to the target data point are determined; Obtain the integral value of each data segment in the plurality of data segments corresponding to the spindle no-load information of the machine tool, and determine the data segment with the largest integral value in the plurality of data segments as the characteristic data segment of the speed mode signal.
2. The method according to claim 1, characterized in that, The step of determining the target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes: If the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, the feature data segment corresponding to the target Pearson correlation coefficient value is determined as the target feature data segment. The target Pearson correlation coefficient 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 the target feature data segment from the K feature data segments based on the Pearson correlation coefficient sequence includes: If the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtain the area enclosed by the feature curve and the X-axis for each of the K feature data segments, where the X-axis is the extension direction of the feature 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. A machine tool cutting tool status monitoring device, characterized in that, The device includes: The first acquisition module is used to acquire the spindle power curve of the machine tool during the machining process, and to acquire the speed mode signal sequence corresponding to the spindle power curve. The speed mode signal sequence includes K speed mode signals, where K is a positive integer. The second acquisition module is used to acquire the characteristic data segment of each velocity mode signal among the K velocity mode signals; The third acquisition module is used to acquire the load mean of each of the K feature data segments in N processing samples, and obtain the load mean sequence corresponding to each of the K feature data segments, where N is a positive integer; The fourth acquisition module is used to acquire the load mean sequence and the Pearson correlation coefficient corresponding to the tool life of each of the K feature data segments, so as to obtain a Pearson correlation coefficient sequence including K Pearson correlation coefficient values. The 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 to determine the tool state of the machine tool based on the target feature data segment; The second acquisition module is specifically used for: The data points corresponding to each velocity mode signal are preprocessed to obtain target data points that meet preset conditions and the index information of the target data points; The index information of the target data points is subjected to first-order difference processing to obtain an index sequence; Based on the index sequence, determine the index sequence of the interval start point and the index sequence of the interval end point corresponding to the target data point, and 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 starting point of the interval and the index sequence of the ending point of the interval, the characteristic data segment of the velocity mode signal is determined; The second acquisition module is specifically used for: Based on the index information of each starting point in the index sequence of the starting points of the interval and the index information of each ending point in the index sequence of the ending points of the interval, multiple data segments corresponding to the target data point are determined; Obtain the integral value of each data segment in the plurality of data segments corresponding to the spindle no-load information of the machine tool, and determine the data segment with the largest integral value in the plurality of data segments as the characteristic data segment of the speed mode signal.
5. The apparatus according to claim 4, characterized in that, The determining module is specifically used for: If the target Pearson correlation coefficient value in the Pearson correlation coefficient sequence is greater than or equal to a preset value, the feature data segment corresponding to the target Pearson correlation coefficient value is determined as the target feature data segment. The target Pearson correlation coefficient is the maximum value among the K Pearson correlation coefficient values.
6. The apparatus according to claim 4, characterized in that, The determining module is specifically used for: If the Pearson correlation coefficient sequence does not include a target Pearson correlation coefficient value greater than or equal to a preset value, obtain the area enclosed by the feature curve and the X-axis for each of the K feature data segments, where the X-axis is the extension direction of the feature curve. The feature data segment with the largest enclosed area among the K feature data segments is determined as the target feature data segment.
7. A machine tool, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and the program or instructions, when executed by the processor, implement the steps of the machine tool condition monitoring method as described in any one of claims 1 to 3.
8. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the machine tool status monitoring method as described in any one of claims 1 to 3.
9. A computer program product, characterized in that, It includes computer instructions, which, when executed by a processor, implement the steps of the machine tool condition monitoring method as described in any one of claims 1 to 3.