Vibration state data management method and system based on continuous machining of machine tool

Abstract

The invention discloses a vibration state data management method and system based on continuous machining of a machine tool, and relates to the technical field of data processing, and the method comprises the steps: obtaining a machine tool sub-region, and obtaining a vibration data sequence; obtaining a difference value sequence, obtaining a plurality of forward change sections from the difference value sequence of the machine tool sub-regions according to symbols of amplitude difference values in the difference value sequence of the machine tool sub-regions, and taking the forward change sections containing the maximum amplitude difference values and exceeding a preset number as typical change sections of the machine tool sub-regions; obtaining a correlation coefficient between the machine tool sub-region and other machine tool sub-regions according to the typical change sections of the machine tool sub-region and other machine tool sub-regions; and obtaining a current correction coefficient of the machine tool sub-region according to a correlation coefficient between the machine tool sub-region and other machine tool sub-regions, and obtaining a processing strategy according to the current correction coefficient of the machine tool sub-region and the vibration amplitude at the current moment. The method has the advantages of dynamic correction, collaborative analysis and trend identification.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and system for managing vibration state data based on continuous machining of machine tools. Background Technology

[0002] In the field of high-precision manufacturing with continuous machine tool processing, especially in complex equipment scenarios involving the coordinated operation of multiple areas such as the spindle box, worktable, and guide rails, existing vibration monitoring technologies have some shortcomings.

[0003] Specifically, existing methods typically set fixed vibration thresholds for single monitoring points or analyze vibration amplitude data of each sub-region in isolation. However, they cannot effectively capture the linkage characteristics and time correlation of vibration behavior in multiple regions. During the machining process, the mechanical load, motion inertia, and thermal deformation of different sub-regions will affect each other, causing vibration energy to be transmitted and superimposed between the spindle box, worktable, and guide rail area. This coupling effect makes the threshold judgment of instantaneous vibration amplitude in a single region unable to reflect the overall operating risk of the equipment to a certain extent. More importantly, existing technologies lack analysis of vibration data trends, failing to identify sustained upward vibration trends in specific areas (such as positive change segments) or quantify the synergy of vibration events in different areas over time (such as the overlap of typical change segments). This makes it impossible to distinguish between local anomalies and precursors to multi-area chain failures. For example, when abnormal vibration occurs in the guide rail area and synergistically affects the spindle box, the fixed threshold alarm mechanism may be delayed or misjudged. At the same time, the maintenance strategy fails to dynamically adjust the threshold to adapt to the correlation strength between different areas, resulting in inaccurate equipment overload warnings, an increase in sudden shutdowns, and a decrease in machining accuracy and wear and tear on core components due to the failure to suppress linked vibrations in time. Summary of the Invention

[0004] In view of the technical problems described in the background section, the present invention provides a method and system for managing vibration state data based on continuous machining of machine tools.

[0005] A method for managing vibration status data based on continuous machine tool processing includes: acquiring multiple machine tool sub-regions, and acquiring vibration data sequences of each machine tool sub-region from the previous management time period at the current time, wherein the vibration data sequences consist of vibration amplitudes collected sequentially at multiple acquisition time points; acquiring the amplitude difference between the next vibration amplitude and the previous vibration amplitude in the vibration data sequence of the i-th machine tool sub-region to form a difference sequence for the i-th machine tool sub-region, and determining the difference sequence based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region from the i-th machine tool sub-region. Multiple positive change segments are obtained from the difference sequence of the machine tool sub-region. The positive change segment with the most amplitude difference and exceeding the preset number is taken as the typical change segment of the i-th machine tool sub-region. The correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions is obtained based on the typical change segments of the i-th machine tool sub-region and other machine tool sub-regions. The current correction coefficient of the i-th machine tool sub-region is obtained based on the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions. The processing strategy is obtained based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment.

[0006] Optionally, the method further includes: if the i-th machine tool sub-region does not have a positive change segment, or if the number of amplitude differences contained in any positive change segment of the i-th machine tool sub-region does not exceed a preset number, then the i-th machine tool sub-region is configured to not have a typical change segment, and 1 is used as the current correction coefficient of the i-th machine tool sub-region.

[0007] Optionally, obtaining multiple positive change segments from the difference sequence of the i-th machine tool sub-region based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region includes: traversing each amplitude difference in the difference sequence of the i-th machine tool sub-region, and forming a positive change segment by combining two or more amplitude differences that are sequentially adjacent and have positive signs, thereby obtaining multiple positive change segments of the i-th machine tool sub-region.

[0008] Optionally, obtaining the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions based on the typical change segments of the i-th machine tool sub-region and other machine tool sub-regions includes: obtaining the time period in which the typical change segment of the i-th machine tool sub-region is located and using it as the main time period; obtaining the time periods in which the typical change segments of other machine tool sub-regions are located and using them as the time periods to be processed; obtaining the overlapping time periods between the main time period of the i-th machine tool sub-region and the time periods to be processed in each machine tool sub-region; dividing the length of each overlapping time period by the length of the main time period to obtain each correlation ratio; and using each correlation ratio as the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions.

[0009] Optionally, obtaining the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions includes: obtaining the number of correlation coefficients between the i-th machine tool sub-region and the other machine tool sub-regions that are lower than the standard correlation threshold and using them as a first number; dividing the first number by the number of correlation coefficients between the i-th machine tool sub-region and the other machine tool sub-regions to obtain the current correction coefficient of the i-th machine tool sub-region.

[0010] Optionally, the processing strategy obtained based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment includes: multiplying half of the calibration vibration threshold of the i-th machine tool sub-region by the current correction coefficient, adding the product to half of the calibration vibration threshold, and obtaining the real-time vibration threshold of the i-th machine tool sub-region; and obtaining the processing strategy based on the vibration amplitude of the i-th machine tool sub-region at the current moment and the real-time vibration threshold.

[0011] A vibration status data management system based on continuous machine tool processing is also provided, comprising: an acquisition module for acquiring multiple machine tool sub-regions and acquiring vibration data sequences of each machine tool sub-region in the previous management time period at the current time, wherein the vibration data sequence is composed of vibration amplitudes acquired sequentially at multiple acquisition time points; and a first data processing module for acquiring the amplitude difference between the next vibration amplitude and the previous vibration amplitude in the vibration data sequence of the i-th machine tool sub-region, forming a difference sequence for the i-th machine tool sub-region, and processing the data based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region from the i-th machine tool sub-region. The first data processing module obtains multiple positive change segments from the difference sequence of the machine tool sub-regions, and selects the positive change segment containing the most amplitude differences that exceeds a preset number as the typical change segment of the i-th machine tool sub-region. The second data processing module obtains the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions based on the typical change segments of the i-th machine tool sub-region and other machine tool sub-regions. The third data processing module obtains the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions, and obtains the processing strategy based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment.

[0012] Optionally, the first data processing module is further configured to: if the i-th machine tool sub-region does not have a positive change segment, or if the number of amplitude differences contained in any positive change segment of the i-th machine tool sub-region does not exceed a preset number, then configure the i-th machine tool sub-region as having no typical change segment, and use 1 as the current correction coefficient of the i-th machine tool sub-region.

[0013] Optionally, the first data processing module is further configured to: traverse each amplitude difference in the difference sequence of the i-th machine tool sub-region, and form a positive change segment by combining two or more consecutive amplitude differences with positive signs, thereby obtaining multiple positive change segments of the i-th machine tool sub-region.

[0014] Optionally, the second data processing module is further configured to: obtain the time period in which the typical change segment of the i-th machine tool sub-region is located and use it as the main time period; obtain the time periods in which the typical change segments of other machine tool sub-regions are located and use them as the time periods to be processed; obtain the overlapping time periods between the main time period of the i-th machine tool sub-region and the time periods to be processed of each machine tool sub-region; divide the length of each overlapping time period by the length of the main time period to obtain each correlation ratio; and use each correlation ratio as the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions.

[0015] The beneficial effects of this invention are reflected in: In the entire vibration status data management method based on continuous machine tool processing, firstly, the identification of typical change segments accurately captures the continuous vibration rise process in each region, overcoming the problem of missed detection of trend anomalies in existing methods; secondly, by calculating the overlap ratio between the main time period and the time period to be processed (correlation coefficient of S3), the transmission intensity and delay effect of vibration energy between key areas such as the spindle box, worktable, and guide rail are quantified, realizing the digital characterization of mechanical coupling effect for the first time; finally, based on the dynamic correction threshold of regional coordination intensity (real-time vibration threshold generation mechanism of S4), a defense strategy is constructed—when the vibration of a certain region is strongly correlated with other regions (low correction coefficient), the alarm threshold is significantly tightened to achieve early intervention, and conversely, the restrictions are relaxed to avoid false alarms, effectively distinguishing between independent faults and cascading faults. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a schematic diagram illustrating the steps of the vibration state data management method based on continuous machining of machine tools according to the present invention; Figure 2 This is a schematic diagram of a portion of step S3 in the vibration state data management method based on continuous machine tool processing of the present invention; Figure 3 This is a schematic diagram of a portion of step S4 in the vibration state data management method based on continuous machine tool processing of the present invention; Figure 4This is a schematic diagram of another part of step S4 in the vibration state data management method based on continuous machine tool processing of the present invention; Figure 5 This is a partial flowchart of S1, S2, and S3 in the vibration state data management method based on continuous machine tool processing of the present invention; Figure 6 This is a partial flowchart of S3 in the vibration state data management method based on continuous machining of machine tools of the present invention; Figure 7 This is a partial flowchart of S3 and S4 in the vibration state data management method based on continuous machine tool processing of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] like Figure 1 , Figure 5 , Figure 6 and Figure 7 As shown, a vibration state data management method based on continuous machining of machine tools is provided. In one embodiment, the method includes: S1. Obtain multiple machine tool sub-regions, and obtain the vibration data sequence of each machine tool sub-region in the previous management time period before the current management time period, wherein the vibration data sequence is composed of vibration amplitude values ​​collected sequentially at multiple acquisition time points. S2. Obtain the amplitude difference between the next vibration amplitude and the previous vibration amplitude in the vibration data sequence of the i-th machine tool sub-region and form the difference sequence of the i-th machine tool sub-region. Based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region, obtain multiple positive change segments from the difference sequence of the i-th machine tool sub-region. The positive change segment containing the most amplitude differences and exceeding the preset number is taken as the typical change segment of the i-th machine tool sub-region. S3. Obtain the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions based on the typical variation segments of the i-th machine tool sub-region and other machine tool sub-regions. S4. Obtain the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions, and obtain the processing strategy based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment.

[0022] In this embodiment, it should be noted that in S1, during continuous machining of the machine tool, S1 serves as the initial step for vibration state management. Its core lies in building a data foundation for collaborative monitoring of multiple sub-regions. Specifically, the division of sub-regions needs to cover the key mechanical coupling points of the machine tool (such as the spindle head drive system, the worktable moving unit, and the guide rail support structure). These areas are prone to energy transfer under machining loads. For example, the rotational vibration of the spindle head may be transmitted to the worktable through the guide rail, creating a chain reaction. S1 requires the synchronous collection of historical vibration data for each sub-region during a specific management time period. This time period is not a real-time segment, but rather a data sequence selected from a complete machining cycle prior to the current moment (such as the entire process of a continuous milling task). The construction of the vibration data sequence emphasizes temporal continuity: amplitudes are recorded at multiple acquisition points using sensors with fixed frequencies (such as accelerometers), forming an ordered queue of timestamps and amplitudes. This design serves two purposes: first, it avoids misjudgments caused by mechanical backlash or instantaneous impacts at single-point instantaneous values; second, it provides a temporally coherent data foundation for subsequent analysis of vibration trends (such as the identification of positive change segments in S2). It should be noted that selecting the previous management time period instead of real-time data is intended to utilize data from completed stable processing cycles to eliminate interference from current abnormal operating conditions and ensure the reliability of typical vibration mode extraction.

[0023] Furthermore, the management time period is closely related to the characteristics of the actual machining task. For example, for the finishing process of a batch of workpieces, the management time period can be defined as 80% to 90% of the complete machining cycle of that batch (avoiding non-steady-state data during start-up and shutdown phases). Data acquisition needs to consider both physical deployment and logical correlation: the sensor network of each sub-region is deployed independently (e.g., radial sensors are installed on the spindle box, and axial sensors are arranged on the guide rail), but the acquisition time points must be strictly synchronized through the central controller clock to ensure that the data from multiple regions are aligned on the time axis. This synchronization mechanism enables the difference sequence calculation of S2 to accurately reflect the propagation delay of vibration across regions. For example, after the vibration peak of the spindle box occurs, the amplitude of the guide rail area may increase at subsequent time points (corresponding to the correlation coefficient calculation of S3). The length design of the management time period implies engineering experience: it usually covers enough machining features (e.g., multiple cutting operations) to ensure that the vibration data sequence contains typical load change cycles. For example, during the reciprocating feed of the worktable, its vibration sequence needs to completely record the amplitude evolution of the acceleration, constant speed, and deceleration phases to avoid misjudgment of trends due to truncation.

[0024] In S2, mathematical transformations convert the original vibration amplitude sequence into a data structure with quantifiable trend characteristics, thereby identifying potential abnormal evolution patterns. The generation of the difference sequence directly reflects the gradient of vibration energy change: for the vibration data sequence of the i-th sub-region (such as the spindle box), the algebraic difference between the vibration amplitude of the next vibration point and the previous vibration amplitude is calculated sequentially. For example, when the vibration amplitude of the spindle box increases continuously due to tool wear, its difference sequence will show a dense distribution of positive values; conversely, if the guide rail experiences a sudden vibration change due to poor lubrication, it may exhibit an oscillating characteristic of large positive values ​​followed by negative values. The definition of the positive change segment must satisfy the physical continuity constraint: it requires the continuous occurrence of two or more amplitude differences with positive signs, which corresponds to the continuous accumulation of vibration energy in the actual operation of the machine tool (such as the gradual increase in amplitude caused by the friction and heating of the spindle bearing). For example, when the worktable is continuously milling, if the resistance of the lead screw drive gradually increases, its vibration difference sequence may form a positive change segment containing five consecutive positive values.

[0025] Furthermore, the design of the preset quantity balances statistical validity and practical sensitivity, avoiding the misjudgment of short-term fluctuations as valid trends (such as single amplitude jumps caused by hydraulic shocks), and ensuring that the extracted typical change segments represent a stable mechanical state evolution process. Specifically, when determining the preset quantity, the initial value is first selected through a limited number of engineering experiments to ensure that 90% of short-term increases caused by operational fluctuations are avoided. Generally, one-third of the total number of points in the difference sequence can be taken (e.g., 10 for 30 difference points); for example, if the spindle box sequence contains 60 difference points, the preset quantity is 20. Then, for rigid structures (such as guide rails), the preset value is increased by 10%-20% (due to rapid vibration decay), and for flexible structures (such as spindle cantilever), it is decreased by 5%-10% (due to the tendency for vibration to persist).

[0026] Furthermore, the segment containing the most consecutive positive difference values ​​is selected from all positive change segments, and its length is required to exceed a preset number. For example, the spindle box may have three positive change segments in a certain processing cycle (containing 3, 5, and 8 positive difference values ​​respectively). If the preset number is set to 4, then only the segment containing 8 positive difference values ​​is selected as the typical change segment. The time span of this segment (from the time point before the first difference value to the time point after the last difference value) will provide a key time window for the cross-regional collaborative analysis of S3. The special treatment of no typical change segments has a clear engineering meaning: when there are only scattered positive difference values ​​in the sub-region difference value sequence (such as the guide rail area only having isolated peaks due to good rigidity), or when all positive change segments do not reach the preset number (such as the worktable having only 2 consecutive positive difference values), it is determined that there is no significant vibration acceleration trend in this area at present. At this point, the correction coefficient is forcibly set to 1, which means that half of the calibrated vibration threshold is directly used as the benchmark value in the threshold correction of S4. This is equivalent to ignoring the weight of the synergistic influence of this region on other regions, which is in line with the physical law that there is no continuous trend or significant energy transfer in mechanics.

[0027] In S3, the fault propagation intensity between sub-regions is quantified by the time alignment characteristics of typical variation segments. The definition of the main time period is directly related to the energy release cycle of the mechanical system: the typical variation segment of the i-th sub-region (such as the spindle box) corresponds to the continuous rise in its vibration energy. This time period begins at the acquisition time before the first positive difference value (reflecting the starting point of vibration enhancement) and ends at the acquisition time after the last positive difference value (corresponding to the peak inflection point of the upward trend). For example, when the spindle box exhibits a typical variation segment with 8 consecutive positive differences due to bearing wear, its main time period covers the complete process of this region from the initial intensification of friction to the amplitude reaching a local maximum value.

[0028] Furthermore, the acquisition of the time period to be processed needs to consider cross-regional response delays. Typical change segments in other sub-regions (such as the worktable and guide rails) are identified independently, which may manifest as different start and end points. For example, if a typical change segment in the guide rail area appears two acquisition cycles later than the spindle box, it indicates a time difference in the transmission of vibration energy from the spindle to the guide rail. The calculation of the overlapping time period is essentially to capture the temporal intersection of coordinated faults: after aligning the main time period of the spindle box and the time period to be processed in the guide rail along the time axis, the time interval that overlaps between the two is taken. The ratio of the length of this interval to the length of the main time period of the spindle box is the correlation coefficient—the larger this value, the higher the overlap between the vibration rise phase of the guide rail area and the energy release phase of the spindle box, suggesting a strong mechanical coupling between the two (such as the spindle vibration directly exciting the guide rail resonance through the base frame).

[0029] Furthermore, if the overlap ratio between the processing time period of a certain sub-region (such as the worktable) and the main time period of the spindle box is 80%, it indicates that the abnormal vibration of the worktable is very likely induced by the spindle box; conversely, if the overlap ratio is only 10%, the vibration of the worktable may originate from independent factors (such as feed system failure). The setting of the standard correlation threshold distinguishes between effective coordination and noise interference: In engineering, an overlap ratio of less than 30% is usually considered as having no substantial correlation (achieved through the standard correlation threshold of S41). For example, when the processing time period of the guide rail area only overlaps with the main time period of the spindle box in a short segment, its correlation coefficient will be included in the first quantity in S4. The calculation process implicitly contains the causal verification logic of the mechanical system: the main time period is preferentially based on the current analysis area (region i), and it is mandatory that vibration events in other areas must occur within the energy release period of region i to be considered correlated. This is consistent with the failure mechanism of the source region dominating the propagation path. For example, when the typical change segment of the worktable occurs earlier than the main time segment of the spindle box, the overlap ratio is zero, directly eliminating the cooperative influence of this area on the spindle box (because its vibration source is independent of the spindle box failure development period).

[0030] In S4, the current correction coefficient is essentially a quantitative indicator of regional independence. It reflects the degree to which the vibration anomaly of region i (e.g., the spindle box) is affected by the synergistic influence of other regions by calculating the proportion of correlation coefficients below the standard correlation threshold (first quantity / total number of correlation coefficients). For example, when the correlation coefficients between the spindle box and the guide rail, and the worktable are 0.8 and 0.2 respectively (the standard correlation threshold is set to 0.3), the first quantity is 1 (only the worktable's correlation is below the threshold), and the correction coefficient is 1 / 2 = 0.5. The lower this value, the more susceptible the region is to multi-regional cascading failures—in the example above, the spindle box and guide rail are strongly correlated (0.8 > 0.3), requiring more stringent control of their vibration. The calculation formula for the real-time vibration threshold implicitly incorporates a double-insurance design: multiplying half of the calibrated vibration threshold (a fixed empirical value for safe machine tool operation) by the correction coefficient signifies dynamic compression of the floating range. For example, if the spindle box calibration threshold is 10 units, and the correction factor is 0.5, the real-time vibration threshold is (10×0.5×0.5)+(10×0.5)=2.5+5=7.5 units.

[0031] Furthermore, the application of real-time vibration thresholds addresses, to some extent, the delayed misjudgment in technical issues: For the vibration amplitude of the i-th region at the current moment (e.g., the real-time amplitude of the spindle box is 7 units), it is compared with the real-time threshold. If the amplitude does not exceed the threshold (7 < 7.5), even if its absolute value is close to the existing fixed threshold (7 is close to 10), it is still considered normal—because multi-region collaborative analysis indicates that the current vibration is mainly caused by independent factors (the correction coefficient of 0.5 reflects weak collaboration). Conversely, when strong correlation exists, an early warning is triggered: If the spindle box correction coefficient is 0.9 (no substantial correlation with other regions), its real-time threshold is (10 × 0.5 × 0.9) + 5 = 9.5 units. If the amplitude reaches 8 units (below the fixed threshold but above 84% of the real-time threshold of 9.5), a secondary warning (e.g., reducing the feed rate) will be initiated to avoid waiting for the amplitude to exceed 10 units before responding. By predicting the risk transmission path through the spatiotemporal correlation of S3, the threshold can be made adaptive with the intensity of coordination. This not only prevents overreaction of independent failures (reducing invalid downtime by 30%), but also intervenes in the incipient stage of chain failures (before the amplitude exceeds the standard). To a certain extent, this solves the problem of decreased accuracy and life loss caused by unsuppressed linkage vibration in existing technologies.

[0032] In summary, this method for managing vibration status data based on continuous machine tool machining firstly, by identifying typical change segments, accurately captures the continuous vibration rise process in each region, overcoming the problem of missed detection of trend anomalies in existing methods. Secondly, by calculating the overlap ratio between the main time period and the time period to be processed (correlation coefficient of S3), the transmission intensity and delay effect of vibration energy between key areas such as the spindle box, worktable, and guide rails are quantified, achieving digital characterization of mechanical coupling effects for the first time. Finally, based on the dynamic correction threshold of regional coordination intensity (real-time vibration threshold generation mechanism of S4), a defense strategy is constructed—when the vibration of a certain region is strongly correlated with other regions (low correction coefficient), the alarm threshold is significantly tightened to achieve early intervention; conversely, the restrictions are relaxed to avoid false alarms, effectively distinguishing between independent faults and cascading faults. In conclusion, this method, to a certain extent, solves the problems of misjudgment of linked vibrations, decreased accuracy, and lifespan loss in the background technology.

[0033] In one implementation, S2 further includes: if the i-th machine tool sub-region does not have a positive change segment, or if the number of amplitude differences contained in any positive change segment of the i-th machine tool sub-region does not exceed a preset number, then the i-th machine tool sub-region is configured to not have a typical change segment, and 1 is used as the current correction coefficient of the i-th machine tool sub-region.

[0034] In this embodiment, it should be noted that this embodiment is used to distinguish between normal vibration and potential fault signals. When the difference sequence of the i-th sub-region (such as the high-rigidity guide rail region) does not contain any positive change segments (i.e., no more than two consecutive positive amplitude differences), or when all of its positive change segments are too short (e.g., the largest segment contains only 2 positive difference values ​​but the preset quantity requirement is 4), it is determined that there is no continuous energy accumulation trend in this region. For example, if the workbench is subjected to a momentary impact during workpiece loading and unloading, generating a single positive amplitude difference, it is excluded because it does not meet the condition of two consecutive positive difference values; if the three positive change segments of the guide rail region contain only 3 positive difference values ​​at most (preset requirement 5), it indicates that the vibration energy release is short-lived and has not formed an effective transmission chain.

[0035] Furthermore, forcibly setting the correction coefficient to 1 essentially enables the independent operation mode. In this mode, the area is determined to have a high degree of vibration source independence (e.g., spindle box vibration caused by its own bearing wear, without triggering guide rail resonance). Therefore, the most lenient threshold scheme is adopted in S4: half of the calibrated vibration threshold multiplied by 1 plus another half, effectively restoring it to the original calibrated threshold—this forms a logical closed loop with the correlation coefficient calculation in S3 (since there are no typical change segments, its main time period does not participate in the collaborative analysis in S31). This mechanism significantly reduces the false alarm rate; for example, when an area experiences only brief vibration caused by external handling equipment, a collaborative alarm will not be triggered.

[0036] In one implementation, S2, obtaining multiple positive change segments from the difference sequence of the i-th machine tool sub-region based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region, includes: Traverse the difference sequence of the i-th machine tool sub-region and form a positive change segment by combining two or more consecutive positive amplitude differences. This will result in multiple positive change segments of the i-th machine tool sub-region.

[0037] In this implementation, it should be noted that the essence of the traversal algorithm is to capture the continuous release cycle of mechanical energy. By sequentially scanning the difference sequence (such as the amplitude difference of 20 time points of the spindle box), a segment marker is activated when the first positive value is detected. The segment length is only extended when subsequent consecutive positive values ​​of the same sign appear. The constraint of two or more consecutive positive values ​​corresponds to the smallest time unit of fault development, indicating that the vibration energy increases in adjacent acquisition cycles (such as the continuous increase in amplitude of the spindle from t1 to t3), reflecting the smallest continuous degradation unit; three consecutive positive values ​​correspond to typical progressive faults (such as the amplitude increasing continuously for three cycles due to accelerated wear of the guide rail).

[0038] Furthermore, the strict continuity requirement of segments (adjacent and with the same sign) eliminates noise interference. For example, in the spindle box sequence, a positive-zero-positive pattern appears (due to a brief power failure of the sensor), and the zero value interrupts the continuity and does not constitute a positive change segment; similarly, in the table sequence, positive-negative-positive fluctuations (reflecting gear meshing clearance) are interrupted by negative values, preventing the segment from continuing. This mechanism ensures that each positive change segment represents a continuous evolution of the mechanical state. For example, in the case of lead screw wear, the identified 5 consecutive positive value segments precisely correspond to the time window of gradual accumulation of wear debris (from t5 to t10).

[0039] like Figure 2 As shown, in one embodiment, obtaining the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions in S3 based on the typical variation segments of the i-th machine tool sub-region and the other machine tool sub-regions includes: S31. Obtain the time period of the typical change segment of the i-th machine tool sub-region and use it as the main time period. Obtain the time periods of the typical change segments of other machine tool sub-regions and use them as the time periods to be processed. S32. Obtain the overlapping time period between the main time period of the i-th machine tool sub-region and the time period to be processed of each machine tool sub-region. Divide the length of each overlapping time period by the length of the main time period to obtain each correlation ratio, and use each correlation ratio as the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions.

[0040] In this embodiment, it should be noted that in S31, the main time period clearly defines the fault energy release window. For the typical change segment of the i-th sub-region (such as the spindle box), its range of influence on the time axis is extended to the actual vibration change range. Specifically, the starting point is moved forward to the previous acquisition time corresponding to the first amplitude difference (e.g., if the first difference of a typical change segment is calculated at time t3, then the starting point is time t2), and the ending point is postponed to the next acquisition time corresponding to the last amplitude difference (e.g., if the last difference is calculated at time t8, then the ending point is time t9). For example, when the spindle box forms a typical segment with 5 consecutive positive differences due to bearing wear (calculation points t4 to t8), its main time period actually covers t3 to t9, completely including the entire process of amplitude rising from the initial increase (t3) to reaching the peak value (t9), ensuring that subsequent correlation analysis covers the complete mechanical energy transfer cycle.

[0041] In S32, the overlap time calculation quantifies the synergistic effect strength. The main time period of the i-th region is aligned with the time periods to be processed in other sub-regions (such as the guide rail region) according to the time axis, and the proportion of the overlapping interval between the two to the main time period is calculated.

[0042] For example, if the main time period of the spindle box is 100 seconds (t1 to t100) and the waiting time period of the guide rail is 80 seconds (t20 to t99), then the overlapping interval is 80 seconds (t20 to t99), and the correlation coefficient is 80 / 100 = 0.8. This value reflects that 80% of the vibration rise process in the guide rail area occurs during the energy release period of the spindle box, suggesting that spindle vibration is the main cause of guide rail abnormalities. If the waiting time period of the worktable only overlaps with the main time period of the spindle box (t90 to t99) by 10 seconds, the correlation coefficient of 0.1 indicates that there is no substantial correlation between the two.

[0043] like Figure 3 As shown, in one embodiment, obtaining the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions in S4 includes: S41. Obtain the number of correlation coefficients between the i-th machine tool sub-region and each of the other machine tool sub-regions that are lower than the standard correlation threshold and use them as the first number. S42. Divide the first quantity by the number of correlation coefficients between the i-th machine tool sub-region and the other machine tool sub-regions, and obtain the current correction coefficient of the i-th machine tool sub-region.

[0044] In this embodiment, it should be noted that in S41, ineffective collaborative interference is isolated by counting the number of regions (e.g., the workbench) whose correlation coefficient with all other regions is lower than a standard threshold. The standard threshold is determined by first conducting a limited number of engineering experiments to establish an initial value, then filtering out independent events to obtain multiple machine tool vibration transmission events. The average of the minimum transmission time window required for these multiple machine tool vibration transmission events is used as the initial value of the standard threshold. Then, a ±5% time synchronization error is considered, and a floating range is retained to avoid incorrect screening.

[0045] For example, if the correlation coefficient between the worktable and the spindle box is 0.8 (>0.3), and with the guide rail area it is 0.1 (<0.3), then the first quantity is 1 (guide rail correlation is invalid). This step filters out non-causal vibration events, such as brief fluctuations in the guide rail (10-second overlap), from the evaluation of the synergistic effect of the worktable, ensuring that the correction coefficient only reflects effective mechanical coupling.

[0046] In S42, the correction coefficient is obtained by dividing the first quantity by the total number of regions (excluding itself). In the example above, the total number of associated objects on the workbench is 2 (spindle box, guide rail), the first quantity is 1, and the correction coefficient = 1 / 2 = 0.5. The closer this value is to 1 (e.g., 0.9), the higher the independence of the current region (most associations are invalid), and the closer it is to 0, the stronger the cascading risk (most association coefficients > 0.3). For example, when the workbench correction coefficient is 0.2 (only 1 / 5 of the associations are invalid), it indicates that its anomaly is very likely to be induced by multiple regions.

[0047] like Figure 4 As shown, in one embodiment, the processing strategy obtained in S4 based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment includes: S43. Multiply half of the calibration vibration threshold of the i-th machine tool sub-region by the current correction coefficient, and add the product to half of the calibration vibration threshold to obtain the real-time vibration threshold of the i-th machine tool sub-region. S44. Obtain the processing strategy based on the vibration amplitude and real-time vibration threshold of the i-th machine tool sub-region at the current moment.

[0048] In this embodiment, it should be noted that in S43, the dual-interval coupling protection mechanism consists of a fixed baseline and a floating interval for the real-time vibration threshold. The calibrated vibration threshold is set to 80% of the peak value of the vibration test at the factory of the new machine (e.g., a peak value of 12μm for the spindle box at the factory, with a calibrated threshold of 9.6μm). The fixed portion is always 50% of the calibrated vibration threshold (e.g., 5 units of a rigid defense line within a 10-unit threshold), and the floating portion is the remaining 50% of the calibrated vibration threshold multiplied by a correction factor. For example, when the correction factor for the spindle box is 0.5, the floating portion is compressed to 2.5 units (5 × 0.5), and the total threshold is 5 + 2.5 = 7.5 units. In cases of strong cascading risk (correction factor 0.2), the floating portion is further compressed to 1 unit (5 × 0.2), reducing the total threshold to 6 units, thus achieving risk-level control.

[0049] In S44, a layered decision-making process based on the proportion of the floating range compares the current amplitude with the real-time threshold and triggers a response according to the proportion of the amplitude to the floating range. For example, if the amplitude does not exceed the threshold, monitoring continues (e.g., 7 units < 7.5); if the amplitude exceeds the real-time threshold but is within 150% of the real-time threshold, the speed is reduced by 20%; if the amplitude exceeds the real-time threshold by 150% but is within 200% of the real-time threshold, the speed is reduced by 50% and the process is checked; if the real-time threshold is exceeded by 200%, the machine is stopped for maintenance (e.g., > 7.5 units). For example, when the spindle box amplitude reaches 8.3 units (real-time threshold 7.5), only a 20% speed reduction is triggered, which can avoid the existing method from mistakenly stopping when the amplitude exceeds 8 units.

[0050] A vibration status data management system based on continuous machining of machine tools is also provided. The system includes: The acquisition module is used to acquire multiple machine tool sub-regions and acquire the vibration data sequence of each machine tool sub-region in the previous management time period before the current management time period. The vibration data sequence is composed of vibration amplitude values ​​acquired sequentially at multiple acquisition time points. The first data processing module is used to obtain the amplitude difference between the next vibration amplitude and the previous vibration amplitude in the vibration data sequence of the i-th machine tool sub-region and form the difference sequence of the i-th machine tool sub-region. Based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region, multiple positive change segments are obtained from the difference sequence of the i-th machine tool sub-region. The positive change segment containing the most amplitude differences and exceeding the preset number is taken as the typical change segment of the i-th machine tool sub-region. The second data processing module is used to obtain the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions based on the typical change segments of the i-th machine tool sub-region and other machine tool sub-regions. The third data processing module is used to obtain the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions, and to obtain the processing strategy based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment.

[0051] In one embodiment, the first data processing module is further configured to: if the i-th machine tool sub-region does not have a positive change segment, or if the number of amplitude differences contained in any positive change segment of the i-th machine tool sub-region does not exceed a preset number, then configure the i-th machine tool sub-region as having no typical change segment, and use 1 as the current correction coefficient of the i-th machine tool sub-region.

[0052] In one embodiment, the first data processing module is further configured to: traverse each amplitude difference in the difference sequence of the i-th machine tool sub-region, and form a positive change segment by combining two or more amplitude differences that are adjacent in sequence and have positive signs, thereby obtaining multiple positive change segments of the i-th machine tool sub-region.

[0053] In one embodiment, the second data processing module is further configured to: obtain the time period in which the typical change segment of the i-th machine tool sub-region is located and use it as the main time period; obtain the time periods in which the typical change segments of other machine tool sub-regions are located and use them as the time periods to be processed; obtain the overlapping time periods between the main time period of the i-th machine tool sub-region and the time periods to be processed of each machine tool sub-region; divide the length of each overlapping time period by the length of the main time period to obtain each correlation ratio; and use each correlation ratio as the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions.

[0054] In this embodiment, it should be noted that the specific method of performing the operation of the above-mentioned vibration state data management system based on continuous machine tool machining has been described in detail in the embodiments of the vibration state data management method based on continuous machine tool machining, and will not be elaborated here.

[0055] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0056] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0057] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for managing vibration state data based on continuous machining of machine tools, characterized in that, include: Multiple machine tool sub-regions are acquired, and vibration data sequences of each machine tool sub-region in the previous management time period at the current time are acquired. The vibration data sequences are composed of vibration amplitudes collected sequentially at multiple acquisition time points. Obtain the amplitude difference between the next vibration amplitude and the previous vibration amplitude in the vibration data sequence of the i-th machine tool sub-region and form the difference sequence of the i-th machine tool sub-region. Based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region, obtain multiple positive change segments from the difference sequence of the i-th machine tool sub-region. The positive change segment containing the most amplitude differences and exceeding the preset number is taken as the typical change segment of the i-th machine tool sub-region. The correlation coefficients between the i-th machine tool sub-region and the other machine tool sub-regions are obtained based on the typical variation segments of the i-th machine tool sub-region and the other machine tool sub-regions. The current correction coefficient of the i-th machine tool sub-region is obtained based on the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions, and the processing strategy is obtained based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment.

2. The vibration state data management method based on continuous machining of machine tools according to claim 1, characterized in that, Also includes: If the i-th machine tool sub-region does not have a positive change segment, or if the number of amplitude differences contained in any positive change segment of the i-th machine tool sub-region does not exceed a preset number, then the i-th machine tool sub-region is configured to not have a typical change segment, and 1 is used as the current correction coefficient of the i-th machine tool sub-region.

3. The vibration state data management method based on continuous machining of machine tools according to claim 1, characterized in that, The step of obtaining multiple positive change segments from the difference sequence of the i-th machine tool sub-region based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region includes: Traverse the difference sequence of the i-th machine tool sub-region and form a positive change segment by combining two or more consecutive positive amplitude differences. This will result in multiple positive change segments of the i-th machine tool sub-region.

4. The vibration state data management method based on continuous machining of machine tools according to any one of claims 2 or 3, characterized in that, The process of obtaining the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions based on the typical variation segments of the i-th machine tool sub-region and other machine tool sub-regions includes: Obtain the time period of the typical change segment of the i-th machine tool sub-region and use it as the main time period; obtain the time periods of the typical change segments of other machine tool sub-regions and use them as the time periods to be processed. Obtain the overlapping time periods between the main time period of the i-th machine tool sub-region and the time periods to be processed of each machine tool sub-region. Divide the length of each overlapping time period by the length of the main time period to obtain the correlation ratio, and use each correlation ratio as the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions.

5. The vibration state data management method based on continuous machining of machine tools according to claim 4, characterized in that, The step of obtaining the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions includes: Get the number of correlation coefficients between the i-th machine tool sub-region and all other machine tool sub-regions that are lower than the standard correlation threshold, and use them as the first quantity; Divide the first quantity by the number of correlation coefficients between the i-th machine tool sub-region and all other machine tool sub-regions to obtain the current correction coefficient of the i-th machine tool sub-region.

6. The vibration state data management method based on continuous machining of machine tools according to claim 5, characterized in that, The processing strategy based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment includes: Multiply half of the calibrated vibration threshold of the i-th machine tool sub-region by the current correction coefficient, and add the product to half of the calibrated vibration threshold to obtain the real-time vibration threshold of the i-th machine tool sub-region. The processing strategy is obtained based on the vibration amplitude and real-time vibration threshold of the i-th machine tool sub-region at the current moment.

7. A vibration state data management system based on continuous machining of machine tools, characterized in that, The system includes: The acquisition module is used to acquire multiple machine tool sub-regions and acquire the vibration data sequence of each machine tool sub-region in the previous management time period before the current management time period. The vibration data sequence is composed of vibration amplitude values ​​acquired sequentially at multiple acquisition time points. The first data processing module is used to obtain the amplitude difference between the next vibration amplitude and the previous vibration amplitude in the vibration data sequence of the i-th machine tool sub-region and form the difference sequence of the i-th machine tool sub-region. Based on the sign of each amplitude difference in the difference sequence of the i-th machine tool sub-region, multiple positive change segments are obtained from the difference sequence of the i-th machine tool sub-region. The positive change segment containing the most amplitude differences and exceeding the preset number is taken as the typical change segment of the i-th machine tool sub-region. The second data processing module is used to obtain the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions based on the typical change segments of the i-th machine tool sub-region and other machine tool sub-regions. The third data processing module is used to obtain the current correction coefficient of the i-th machine tool sub-region based on the correlation coefficient between the i-th machine tool sub-region and other machine tool sub-regions, and to obtain the processing strategy based on the current correction coefficient of the i-th machine tool sub-region and the vibration amplitude at the current moment.

8. The vibration state data management system based on continuous machining of machine tools according to claim 7, characterized in that, The first data processing module is also used for: If the i-th machine tool sub-region does not have a positive change segment, or if the number of amplitude differences contained in any positive change segment of the i-th machine tool sub-region does not exceed a preset number, then the i-th machine tool sub-region is configured to not have a typical change segment, and 1 is used as the current correction coefficient of the i-th machine tool sub-region.

9. The vibration state data management system based on continuous machining of machine tools according to claim 7, characterized in that, The first data processing module is also used for: Traverse the difference sequence of the i-th machine tool sub-region and form a positive change segment by combining two or more consecutive positive amplitude differences. This will result in multiple positive change segments of the i-th machine tool sub-region.

10. The vibration state data management system based on continuous machining of machine tools according to claim 7, characterized in that, The second data processing module is also used for: Obtain the time period of the typical change segment of the i-th machine tool sub-region and use it as the main time period; obtain the time periods of the typical change segments of other machine tool sub-regions and use them as the time periods to be processed. Obtain the overlapping time periods between the main time period of the i-th machine tool sub-region and the time periods to be processed of each machine tool sub-region. Divide the length of each overlapping time period by the length of the main time period to obtain the correlation ratio, and use each correlation ratio as the correlation coefficient between the i-th machine tool sub-region and the other machine tool sub-regions.

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