A grinding vibration management data processing method and system for a grinding machine

Abstract

The present invention discloses a grinding vibration management data processing method and system for a grinding machine, which relates to the field of data processing technology. The method comprises: obtaining real-time grinding machine vibration data and real-time grinding feed rate, obtaining sampled grinding machine vibration data and sampled grinding feed rate; obtaining real-time vibration displacement data in the X, Y, and Z axes, obtaining real-time three-axis feed rates in the X, Y, and Z axes, obtaining sampled vibration displacement data in the X, Y, and Z axes, obtaining sampled feed rates in the X, Y, and Z axes, obtaining X-axis vibration variation, Y-axis vibration variation, and Z-axis vibration variation, obtaining X-axis feed variation, Y-axis feed variation, and Z-axis feed variation; obtaining X-axis abnormality index, Y-axis abnormality index, and Z-axis abnormality index, marking abnormal axial direction and marking normal axial direction; obtaining real-time vibration displacement data and real-time feed rate, and obtaining corrected vibration displacement data. The present invention has the advantages of three-axis solution processing, sampling adaptation, and abnormality correction.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a grinding vibration management data processing method and system for a grinding machine. Background Art

[0002] Existing grinding machine vibration monitoring systems all employ an absolute vibration monitoring mechanism with a fixed threshold. This leads to frequent false alarms during high-feed grinding and the risk of missed alarms during low-feed fine grinding. Specifically, when a high feed rate is used during the rough grinding phase, structural vibrations generated by normal cutting loads are often misidentified as abnormal vibrations, triggering unnecessary downtime and maintenance, reducing production efficiency. Furthermore, during the fine grinding phase, under low feed rates, weak vibration signals that represent true abnormalities are ignored because they do not reach the fixed threshold, leading to surface quality defects in the workpiece. Furthermore, existing technologies fail to consider the dynamic coupling relationship between feed rate and vibration amplitude, making it impossible to distinguish between normal vibration exacerbated by increased feed rate and true abnormal vibration caused by equipment failure. Furthermore, the differences in the correlation between axial vibration and feed rate are ignored, making the fixed threshold unable to adapt to the dynamic characteristics of different axes. Existing methods use a static reference benchmark and fail to establish a dynamic comparison mechanism that matches the machining phase (rough grinding / fine grinding), resulting in a mismatch between monitoring sensitivity and operating conditions. These defects together result in low reliability of vibration monitoring during the grinding process, which not only increases unplanned downtime losses but also makes it difficult to ensure the surface processing quality of high-precision parts. Summary of the Invention

[0003] In view of the defects in the prior art, the present invention provides a grinding vibration management data processing method and system for a grinding machine.

[0004] A grinding vibration management data processing method for a grinding machine, comprising: obtaining real-time grinding machine vibration data and real-time grinding feed amount of the grinding machine at the current moment, presetting a sampling time period, obtaining a sampling moment separated by the sampling time period before the current moment, and obtaining sampled grinding machine vibration data and sampled grinding feed amount of the grinding machine at the sampling moment; obtaining real-time vibration displacement data in the three axes of X, Y, and Z according to the real-time grinding machine vibration data, and obtaining real-time three-axis feed amounts in the three axes of X, Y, and Z according to the real-time grinding feed amount, and obtaining sampled vibration displacement data in the three axes of X, Y, and Z according to the sampled grinding machine vibration data, and obtaining sampled feed amounts in the three axes of X, Y, and Z according to the sampled grinding feed amount, and subtracting the sampled vibration displacement data from the real-time vibration displacement data to obtain X-axis vibration variation, Y-axis vibration variation, and Z-axis vibration variation, and converting the real-time vibration displacement data into the sampled vibration displacement data. The feed amount is subtracted from the sampling feed amount to obtain the X-axis feed change, Y-axis feed change and Z-axis feed change; the X-axis abnormality index, Y-axis abnormality index and Z-axis abnormality index are obtained according to the X-axis vibration change, Y-axis vibration change, Z-axis vibration change, X-axis feed change, Y-axis feed change and Z-axis feed change, and it is judged whether the X-axis abnormality index, Y-axis abnormality index or Z-axis abnormality index exceeds the abnormal threshold value respectively. If it exceeds, the corresponding axis is marked as an abnormal axis; if it does not exceed, the corresponding axis is marked as a normal axis; the real-time vibration displacement data and real-time feed amount of the abnormal axis are obtained, and the corrected vibration displacement data of the abnormal axis is obtained based on the correction model, the real-time vibration displacement data of the abnormal axis and the real-time feed amount, and monitoring and management are carried out according to the corrected vibration displacement data of the abnormal axis and the real-time vibration displacement data of the normal axis.

[0005] Optionally, the correction model in obtaining the abnormal axial corrected vibration displacement data based on the correction model, the abnormal axial real-time vibration displacement data, and the real-time feed rate is expressed as: ;in, is the corrected vibration displacement data of the abnormal axial direction, is the real-time vibration displacement data of abnormal axial direction, is the minimum attenuation coefficient, is the reduction factor, is the real-time feed rate of the abnormal axis, is the standard feed rate of the abnormal axis, and exp is the exponential function.

[0006] Optionally, the preset sampling time period includes: setting the sampling time period according to the current processing stage of the grinder, wherein the processing stage includes a coarse grinding stage, a fine grinding stage and a remaining stage, and the sampling time period of the remaining stage is set as a standard time period, the sampling time period of the coarse grinding stage is set to be shorter than the sampling time period of the remaining stage, and the sampling time period of the fine grinding stage is set to be longer than the sampling time period of the remaining stage.

[0007] Optionally, obtaining real-time vibration displacement data in the three axes of X, Y, and Z based on the real-time grinder vibration data includes: axially separating the real-time grinder vibration data to extract vibration components in the three independent orthogonal directions of X, Y, and Z respectively; and eliminating high-frequency electromagnetic interference and resonance noise from the vibration components in the three independent directions of X, Y, and Z to obtain real-time vibration displacement data in the three axes of X, Y, and Z.

[0008] Optionally, obtaining the real-time three-axis feed in the X, Y, and Z directions based on the real-time grinding feed includes: decoupling the real-time grinding feed and extracting the motion components in the three independent orthogonal directions of X, Y, and Z respectively; collecting the servo motor encoder feedback signal and the ball screw displacement sensor data, and verifying and matching them with the motion components in the three independent orthogonal directions of X, Y, and Z; if the matching is successful, using the motion components in the three independent orthogonal directions of X, Y, and Z as the real-time three-axis feed in the X, Y, and Z directions.

[0009] Optionally, obtaining the X-axis abnormality index, the Y-axis abnormality index, and the Z-axis abnormality index based on the X-axis vibration change, the Y-axis vibration change, the Z-axis vibration change, the X-axis feed change, the Y-axis feed change, and the Z-axis feed change includes: obtaining the X-axis abnormality index based on the X-axis vibration change and the X-axis feed change; obtaining the Y-axis abnormality index based on the Y-axis vibration change and the Y-axis feed change; and obtaining the Z-axis abnormality index based on the Z-axis vibration change and the Z-axis feed change.

[0010] Optionally, the X-axis abnormality index is obtained based on the X-axis vibration change and the X-axis feed change as follows: ;in, is the X-axis abnormality indicator, is the X-axis feed variation, is the X-axis vibration variation, and sgn is the sign function.

[0011] A grinding vibration management data processing system for a grinding machine is also provided, and the system includes: a data acquisition module for acquiring real-time grinding machine vibration data and real-time grinding feed amount of the grinding machine at the current moment, and presetting a sampling time period, and acquiring a sampling moment separated by the sampling time period before the current moment, and acquiring sampled grinding machine vibration data and sampled grinding feed amount of the grinding machine at the sampling moment; a first data processing module for acquiring real-time vibration displacement data in the three axes of X, Y, and Z according to the real-time grinding machine vibration data, and acquiring real-time three-axis feed amount in the three axes of X, Y, and Z according to the real-time grinding feed amount, and acquiring sampled vibration displacement data in the three axes of X, Y, and Z according to the sampled grinding machine vibration data, and acquiring sampled feed amount in the three axes of X, Y, and Z according to the sampled grinding feed amount, and subtracting the sampled vibration displacement data from the real-time vibration displacement data to acquire X-axis vibration change amount, Y-axis vibration change amount, and Z-axis vibration change amount, and The real-time feed amount is subtracted from the sampling feed amount to obtain the X-axis feed change amount, Y-axis feed change amount and Z-axis feed change amount; the second data processing module is used to obtain the X-axis abnormality index, Y-axis abnormality index and Z-axis abnormality index according to the X-axis vibration change amount, Y-axis vibration change amount, Z-axis vibration change amount, X-axis feed change amount, Y-axis feed change amount and Z-axis feed change amount, and respectively determine whether the X-axis abnormality index, Y-axis abnormality index or Z-axis abnormality index exceeds the abnormal threshold value, if exceeded, the corresponding axis is marked as an abnormal axis, if not exceeded, the corresponding axis is marked as a normal axis; the third data processing module is used to obtain the real-time vibration displacement data and real-time feed amount of the abnormal axis, and obtain the corrected vibration displacement data of the abnormal axis based on the correction model, the real-time vibration displacement data of the abnormal axis and the real-time feed amount, and monitor and manage according to the corrected vibration displacement data of the abnormal axis and the real-time vibration displacement data of the normal axis.

[0012] Optionally, the data acquisition module is also used to: set a sampling time period according to the current processing stage of the grinder, wherein the processing stage includes a rough grinding stage, a fine grinding stage and a remaining stage, and the sampling time period of the remaining stage is set to a standard time period, the sampling time period of the rough grinding stage is set to be shorter than the sampling time period of the remaining stage, and the sampling time period of the fine grinding stage is set to be longer than the sampling time period of the remaining stage.

[0013] Optionally, the second data processing module is also used to: obtain an X-axis abnormality index based on the X-axis vibration change and the X-axis feed change; obtain a Y-axis abnormality index based on the Y-axis vibration change and the Y-axis feed change; and obtain a Z-axis abnormality index based on the Z-axis vibration change and the Z-axis feed change.

[0014] The beneficial effects of the present invention are embodied in:

[0015] The entire grinding machine grinding vibration management data processing method significantly improves the reliability of grinding process diagnosis by establishing a dynamic coupling vibration monitoring mechanism for the entire process. First, based on the adaptive sampling time period setting of the processing stage (rough grinding / fine grinding), a short time window is used to capture rapid dynamic responses during rough grinding to avoid false alarms, while an extended time window is used to enhance weak signal detection and prevent missed alarms during fine grinding. Furthermore, pure three-axis vibration and feed rate are extracted through axial decoupling and multi-source verification, and the vibration-feed synergistic variation is innovatively calculated to quantify the essential difference between normal process load and true faults. Furthermore, independent axial diagnosis is achieved based on abnormal indicators based on the synergistic relationship of variation. When vibration and feed rate show abnormal opposite fluctuations, abnormal axial directions are accurately marked, fundamentally distinguishing between structural vibration and equipment failure. Furthermore, the sensitivity is intelligently adjusted through a feed-dependent vibration correction model. The vibration amplitude is compressed to suppress false alarms in high-feed conditions, and the original value is retained in low-feed conditions to ensure weak signal detection. Normal axial directions are directly monitored to ensure full spatial domain coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0017] Figure 1 A schematic diagram of the steps of a grinding machine grinding vibration management data processing method according to one embodiment of the present invention;

[0018] Figure 2 Schematic diagram of a portion of steps S1 in the grinding vibration management data processing method for a grinding machine according to the present invention;

[0019] Figure 3 Schematic diagram of a portion of step S2 in the grinding vibration management data processing method for a grinding machine according to the present invention;

[0020] Figure 4 Schematic diagram of another part of the steps of S2 in the grinding vibration management data processing method of the grinding machine of the present invention;

[0021] Figure 5 This is a schematic diagram of a portion of step S3 in the grinding vibration management data processing method for a grinding machine according to the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0024] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. In addition, the terms "first," "second," etc. are used only to distinguish the descriptions and are not to be understood as indicating or implying relative importance.

[0025] like Figure 1 As shown, a method for processing grinding vibration management data of a grinding machine is provided, comprising:

[0026] S1. Acquire real-time grinding machine vibration data and real-time grinding feed rate of the grinding machine at the current moment, preset a sampling time period, acquire a sampling time interval before the current moment, and acquire sampled grinding machine vibration data and sampled grinding feed rate of the grinding machine at the sampling time period;

[0027] S2. Acquire real-time vibration displacement data in the X, Y, and Z axes according to the real-time grinder vibration data, and acquire real-time three-axis feed amounts in the X, Y, and Z axes according to the real-time grinding feed amount, and acquire sampled vibration displacement data in the X, Y, and Z axes according to the sampled grinder vibration data, and acquire sampled feed amounts in the X, Y, and Z axes according to the sampled grinding feed amount, and subtract the sampled vibration displacement data from the real-time vibration displacement data to acquire X-axis vibration variation, Y-axis vibration variation, and Z-axis vibration variation, and subtract the sampled feed amount from the real-time feed amount to acquire X-axis feed variation, Y-axis feed variation, and Z-axis feed variation;

[0028] S3. Obtain an X-axis abnormality index, a Y-axis abnormality index, and a Z-axis abnormality index according to the X-axis vibration variation, the Y-axis vibration variation, the Z-axis vibration variation, the X-axis feed variation, the Y-axis feed variation, and the Z-axis feed variation, and respectively determine whether the X-axis abnormality index, the Y-axis abnormality index, or the Z-axis abnormality index exceeds an abnormality threshold; if so, mark the corresponding axis as an abnormal axis; if not, mark the corresponding axis as a normal axis;

[0029] S4. Acquire the real-time vibration displacement data and real-time feed rate of the abnormal axial direction, and acquire the corrected vibration displacement data of the abnormal axial direction based on the correction model, the real-time vibration displacement data of the abnormal axial direction, and the real-time feed rate, and perform monitoring and management according to the corrected vibration displacement data of the abnormal axial direction and the real-time vibration displacement data of the normal axial direction.

[0030] In this embodiment, it should be noted that in S1, a variable sampling period is first preset. This period is not a fixed value but is dynamically adjusted based on the grinder's current processing stage (e.g., rough grinding, fine grinding, or remaining stage). Specifically, when the grinder is in the high-speed, high-feed rough grinding stage, the sampling period is set to a shorter duration to quickly capture short-term vibration changes and avoid false alarms. Conversely, during the low-speed, low-feed fine grinding stage, the sampling period is extended to provide a more stable reference baseline to enhance interference resistance and reduce noise impact. The remaining stages use a standard time duration. Subsequently, two key data sets are acquired: the current moment's real-time grinder vibration data and real-time grinding feed rate, which reflect the current operating status; and the current moment's sampled grinding machine vibration data and sampled grinding feed rate, which are obtained by tracing back to the sampling time interval preceding the current moment. This provides a temporal comparison basis, ensuring that the monitoring mechanism can adapt to the dynamic impact of feed rate changes.

[0031] Furthermore, assuming the grinder is performing a rough grinding operation (e.g., removing a large amount of material at high speed), a short sampling period (e.g., a window of several seconds) is set based on the characteristics of this stage, and sampling data is extracted from the moment before this period as a proximal reference to match the rapid dynamic response requirements of rough grinding. In contrast, during the fine grinding stage (e.g., fine machining of small surfaces), a long sampling period (e.g., a window of tens of seconds) is used, and sampling data is taken from an earlier point in time, providing a smooth historical baseline to suppress high-frequency interference. This dynamic setting avoids misjudgment of abnormalities due to aggravation of normal vibration during rough grinding, while effectively identifying weak abnormal signals during fine grinding, improving the adaptability and reliability of monitoring. The entire process does not introduce additional data processing; simply by strategically adjusting the time range and dual data collection, it lays a flexible foundation for subsequent vibration anomaly determination.

[0032] In S2, the raw vibration and feed rate data are converted into dynamic variations along three axes to establish an axially independent condition monitoring benchmark. This step first performs axial decoupling and noise suppression on the real-time and sampled grinding machine vibration data: orthogonal decomposition techniques are used to separate pure vibration components in the three orthogonal directions of X, Y, and Z. Digital filtering techniques are then used to actively eliminate high-frequency electromagnetic interference and mechanical resonance noise (such as grinding wheel spindle harmonic interference) to ensure that the extracted vibration displacement data truly reflects the movement of the mechanical structure. Simultaneously, a motion decoupling algorithm is used to extract the three-axis independent feed components for the real-time and sampled grinding feed rates. The servo motor encoder data is then integrated with the ball screw displacement signal for real-time verification (for example, sensor data matching is performed on the Y-axis feed component). After verifying the reliability of the data, it is used as a three-axis feed benchmark. This process resolves the problem of multi-axis coupling interference and lays the data foundation for independent analysis of each axis.

[0033] Furthermore, the change evaluation is then performed based on the spatiotemporal dimensions. The real-time vibration displacement data of the three axes at the current moment is differentially calculated with the historical data of the corresponding axes at the sampling moment to generate the vibration change of each axis (such as the fluctuation value of the current vibration amplitude of the Z axis compared to the sampling moment); the difference between the real-time feed of the three axes and the feed at the sampling moment is simultaneously calculated to obtain the feed change (such as the offset of the current feed speed of the X axis from the historical benchmark). This dynamic comparison mechanism directly captures abnormal characteristics by quantifying the coordinated changes of "vibration-feed" within a short-term window. For example, when the Z-axis feed drops sharply and the vibration increases, it may indicate a bearing seizure fault, while the same vibration amount is attributed to normal load when the rough grinding feed increases. By independently calculating the change in each axis, the defect of fixed thresholds being insensitive to axial dynamic characteristics is avoided, providing accurate input for subsequent abnormality judgment.

[0034] In S3, an independent dynamic anomaly determination mechanism is constructed for each axis, identifying true faults by quantifying the synergistic relationship between vibration and feed changes. This step independently calculates anomaly indicators for each axis (such as the Z-axis feed). Its design principle focuses on capturing the anomalous coupling characteristics of vibration and feed rate: when the feed change and vibration change in a particular axis show an inverse correlation (for example, a sudden drop in feed rate and a sharp increase in vibration), it is determined to be an abnormal state; conversely, if the two change in the same direction (for example, an increase in feed rate accompanied by increased vibration), it is attributed to normal operating load fluctuations. This dynamic correlation analysis fundamentally distinguishes vibration anomalies caused by equipment failure from structural vibration caused by changes in process parameters, resolving the problem that traditional fixed thresholds cannot adapt to the dynamic influence of feed rate.

[0035] Furthermore, the abnormality indicator calculation and determination process utilizes an axial decoupling strategy to improve fault location accuracy. Dedicated abnormality indicators are calculated for the X, Y, and Z axes. Each indicator is mathematically integrated with the corresponding axis's vibration and feed changes (e.g., analyzing their synergistic trends through specific sign functions) to generate a numerical value quantifying the degree of abnormality. A unified abnormality determination threshold is set for all axes, but each axis performs independent determinations: when an axis's abnormality indicator exceeds the threshold (indicating a significant divergence between its vibration behavior and feed changes), the axis is labeled "abnormal"; otherwise, it is labeled "normal." For example, during the fine grinding stage, if a slight adjustment in the Y-axis feed causes an abnormal surge in vibration (the indicator exceeds the threshold), an alarm is triggered even if the absolute vibration value is relatively small, effectively mitigating the risk of missed alarms. Conversely, during the rough grinding stage, if a significant increase in the X-axis feed causes increased vibration, the indicator value remains below the threshold, avoiding false alarms. This axis-independent determination mechanism precisely matches the dynamic characteristics of each axis, providing a reliable basis for classification and diagnosis during the S4 stage.

[0036] In S4, a differentiated vibration monitoring mechanism is established for each axis, enabling adaptive adjustment of abnormal sensitivity through dynamic correction. For abnormal axes identified in step S3 (e.g., the Z axis, which was misidentified as abnormal due to high feed rates during rough grinding), a feed-dependent intelligent attenuation strategy is implemented. This strategy extracts the axis's real-time feed rate and applies a weighted adjustment to the raw vibration displacement data using a specific correction model. When the feed rate increases significantly (e.g., during the rough grinding phase), the model automatically compresses the vibration data amplitude (e.g., by 30%-70%), thereby reducing the vibration amplification caused by normal loads and avoiding false alarm shutdowns. Conversely, when the feed rate is minimal or decreasing (e.g., during fine grinding phase), the model only makes small adjustments or even retains the original vibration value (e.g., by only 5%-10%), ensuring that true abnormal signals are not masked. This correction logic essentially transforms the physical relationship between feed rate and vibration into a mathematical suppression coefficient, directly addressing the persistent problem of false alarms during high-feed conditions.

[0037] Furthermore, for normal axes, raw vibration data is retained for direct monitoring, enabling precise diagnosis across the entire spatial domain. For all axes identified as normal by S3 (e.g., the X-axis, where feed and vibration change in the same direction during the rough grinding phase), uncorrected real-time vibration displacement data is used for threshold comparison, maintaining inherent monitoring sensitivity. Ultimately, two types of data are simultaneously output: corrected vibration displacement data for the abnormal axis (e.g., the Y-axis's low-frequency vibration after compression) and raw vibration data for the normal axis (e.g., the X-axis's unprocessed high-frequency vibration), which are fed into a unified monitoring module for alarm decision-making. For example, during the fine grinding phase, if the Z-axis is marked as abnormal but the actual feed rate is extremely low, the correction model retains nearly all of its weak original vibration value. In this case, even a 10-micron abnormal vibration caused by a bearing fault can trigger an effective alarm despite its small absolute magnitude. Furthermore, a 50-micron "pseudo-abnormal" vibration on the Y-axis caused by high feed rate during the rough grinding phase is effectively reduced to a safe range of 20 microns after correction, avoiding unintended downtime. This two-way adjustment mechanism ensures that monitoring sensitivity is consistently and dynamically aligned with process requirements.

[0038] In summary, the entire grinding machine grinding vibration management data processing method significantly improves the reliability of grinding process diagnosis by establishing a dynamic coupling vibration monitoring mechanism for the entire process. First, based on the adaptive sampling time period setting of the processing stage (rough grinding / fine grinding), a short time window is used to capture rapid dynamic responses during rough grinding to avoid false alarms, while an extended time window is used during fine grinding to enhance weak signal detection and prevent missed alarms. Furthermore, pure three-axis vibration and feed rate are extracted through axial decoupling and multi-source verification, and the vibration-feed synergistic variation is innovatively calculated to quantify the essential difference between normal process loads and true faults. Furthermore, independent axial diagnosis is achieved based on abnormal indicators based on the synergistic relationship of variation. When vibration and feed rate show abnormal opposite fluctuations, abnormal axial directions are accurately marked, fundamentally distinguishing between structural vibration and equipment failures. Furthermore, the sensitivity is intelligently adjusted through a feed-dependent vibration correction model. The vibration amplitude is compressed to suppress false alarms in high-feed conditions, while the original value is retained in low-feed conditions to ensure weak signal detection. Normal axial directions are directly monitored to ensure full spatial domain coverage.

[0039] In one embodiment, the correction model in S4 for obtaining the corrected vibration displacement data of the abnormal axial direction based on the correction model, the real-time vibration displacement data of the abnormal axial direction, and the real-time feed rate is expressed as:

[0040] ;in,

[0041] is the corrected vibration displacement data of the abnormal axial direction, is the real-time vibration displacement data of abnormal axial direction, is the minimum attenuation coefficient, is the reduction factor, is the real-time feed rate of the abnormal axis, is the standard feed rate of the abnormal axis, is an exponential function.

[0042] In this embodiment, it should be noted that It is the core of the expression and represents the dynamic attenuation ratio. It achieves adaptive scaling through three parts. The first part is the fixed bottom attenuation. , ensuring that even with extremely large feed rates, vibration data is compressed to times (such as Indicates that 20% is retained) to avoid the vibration being completely ignored in extreme cases. The second part is dynamic attenuation , as the feed rate increases, the exponential term decays rapidly, amplifying the overall compression effect. The third part is ,As a normalization process, the real time feed amount is mapped to the proportion of the relative standard working condition to eliminate the dimensional effect.

[0043] Furthermore, in high feed conditions ( ), Approaches 0, the dynamic attenuation ratio approaches , thus achieving the goal of suppressing the vibration amplification caused by normal load. ), Approaching , the dynamic attenuation ratio approaches , resulting in almost no attenuation of the vibration data (k≈1), retaining weak abnormal signals.

[0044] Further, The attenuation lower limit is set to ensure that Small, but still retain the minimum monitoring sensitivity. At the same time, the reduction factor To achieve controlled attenuation speed, Large, a slight increase in feed rate will trigger strong attenuation (suitable for vibration-sensitive scenarios); when Smaller → Smooth attenuation transition (adaptable to gradual working conditions).

[0045] In summary, the problem of false alarms in the rough grinding stage is solved. In the prior art, the fixed threshold cannot distinguish between "normal vibration due to large feed" and "real fault", resulting in frequent false shutdowns. In the expression of this embodiment, when the rough grinding feed rate increases significantly, that is, The dynamic coefficient k is greatly improved and drops to For example, a 50μm vibration (normal load) is equivalent to 10μm after being corrected by the dynamic attenuation ratio (0.2), which is much lower than the alarm threshold (such as 30μm), thus avoiding misjudgment. Furthermore, reliable alarms are ensured during the fine grinding stage. Specifically, during the fine grinding stage, The dynamic attenuation ratio is close to 0, and the dynamic attenuation ratio is close to 1, ensuring that the corrected vibration displacement data will not be reduced; for example, the 8μm abnormal vibration caused by a bearing failure is completely retained, combined with the lower judgment threshold (such as 5μm) in the fine grinding stage, an alarm is accurately triggered. Furthermore, the axial characteristic mismatch is solved, and the correction coefficient is calculated independently for each abnormal axis, such as: Z axis (main cutting direction), Larger, Small, slow decay to adapt to large feed range; X / Y axis (auxiliary direction), Smaller, Large, fast decay to deal with sensitive vibration; under the same large feed condition, the Z-axis vibration compression amplitude is smaller than the X / Y axis, matching the axial dynamic differences. Furthermore, exponential decay simulates the physical law, and the vibration energy increases nonlinearly with the increase of load, but the abnormal vibration caused by the fault has no such correlation with the feed rate; the coefficient The minimum setting corresponds to the system noise bottom line. Even if the feed rate is zero, the environmental vibration still exists (such as motor harmonics), and the basic monitoring capability needs to be retained; at the same time, the absolute value processing , indicating that the reverse feed rate will also induce abnormal vibration and requires the same monitoring. In summary, this implementation decouples "vibration caused by process load" from "vibration caused by equipment failure," achieving real-time process adaptation of monitoring sensitivity.

[0046] like Figure 2 As shown, in one embodiment, the preset sampling time period in S1 includes:

[0047] S11. Setting a sampling time period according to the current processing stage of the grinder, wherein the processing stages include a coarse grinding stage, a fine grinding stage, and a remaining stage, and the sampling time period of the remaining stage is set as a standard time period, the sampling time period of the coarse grinding stage is set to be shorter than the sampling time period of the remaining stage, and the sampling time period of the fine grinding stage is set to be longer than the sampling time period of the remaining stage.

[0048] In this embodiment, it should be noted that the core of the sampling time period setting in S11 is the dynamic identification and response of the machining phase. Specifically, the current state of the grinding machine, namely, rough grinding, fine grinding, or residual grinding, is automatically determined based on the real-time operating conditions of the grinding machine (such as spindle load, feed rate, and process parameters). The rough grinding phase (high feed rate, high material removal rate) uses a very short sampling period to simultaneously track transient vibrations caused by rapid changes in cutting load. For example, when the grinding wheel removes residual stock at high speed, the vibration of the machine tool structure may fluctuate violently within milliseconds. A short time window (e.g., 0.5-2 seconds) can instantly capture sudden vibration changes, avoiding the misinterpretation of normal load vibration as a fault due to historical data lag. The fine grinding phase (micro feed rate, high precision requirements) uses a significantly longer sampling period. By expanding the time window (e.g., 10-30 seconds), more historical vibration data is aggregated to form a steady-state baseline. This can suppress the masking of weak abnormal signals by high-frequency noise (such as motor harmonics and environmental interference), ensuring that even abnormal bearing vibrations at the 0.1μm level can be effectively identified during surface finishing, eliminating missed reports at the root.

[0049] Furthermore, this step further reconstructs the reference benchmark for vibration monitoring by precisely matching the stage characteristics with the time window: the remaining stages (non-coarse grinding / fine grinding transition state) use a standard time period (e.g., 5 seconds) as the balance point between the coarse grinding and fine grinding strategies, taking into account both response speed and stability.

[0050] In summary, the short-term window provides a "near-end dynamic baseline" during the rough grinding phase, preventing false alarms from vibration excursions caused by sudden increases in feed rate. The long-term window generates a "far-end steady-state baseline" during the fine grinding phase, enhancing noise sensitivity to subtle anomalies. For example, during rough grinding, a short sampling window compares only the vibration state from a few seconds ago, quickly identifying whether the current high feed rate is causing an abnormal deviation. During fine grinding, a long sampling window smoothes data over tens of seconds, stripping away process noise and revealing true fault signals. This dynamic baseline fundamentally addresses the monitoring failure problem caused by fixed thresholds, which cannot distinguish between process load and equipment failure.

[0051] like Figure 3 As shown, in one embodiment, obtaining real-time vibration displacement data in the X, Y, and Z axes according to the real-time grinding machine vibration data in S2 includes:

[0052] S21, performing axial separation on the real-time grinding machine vibration data to extract vibration components in three independent orthogonal directions: X, Y, and Z;

[0053] S22. Eliminate high-frequency electromagnetic interference and resonance noise from the vibration components in the three independent directions of X, Y, and Z, thereby obtaining real-time vibration displacement data in the three axes of X, Y, and Z.

[0054] In this embodiment, it should be noted that in S21, the core goal of axial separation is to solve the vector coupling problem of multi-degree-of-freedom vibration signals. Since the essence of grinding machine vibration is the vector superposition of X, Y, and Z motions, traditional overall monitoring will mask the axial specificity. This step adopts the orthogonal reference frame decomposition technology: based on the machine tool body coordinate system (such as the spindle axis is the Z axis and the transverse guide is the X axis), the original vibration data is decoupled into three independent orthogonal directions of pure vibration components through multi-sensor fusion (such as a three-axis accelerometer array) or signal processing algorithms (such as time domain projection). For example, when the grinding wheel is cutting in the Z-axis direction, the X / Y axis may only carry the structural resonance component, and axial separation can accurately separate the Z-direction main cutting vibration to avoid misjudgment caused by interference in all directions. This process ensures the directional independence of vibration monitoring and lays a physical foundation for subsequent axial differentiation analysis.

[0055] In the S22, active noise reduction is implemented for the decoupled axial vibration component to eliminate non-structural parasitic interference. High-frequency electromagnetic interference (e.g., above 10kHz) generated by motor drive circuits, inverters, etc. is suppressed by digital band-stop filters targeting specific frequency bands (e.g., harmonics of the motor fundamental frequency). Mechanical resonance noise (e.g., 500-2000Hz) is addressed by adaptive notch filtering or spectral subtraction techniques to dynamically track and eliminate resonance peaks caused by grinding wheel imbalance, drive chain backlash, and other factors.

[0056] For example, if the separated Z-axis vibration component exhibits a narrowband peak at 1500Hz (not caused by cutting force), the noise reduction module will identify and filter out this resonant component, retaining only the valid cutting vibration signal from 0-500Hz. This process, through combined frequency-domain and time-domain purification, ensures that the extracted vibration displacement data reflects only the actual mechanical structure movement, completely avoiding the risk of noise masking weak abnormal signals.

[0057] like Figure 4 As shown, in one embodiment, obtaining the real-time three-axis feed amounts in the X, Y, and Z directions according to the real-time grinding feed amounts in S2 includes:

[0058] S23, decoupling the real-time grinding feed and extracting the motion components in three independent orthogonal directions of X, Y, and Z respectively;

[0059] S24, collecting the feedback signal of the servo motor encoder and the data of the ball screw displacement sensor, and verifying and matching them with the motion components in the three independent orthogonal directions of X, Y, and Z;

[0060] If the matching is successful, the motion components in the three independent orthogonal directions of X, Y, and Z are used as the real-time three-axis feed in the X, Y, and Z directions.

[0061] In this embodiment, it should be noted that in S23, the core of motion decoupling is to break the vector synthesis interference under multi-axis linkage. The grinding feed is essentially the vector synthesis result of the X, Y, and Z three-dimensional motion. Traditional overall monitoring will confuse the axial dynamic characteristics. This step decomposes the synthetic feed into three independent axial motion components through coordinate transformation. For example, when performing bevel grinding, the synthetic feed instruction output by the control is reversely decomposed into X-axis horizontal displacement, Z-axis vertical feed, and Y-axis lateral compensation components to ensure that each axial motion is independently quantified. This process establishes a precise mapping at the mechanical transmission level for subsequent axial differentiated monitoring, fundamentally avoiding misjudgment caused by multi-axis coupling.

[0062] In S24, multi-sensor closed-loop verification is implemented for the decoupled motion components, aiming to eliminate interference caused by deviations between CNC commands and mechanical execution. Its core function is to verify electromechanical consistency. The encoder feedback signal captures the servo motor rotor angular displacement in real time and converts it into theoretical axial displacement using the transmission ratio. The displacement sensor data directly measures the actual physical displacement of the ball screw or guide rail. These two components are compared in real time with the decomposed motion components in S23. If the theoretical displacement, command component, and actual displacement match within the tolerance band (for example, the error between the Y-axis command displacement and the measured value on the scale is less than 0.1%), the data is deemed valid. This closed-loop mechanism ensures the physical authenticity and reliability of the feed rate data through electromechanical cross-verification.

[0063] It should also be noted that the method used to obtain the sampled vibration displacement data in the X, Y, and Z axes based on the sampled grinder vibration data and to obtain the sampled feed amount in the X, Y, and Z axes based on the sampled grinding feed amount can be the same as the method in the above two embodiments.

[0064] like Figure 5 As shown, in one embodiment, in S3, obtaining the X-axis abnormality index, the Y-axis abnormality index, and the Z-axis abnormality index according to the X-axis vibration change, the Y-axis vibration change, the Z-axis vibration change, the X-axis feed change, the Y-axis feed change, and the Z-axis feed change includes:

[0065] S31, obtaining an X-axis abnormality index according to the X-axis vibration variation and the X-axis feed variation;

[0066] S32, obtaining a Y-axis abnormality index based on the Y-axis vibration change and the Y-axis feed change;

[0067] S33. Obtain a Z-axis abnormality index according to the Z-axis vibration variation and the Z-axis feed variation.

[0068] In this embodiment, it should be noted that the construction principle of the X-axis anomaly indicator in S31 focuses on the mathematical quantification of the vibration-feed synergistic trend. Its core logic captures two key physical characteristics: Using specific calculations to strengthen the anomaly weight when the feed rate decreases dramatically (e.g., a sudden drop in feed during tool retraction); and when the vibration increment and feed increment show a negative correlation (i.e., vibration increases with a decrease in feed), determining that this divergence indicates equipment anomaly. In S32, this logic is continued, and the synergistic trend is mathematically quantified. In S33, this logic is also continued, and the synergistic trend is mathematically quantified.

[0069] In one embodiment, the X-axis abnormality index is obtained according to the X-axis vibration variation and the X-axis feed variation in S31 and is expressed as:

[0070] ;in,

[0071] is the X-axis abnormality indicator, is the X-axis feed variation, is the X-axis vibration variation, is a symbolic function.

[0072] In this embodiment, it should be noted that, in the entire expression, Represents the X-axis feed change, Positive values ​​indicate an increase in feed. Negative values ​​represent reduced feed; Similarly, it represents the X-axis vibration change. Positive values ​​represent increased vibration. Negative values ​​indicate reduced vibration; is a sign function that outputs 1, 0, or +1.

[0073] Further, In the example, the output of feed reduction is 1, and the output of feed increase is -1, marking the feed reduction behavior, which contributes to the abnormal index when the feed is reduced. If the same direction changes ( and If the same sign) the output is 1, if the reverse change ( and If the value is of opposite sign, the output is -1. A negative value indicates that the vibration and feed are in opposite directions (the core feature of the fault). , to achieve enhanced reverse divergence, and only contribute to the abnormal index when the vibration feed changes in the opposite direction. The final synthesis logic, when the feed minus + same direction vibration, is 1; when feed increases + same direction vibration, is -1; when feed minus + reverse vibration, is 2; when feed increase + reverse vibration, 0; thus penalizing the maximum scenario: when the feed rate drops suddenly, the vibration increases abnormally, corresponding to a typical mechanical failure (such as a stuck guide rail).

[0074] In summary, this expression utilizes three synergistic diagnostic methods: feed direction monitoring to detect feed behavior anomalies; coupled contradiction detection to quantify physical correlation anomalies; and penalty weight allocation to focus on high-risk scenarios. This approach addresses the disconnect between vibration monitoring and process parameters, proactively suppressing false alarms for normal load vibration (same-direction variations); enhancing sensitivity for subtle true faults (opposite-direction deviations); and providing a comprehensive dead-zone detection solution for latent faults (zero feed variation). This represents a paradigm shift in vibration anomaly diagnosis from "absolute threshold" to "physical correlation quantification."

[0075] It should also be noted that both S32 and S33 can use the expression of S31 for data processing, just substitute the corresponding data.

[0076] A grinding vibration management data processing system for a grinding machine is also provided, the system comprising:

[0077] a data acquisition module, configured to acquire real-time grinding machine vibration data and real-time grinding feed rate of the grinding machine at the current moment, preset a sampling time period, acquire a sampling moment that is separated by the sampling time period before the current moment, and acquire sampled grinding machine vibration data and sampled grinding feed rate of the grinding machine at the sampling moment;

[0078] a first data processing module, configured to obtain real-time vibration displacement data in the X, Y, and Z axes based on the real-time grinder vibration data, obtain real-time three-axis feed amounts in the X, Y, and Z axes based on the real-time grinding feed amount, obtain sampled vibration displacement data in the X, Y, and Z axes based on the sampled grinder vibration data, obtain sampled feed amounts in the X, Y, and Z axes based on the sampled grinding feed amount, subtract the sampled vibration displacement data from the real-time vibration displacement data to obtain an X-axis vibration variation, a Y-axis vibration variation, and a Z-axis vibration variation, and subtract the sampled feed amount from the real-time feed amount to obtain an X-axis feed variation, a Y-axis feed variation, and a Z-axis feed variation;

[0079] a second data processing module, configured to obtain an X-axis abnormality index, a Y-axis abnormality index, and a Z-axis abnormality index based on the X-axis vibration variation, the Y-axis vibration variation, the Z-axis vibration variation, the X-axis feed variation, the Y-axis feed variation, and the Z-axis feed variation, and respectively determine whether the X-axis abnormality index, the Y-axis abnormality index, or the Z-axis abnormality index exceeds an abnormality threshold; if so, mark the corresponding axis as an abnormal axis; if not, mark the corresponding axis as a normal axis;

[0080] The third data processing module is used to obtain the real-time vibration displacement data and real-time feed rate of the abnormal axial direction, and obtain the corrected vibration displacement data of the abnormal axial direction based on the correction model, the real-time vibration displacement data of the abnormal axial direction and the real-time feed rate, and monitor and manage according to the corrected vibration displacement data of the abnormal axial direction and the real-time vibration displacement data of the normal axial direction.

[0081] In one embodiment, the data acquisition module is further used to: set a sampling time period according to the current processing stage of the grinder, wherein the processing stage includes a coarse grinding stage, a fine grinding stage and a remaining stage, and the sampling time period of the remaining stage is set as a standard time period, the sampling time period of the coarse grinding stage is set to be shorter than the sampling time period of the remaining stage, and the sampling time period of the fine grinding stage is set to be longer than the sampling time period of the remaining stage.

[0082] In one embodiment, the second data processing module is also used to: obtain an X-axis abnormality index based on the X-axis vibration change and the X-axis feed change; obtain a Y-axis abnormality index based on the Y-axis vibration change and the Y-axis feed change; and obtain a Z-axis abnormality index based on the Z-axis vibration change and the Z-axis feed change.

[0083] In this embodiment, it should be noted that, regarding the above-mentioned grinding machine grinding vibration management data processing system, the specific method of performing operations has been described in detail in the embodiment of the grinding machine grinding vibration management data processing method, and will not be elaborated here.

[0084] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the scope of protection of the present invention.

[0085] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. To avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0086] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A grinding vibration management data processing method for a grinding machine, characterized in that: include: Acquire real-time grinding machine vibration data and real-time grinding feed rate of the grinding machine at the current moment, preset a sampling time period, acquire a sampling time interval before the current moment, and acquire sampled grinding machine vibration data and sampled grinding feed rate of the grinding machine at the sampling time period; Acquire real-time vibration displacement data in the X, Y, and Z axes according to the real-time grinder vibration data, acquire real-time three-axis feed amounts in the X, Y, and Z axes according to the real-time grinding feed amount, acquire sampled vibration displacement data in the X, Y, and Z axes according to the sampled grinder vibration data, acquire sampled feed amounts in the X, Y, and Z axes according to the sampled grinding feed amount, subtract the sampled vibration displacement data from the real-time vibration displacement data to acquire X-axis vibration variation, Y-axis vibration variation, and Z-axis vibration variation, and subtract the sampled feed amount from the real-time feed amount to acquire X-axis feed variation, Y-axis feed variation, and Z-axis feed variation; Obtain an X-axis abnormality index, a Y-axis abnormality index, and a Z-axis abnormality index according to the X-axis vibration variation, the Y-axis vibration variation, the Z-axis vibration variation, the X-axis feed variation, the Y-axis feed variation, and the Z-axis feed variation, and respectively determine whether the X-axis abnormality index, the Y-axis abnormality index, or the Z-axis abnormality index exceeds an abnormality threshold; if so, mark the corresponding axis as an abnormal axis; if not, mark the corresponding axis as a normal axis; Acquire real-time vibration displacement data and real-time feed rate of the abnormal axial direction, acquire corrected vibration displacement data of the abnormal axial direction based on the correction model, the real-time vibration displacement data of the abnormal axial direction and the real-time feed rate, and perform monitoring and management according to the corrected vibration displacement data of the abnormal axial direction and the real-time vibration displacement data of the normal axial direction; The modified model is expressed as: ;in, is the corrected vibration displacement data of the abnormal axial direction, is the real-time vibration displacement data of abnormal axial direction, is the minimum attenuation coefficient, is the reduction factor, is the real-time feed rate of the abnormal axis, is the standard feed rate of the abnormal axis, is an exponential function.

2. The grinding vibration management data processing method for a grinding machine according to claim 1, characterized in that: The preset sampling time period includes: The sampling time period is set according to the current processing stage of the grinder, wherein the processing stage includes a coarse grinding stage, a fine grinding stage and a remaining stage, and the sampling time period of the remaining stage is set as a standard time period, the sampling time period of the coarse grinding stage is set to be shorter than the sampling time period of the remaining stage, and the sampling time period of the fine grinding stage is set to be longer than the sampling time period of the remaining stage.

3. The grinding vibration management data processing method for a grinding machine according to claim 1, characterized in that: The method of obtaining real-time vibration displacement data in the X, Y, and Z axes according to the real-time grinding machine vibration data includes: Axial separation is performed on the real-time grinding machine vibration data to extract the vibration components in three independent orthogonal directions: X, Y, and Z. The vibration components in the three independent directions of X, Y, and Z are processed to eliminate high-frequency electromagnetic interference and resonance noise, thereby obtaining real-time vibration displacement data in the three axes of X, Y, and Z.

4. The grinding vibration management data processing method for a grinding machine according to claim 1, characterized in that: The method of obtaining the real-time three-axis feed in the X, Y, and Z directions according to the real-time grinding feed includes: Decouple the real-time grinding feed and extract the motion components in three independent orthogonal directions: X, Y, and Z. Collect the feedback signal of the servo motor encoder and the data of the ball screw displacement sensor, and verify and match them with the motion components in the three independent orthogonal directions of X, Y, and Z; If the matching is successful, the motion components in the three independent orthogonal directions of X, Y, and Z are used as the real-time three-axis feed in the X, Y, and Z directions.

5. The grinding vibration management data processing method for a grinding machine according to claim 1, characterized in that: The step of obtaining the X-axis abnormality index, the Y-axis abnormality index, and the Z-axis abnormality index according to the X-axis vibration variation, the Y-axis vibration variation, the Z-axis vibration variation, the X-axis feed variation, the Y-axis feed variation, and the Z-axis feed variation includes: Obtain X-axis abnormality indicators based on X-axis vibration variation and X-axis feed variation; Obtain the Y-axis abnormality index based on the Y-axis vibration change and the Y-axis feed change; The Z-axis abnormality index is obtained based on the Z-axis vibration change and the Z-axis feed change.

6. The grinding vibration management data processing method for a grinding machine according to claim 5, characterized in that: The X-axis abnormality index obtained according to the X-axis vibration change and the X-axis feed change is expressed as: ;in, is the X-axis abnormality indicator, is the X-axis feed variation, is the X-axis vibration variation, is a symbolic function.

7. A grinding vibration management data processing system for a grinding machine, characterized in that: The system comprises: a data acquisition module, configured to acquire real-time grinding machine vibration data and real-time grinding feed rate of the grinding machine at the current moment, preset a sampling time period, acquire a sampling moment that is separated by the sampling time period before the current moment, and acquire sampled grinding machine vibration data and sampled grinding feed rate of the grinding machine at the sampling moment; a first data processing module, configured to obtain real-time vibration displacement data in the X, Y, and Z axes based on the real-time grinder vibration data, obtain real-time three-axis feed amounts in the X, Y, and Z axes based on the real-time grinding feed amount, obtain sampled vibration displacement data in the X, Y, and Z axes based on the sampled grinder vibration data, obtain sampled feed amounts in the X, Y, and Z axes based on the sampled grinding feed amount, subtract the sampled vibration displacement data from the real-time vibration displacement data to obtain an X-axis vibration variation, a Y-axis vibration variation, and a Z-axis vibration variation, and subtract the sampled feed amount from the real-time feed amount to obtain an X-axis feed variation, a Y-axis feed variation, and a Z-axis feed variation; a second data processing module, configured to obtain an X-axis abnormality index, a Y-axis abnormality index, and a Z-axis abnormality index based on the X-axis vibration variation, the Y-axis vibration variation, the Z-axis vibration variation, the X-axis feed variation, the Y-axis feed variation, and the Z-axis feed variation, and respectively determine whether the X-axis abnormality index, the Y-axis abnormality index, or the Z-axis abnormality index exceeds an abnormality threshold; if so, mark the corresponding axis as an abnormal axis; if not, mark the corresponding axis as a normal axis; a third data processing module, configured to obtain real-time vibration displacement data and real-time feed rate of the abnormal axial direction, obtain corrected vibration displacement data of the abnormal axial direction based on the correction model, the real-time vibration displacement data of the abnormal axial direction, and the real-time feed rate, and perform monitoring and management based on the corrected vibration displacement data of the abnormal axial direction and the real-time vibration displacement data of the normal axial direction; The modified model is expressed as: ;in, is the corrected vibration displacement data of the abnormal axial direction, is the real-time vibration displacement data of abnormal axial direction, is the minimum attenuation coefficient, is the reduction factor, is the real-time feed rate of the abnormal axis, is the standard feed rate of the abnormal axis, is an exponential function.

8. The grinding vibration management data processing system for a grinding machine according to claim 7, characterized in that: The data acquisition module is also used for: The sampling time period is set according to the current processing stage of the grinder, wherein the processing stage includes a coarse grinding stage, a fine grinding stage and a remaining stage, and the sampling time period of the remaining stage is set as a standard time period, the sampling time period of the coarse grinding stage is set to be shorter than the sampling time period of the remaining stage, and the sampling time period of the fine grinding stage is set to be longer than the sampling time period of the remaining stage.

9. The grinding vibration management data processing system for a grinding machine according to claim 7, characterized in that: The second data processing module is further configured to: Obtain X-axis abnormality indicators based on X-axis vibration variation and X-axis feed variation; Obtain the Y-axis abnormality index based on the Y-axis vibration change and the Y-axis feed change; The Z-axis abnormality index is obtained based on the Z-axis vibration change and the Z-axis feed change.

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