Lead screw guide rail data acquisition method and system

Abstract

The invention relates to the technical field of lead screw guide rail data acquisition, in particular to a lead screw guide rail data acquisition method and system, and the method comprises the following steps: obtaining a vibration signal of a lead screw guide rail; preprocessing the vibration signal to obtain a preprocessed vibration signal; self-adaptive noise suppression is carried out on the preprocessed vibration signal, and a noise-suppressed vibration signal is obtained; performing time-frequency domain conversion on the vibration signal subjected to noise suppression to obtain time-frequency distribution of the vibration signal; extracting transient high-frequency impact characteristics from the time-frequency distribution; dynamically adjusting the judgment rule of the early damage state according to the operation condition of the lead screw guide rail; according to the transient high-frequency impact characteristics and a judgment rule, the early damage state of the lead screw guide rail is judged; according to the judgment result of the early damage state, graded early warning is sent out; through obtaining the vibration signal of the lead screw guide rail and carrying out preprocessing and self-adaptive noise suppression, the purity of the signal is effectively improved.

Description

A method and system for acquiring data from lead screw guides Technical Field

[0001] This invention relates to the field of lead screw guide rail data acquisition technology, and in particular to a lead screw guide rail data acquisition method and system. Background Technology

[0002] In modern precision manufacturing, the lead screw guide system, as a core transmission component, is crucial for ensuring machining accuracy, equipment stability, and production efficiency through accurate monitoring of its operating status. In actual production, to respond to market demands or optimize efficiency, production departments often adjust equipment process parameters (such as increasing the idle travel speed and acceleration / deceleration settings of the lead screw guide). While this can improve efficiency, it significantly alters the operating conditions of the lead screw guide.

[0003] Under the new high-speed, high-acceleration / deceleration mode, the stress distribution in the contact between the lead screw and the balls changes, leading to high-frequency localized impacts of the balls within the circulation channel. Simultaneously, the formation and maintenance of the lubricating oil film are affected, easily resulting in insufficient localized lubrication and increased micro-sliding friction. These phenomena can induce high-frequency vibrations (frequency reaching several kilohertz or higher) that are not noticeable under normal operating conditions. These high-frequency vibrations are key early warning signals for early localized damage such as microcracks on the ball surface and pitting / stripping on the thread surface.

[0004] However, process parameter adjustments are often not promptly and comprehensively synchronized with the equipment maintenance department, causing the data acquisition system to continue using a low, fixed sampling frequency set under normal operating conditions (e.g., only suitable for low-frequency vibrations of several hundred hertz). This sampling frequency cannot effectively capture newly generated high-frequency vibration signals, resulting in undersampling (or misjudging them as low-frequency signals, or even complete omission) of key high-frequency information related to early damage.

[0005] In the absence of effective high-frequency information, conventional vibration spectrum analysis of the collected data will fail to accurately reflect the actual operating status of the lead screw guide. In particular, the analysis system will be unable to identify early signs of failure related to localized impacts and micro-slippage. This directly leads to the system's inability to issue timely warnings, as the data upon which its judgment is based is incomplete. Based on this incomplete analysis report, maintenance personnel may misjudge the lead screw guide's operating condition as normal or with only minor wear, thus failing to arrange necessary maintenance measures in a timely manner. As the equipment continues to operate without effective monitoring and maintenance, the microscopic damage initially caused by localized impacts and micro-slippage will further aggravate and spread rapidly, ultimately leading to a sharp decline in the lead screw guide's operating accuracy, producing noticeable abnormal noises and vibrations, and even potentially causing ball jamming or lead screw seizure, resulting in unplanned downtime and significant economic losses.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by proposing a data acquisition method and system for lead screw guideways.

[0008] In a first aspect, the present invention provides a data acquisition method for a lead screw guide rail, the method comprising the following steps: acquiring a vibration signal of the lead screw guide rail; preprocessing the vibration signal to obtain a preprocessed vibration signal; performing adaptive noise suppression on the preprocessed vibration signal to obtain a noise-suppressed vibration signal; performing time-frequency domain transformation on the noise-suppressed vibration signal to obtain a time-frequency distribution of the vibration signal; extracting transient high-frequency impact features from the time-frequency distribution; dynamically adjusting the judgment rules for early damage state according to the operating conditions of the lead screw guide rail; judging the early damage state of the lead screw guide rail according to the transient high-frequency impact features and the judgment rules; and issuing a graded early warning based on the judgment result of the early damage state.

[0009] Secondly, a data acquisition system for a lead screw guide rail is provided. This system includes: a vibration signal acquisition module for acquiring vibration signals of the lead screw guide rail; a preprocessing module for preprocessing the vibration signals to obtain preprocessed vibration signals; an adaptive noise suppression module for adaptively suppressing noise in the preprocessed vibration signals to obtain noise-suppressed vibration signals; a time-frequency domain conversion module for performing time-frequency domain conversion on the noise-suppressed vibration signals to obtain the time-frequency distribution of the vibration signals; a feature extraction module for extracting transient high-frequency impact features from the time-frequency distribution; a judgment rule adjustment module for dynamically adjusting the judgment rules for early damage states based on the operating conditions of the lead screw guide rail; a damage state judgment module for judging the early damage state of the lead screw guide rail based on the transient high-frequency impact features and the judgment rules; and an early warning module for issuing graded early warnings based on the judgment results of the early damage state.

[0010] Compared with existing technologies, this invention has the following advantages: By acquiring the vibration signal of the lead screw guide and performing preprocessing and adaptive noise suppression, the purity of the signal is effectively improved. Based on this, time-frequency domain transformation of the noise-suppressed vibration signal can comprehensively capture the time-frequency distribution of the vibration signal, laying the foundation for subsequent analysis. Crucially, this application extracts transient high-frequency impact characteristics from the time-frequency distribution and dynamically adjusts the judgment rules for early damage states according to the operating conditions of the lead screw guide. This innovative design overcomes the limitations of fixed sampling frequencies and judgment rules in existing technologies, enabling accurate identification of weak high-frequency damage signals generated under different operating conditions. Finally, based on the transient high-frequency impact characteristics and dynamically adjusted judgment rules, the early damage state of the lead screw guide is accurately determined, and graded early warnings are issued based on the judgment results.

[0011] Through the above technical solution, this application effectively solves the problems in the prior art where high-frequency damage signals are undersampled or missed due to the failure to synchronize process adjustments in a timely manner and the use of a low fixed sampling frequency in the data acquisition system. This avoids unplanned downtime and huge economic losses caused by the inability to identify early damage in a timely manner. This application can detect early damage to the lead screw guide rail in a timely and accurate manner, providing a scientific basis for equipment maintenance and significantly improving the operational reliability and production efficiency of the equipment. Attached Figure Description

[0012] Figure 1 is a flowchart of the method of the present invention.

[0013] Figure 2 is a schematic diagram of the system structure of the present invention.

[0014] In the diagram: 201, Vibration signal acquisition module; 202, Preprocessing module; 203, Adaptive noise suppression module; 204, Time-frequency domain conversion module; 205, Feature extraction module; 206, Judgment rule adjustment module; 207, Damage state judgment module; 208, Early warning module. Detailed Implementation

[0015] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0016] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Traditional lead screw guide systems generally suffer from fixed and low sampling frequencies in their operational status monitoring. When production process adjustments cause changes in equipment operating conditions, especially during high-speed, high-acceleration / deceleration operation, the system cannot effectively capture high-frequency vibration signals associated with early localized damage. This limitation in data acquisition leads to inaccurate analysis results, hinders the timely identification of early fault precursors, delays maintenance, and may ultimately result in decreased equipment accuracy, unplanned downtime, and economic losses.

[0018] In response, this application proposes a data acquisition method for lead screw guide rails. To make the technical solution of this application easier and clearer to understand, some key terms involved are explained first.

[0019] "Vibration signal" refers to the mechanical vibration generated by the lead screw guide during operation, which carries important information about the health status of the equipment. "Transient high-frequency impact characteristics" refer to short-duration, high-energy impact signals caused by localized damage (such as microcracks or pitting) within the vibration signal; these signals typically have high-frequency components. "Operating condition" refers to the operating state of the lead screw guide within a specific time period, including parameters such as operating speed, load, and acceleration / deceleration. "Early damage state" refers to minute, localized signs of damage that appear before a serious failure occurs in the lead screw guide; timely detection and treatment of these damages can effectively prevent more serious failures. The implementation environment of this application is typically an industrial production site, where the lead screw guide operates as a key component in machine tools, automated production lines, and other equipment.

[0020] Figure 1 illustrates a data acquisition method for a lead screw guide rail. The method includes the following steps: S101, acquiring the vibration signal of the lead screw guide rail. It should be noted that the vibration signal can be acquired in various ways. For example, a piezoelectric accelerometer can be used, installed at key parts of the lead screw guide rail, such as the bearing housing or guide rail slider, to convert mechanical vibration into an electrical signal. Another method is to use a laser Doppler vibrometer to measure the vibration velocity on the surface of the lead screw guide rail in a non-contact manner, thereby acquiring the vibration signal. Fiber optic sensors can also be used, utilizing technologies such as fiber Bragg gratings (FBGs) to indirectly acquire vibration information by monitoring fiber strain.

[0021] S102. Preprocess the vibration signal to obtain a preprocessed vibration signal. It should be noted that the purpose of preprocessing is to eliminate irrelevant components in the signal and improve the signal-to-noise ratio. For example, bandpass filtering can be applied to the original vibration signal to filter out power frequency interference and high-frequency noise, retaining the frequency components related to the operation of the lead screw and guide rail. Detrending processing can also be performed to eliminate DC components or low-frequency drift in the signal. Furthermore, resampling can be performed to adjust the signal sampling rate to a suitable range to meet the needs of subsequent analysis.

[0022] S103. Adaptive noise suppression is applied to the preprocessed vibration signal to obtain a noise-suppressed vibration signal. It should be noted that adaptive noise suppression aims to effectively remove environmental noise and non-damage-related noise generated by the equipment itself. For example, adaptive filters, such as the Least Mean Square (LMS) algorithm or the Recursive Least Squares (RLS) algorithm, can be used to suppress noise by introducing a reference noise source or utilizing the statistical characteristics of the signal and adjusting the filter parameters in real time. Another approach is to use wavelet thresholding denoising, which decomposes the signal into different scales through wavelet transform, thresholds the wavelet coefficients, and then reconstructs the signal to achieve denoising. Empirical Mode Decomposition (EMD) or Variational Mode Decomposition (VMD) can also be used to decompose the signal into a series of intrinsic mode functions (IMFs), identify and remove noise modes.

[0023] S104. Perform time-frequency domain transformation on the noise-suppressed vibration signal to obtain its time-frequency distribution. It should be noted that the purpose of time-frequency domain transformation is to reveal the joint distribution characteristics of the signal in time and frequency, which is particularly important for analyzing transient impact signals. For example, the Short-Time Fourier Transform (STFT) can be used. By sliding a window function across the signal and performing a Fourier transform on the signal within each window, the time-frequency distribution can be obtained. Another approach is to use wavelet transform. By selecting appropriate wavelet basis functions, multi-scale analysis of the signal can be performed to obtain a time-frequency distribution with good time-frequency localization characteristics. Alternatively, the Hilbert-Huang Transform (HHT) can be used. Through empirical mode decomposition and Hilbert transform, the instantaneous frequency and instantaneous amplitude of the signal can be obtained, thereby constructing the time-frequency distribution.

[0024] S105. Extracting transient high-frequency impact features from the time-frequency distribution; it should be noted that transient high-frequency impact features are important indicators of early damage. For example, regions with concentrated energy, short duration, and high frequency in the time-frequency distribution can be identified, and these regions may correspond to transient impact events. For these events, statistical features such as instantaneous energy, kurtosis factor, and kurtosis factor can be extracted. The energy envelope of the impact event can also be analyzed to extract its decay rate and duration. Furthermore, the frequency variation of the impact event can be analyzed to extract its center frequency shift and bandwidth variation.

[0025] S106. The system dynamically adjusts the judgment rules for early damage states based on the operating conditions of the lead screw guide. It should be noted that traditional fixed judgment rules are prone to failure when operating conditions change. For example, normal vibration characteristic baselines can be pre-established for different operating conditions (such as high speed, low speed, heavy load, and light load). When the operating conditions of the lead screw guide change, the system can automatically identify the current operating condition and call upon the corresponding judgment rules or adjust the judgment threshold. This dynamic adjustment ensures that the judgment rules always match the actual operating conditions, improving the accuracy of the judgment.

[0026] S107. Based on the transient high-frequency impact characteristics and judgment rules, determine the early damage state of the lead screw guide. It should be noted that the extracted transient high-frequency impact characteristics can be compared with the dynamically adjusted judgment rules. If the feature value exceeds the threshold under the current operating condition, early damage is determined to exist. The judgment process can employ a rule-based expert system or a machine learning model, such as a support vector machine (SVM) or neural network, to build a classification model by training a large amount of normal and early damage data.

[0027] S108. Based on the assessment results of the early damage status, issue a graded early warning.

[0028] It should be noted that the tiered early warning system aims to provide different levels of alert information and action guidance to personnel in different roles based on the severity and urgency of the damage. For example, when the damage is judged to be minor early stage, a low-level warning can be issued to the maintenance engineer, suggesting an inspection during the next scheduled maintenance. When the damage is judged to be moderate early stage, a medium-level warning can be issued to the operator, suggesting a reduction in operating speed or load, and an urgent inspection notice can be issued to the maintenance engineer. When the damage is judged to be severe early stage, a high-level warning can be issued to the operator, requiring immediate shutdown, and production scheduling recommendations can be issued to production management personnel.

[0029] The data acquisition method for lead screw guideways in this application effectively improves signal quality by acquiring vibration signals from the lead screw guideway and performing preprocessing and adaptive noise suppression. Subsequently, the time-frequency distribution of the vibration signal is obtained through time-frequency domain transformation, and transient high-frequency impact characteristics are extracted from it. This enables the system to capture early damage signals that are difficult to detect using traditional methods. Crucially, this application can dynamically adjust the judgment rules for early damage states according to the operating conditions of the lead screw guideway, thus overcoming the drawback of traditional fixed judgment rules failing when operating conditions change. This dynamic adjustment mechanism ensures the accuracy and adaptability of the judgment. Finally, based on the transient high-frequency impact characteristics and the dynamically adjusted judgment rules, the early damage state of the lead screw guideway is determined, and graded early warnings are issued based on the judgment results. The entire process forms a closed-loop, adaptive monitoring and early warning system, significantly improving the timeliness and accuracy of lead screw guideway fault early warning.

[0030] Compared with existing technologies, the core innovation of this application lies in its adaptive capability to changes in operating conditions and the comprehensive utilization of multi-dimensional features. Traditional methods often use fixed sampling frequencies and judgment thresholds. When the operating conditions of the lead screw guide change, such as its running speed, acceleration, and deceleration, the original monitoring system may not be able to effectively capture high-frequency vibration signals, leading to the omission or misjudgment of early damage information. For example, under high-speed operation, the high-frequency impact signals generated by early damage may be undersampled due to insufficient sampling frequency, making it impossible for the analysis system to identify them. This application dynamically adjusts the judgment rules for early damage status, and can optimize the judgment logic and thresholds in real time according to the current operating conditions, thereby ensuring accurate identification of early damage under different operating conditions. In addition, this application not only focuses on the overall characteristics of vibration signals, but also extracts transient high-frequency impact features from the time-frequency distribution. These features have higher sensitivity for identifying micro-damage such as micro-cracks on the ball surface and pitting on the thread surface. This method, which combines adaptive adjustment judgment rules based on operating conditions with multi-dimensional transient high-frequency impact feature extraction, makes the present application significantly superior to existing technologies in terms of early damage identification accuracy and timely warning, effectively avoiding unplanned downtime and production losses caused by misjudgment or omission.

[0031] As one embodiment of the present invention, the step of dynamically adjusting the judgment rule for early damage state according to the operating conditions of the lead screw guide includes: acquiring the operating parameter set of the lead screw guide; it should be noted that the above step refers to acquiring key parameters reflecting the current working state of the lead screw guide in real time through sensors or control system interfaces. These parameters may include, but are not limited to, operating speed, acceleration / deceleration, load size, temperature, and lubrication status. The purpose is to comprehensively characterize the current operating environment of the lead screw guide.

[0032] Based on the operating parameter set, obtain multiple predefined operating state categories. It should be noted that this step can be understood as classifying the various operating conditions that the lead screw guide may experience. For example, operating states can be divided into different categories such as "high speed light load," "medium speed medium load," and "low speed heavy load" based on speed range, load range, etc. These categories are usually predefined during the system design or debugging phase based on experience, experimental data, or simulation models.

[0033] For each operating condition category, a high-frequency vibration characteristic baseline is established under normal operating conditions. It should be noted that this step refers to collecting vibration signals and extracting high-frequency vibration characteristics when the lead screw guide is in a healthy state and operating under a specific operating condition category, forming a "normal" characteristic template for that state. This baseline may include statistical characteristics such as the mean, variance, kurtosis, and energy envelope of the high-frequency vibration signal, used to characterize the inherent vibration modes of the healthy lead screw guide under that operating condition. Its purpose is to provide a reliable reference standard for subsequent damage assessment.

[0034] The system continuously matches the currently acquired operating parameter set with the operating status category to identify the current operating status category of the lead screw guide. It's important to note that this step involves the system continuously monitoring the lead screw guide's operating parameters and comparing them with predefined operating status categories. For example, methods such as fuzzy logic, machine learning classifiers, or simple threshold judgments can be used to determine which predefined operating status category the current parameter set best matches. The purpose is to accurately identify the specific working environment of the lead screw guide.

[0035] Based on the identified current operating state category and the corresponding high-frequency vibration characteristic baseline, the judgment threshold for early damage state is dynamically adjusted to adjust the judgment rules for early damage state.

[0036] It should be noted that the above step refers to the system calling upon the pre-established high-frequency vibration characteristic baseline under that category once the current operating state category is determined. Based on this baseline, an early damage judgment threshold applicable to the current operating condition can be dynamically calculated or found. For example, if the normal vibration level of the current operating condition is high, the judgment threshold will be adjusted accordingly to avoid false alarms; conversely, if the normal vibration level is low, the threshold will be adjusted to improve the sensitivity to early damage. This dynamic adjustment ensures that the judgment rules can adapt to the normal fluctuations of the lead screw guide under different operating conditions, thereby improving the accuracy and robustness of the judgment.

[0037] This application's solution addresses the issue of potentially imprecise judgment rule adjustments in basic solutions by introducing the concepts of operating parameter groups, operating state categories, and high-frequency vibration characteristic baselines. Specifically, firstly, by acquiring the operating parameter groups of the lead screw guide, the system can comprehensively perceive its current working environment. Secondly, matching these operating parameters with predefined operating state categories enables the system to accurately identify the specific operating condition of the lead screw guide. Because a high-frequency vibration characteristic baseline under normal operating conditions is established for each operating state category, the system can obtain an objective and representative "healthy" reference standard for the current operating condition when judging early damage. Finally, based on the identified current operating state category and its corresponding high-frequency vibration characteristic baseline, the judgment threshold for early damage is dynamically adjusted, allowing the judgment rules to adaptively adapt to the normal vibration characteristics of the lead screw guide under different operating conditions, avoiding misjudgments or omissions caused by changes in operating conditions.

[0038] In some preferred embodiments, a specific example is given below. Assume a lead screw guide has three main operating states during actual operation: "low speed and light load," "medium speed and medium load," and "high speed and heavy load." Before the system is put into use, the vibration signals of the lead screw guide are collected under each of these three operating states, assuming the guide is in a healthy state. High-frequency vibration characteristics, such as instantaneous energy and kurtosis factor, are extracted to establish baselines for their respective normal high-frequency vibration characteristics. For example, the instantaneous energy baseline in the "low speed and light load" state might be X, and the kurtosis factor baseline might be Y; while the instantaneous energy baseline in the "high speed and heavy load" state might be X', and the kurtosis factor baseline might be Y', where X' is typically greater than X, and Y' is typically less than Y.

[0039] When the lead screw guide is running in actual production, the system acquires its operating parameter set in real time, such as the current operating speed V1 and load L1. The system matches V1 and L1 with predefined operating state categories to identify that the lead screw guide is currently in the "medium speed and medium load" state category. Subsequently, the system calls the pre-established high-frequency vibration characteristic baseline under the "medium speed and medium load" state category. Based on this baseline, the system dynamically adjusts the judgment threshold for early damage. For example, if the currently acquired instantaneous energy and kurtosis factor characteristics deviate significantly from the baseline of the "medium speed and medium load" state and exceed the dynamically adjusted judgment threshold, the system will judge that early damage exists. This dynamic adjustment mechanism ensures that even under the "high speed and heavy load" operating condition where the lead screw guide is running normally with a high vibration level, false alarms caused by the high vibration signal itself can be avoided; at the same time, under the "low speed and light load" operating condition where the vibration signal is relatively stable, it can maintain sensitivity to weak damage signals, thereby achieving more accurate early damage judgment.

[0040] As one embodiment of the present invention, the operating parameter group includes operating speed, acceleration / deceleration and load.

[0041] Among these parameters, operating speed refers to the distance the lead screw guide travels within a specific time period, reflecting its working rhythm and energy input level. Acceleration / deceleration refers to the rate of speed change of the lead screw guide during startup, stopping, or direction change, directly affecting the inertial load and impact borne by the system. Load refers to the external forces or resistances borne by the lead screw guide during operation, such as workpiece weight and cutting force, which determines the actual working load of the lead screw guide. These parameters together constitute the key indicators describing the operating conditions of the lead screw guide, comprehensively reflecting its stress state and motion characteristics under different working conditions.

[0042] This application's solution incorporates operating speed, acceleration / deceleration, and load as key components of the operating parameter set, enabling more precise quantification and description of the lead screw guide's operating conditions. Once these parameters are acquired, the current operating state category of the lead screw guide can be identified, and a corresponding high-frequency vibration characteristic baseline can be established or selected. For example, under high-speed, high-load, or frequent acceleration / deceleration conditions, the normal vibration level of the lead screw guide will be significantly higher than under low-speed, light-load, or constant-speed operating conditions. By considering these specific operating parameters, the system can more accurately determine whether the current vibration signal deviates from the normal baseline under that specific operating condition, thereby avoiding misjudgment or omission.

[0043] As one embodiment of the present invention, the transient high-frequency impact characteristics include instantaneous energy and kurtosis factor characteristics, energy envelope characteristics, and frequency variation characteristics.

[0044] The instantaneous energy and kurtosis factor characteristics refer to the analysis of identified transient high-frequency impact events within the time-frequency distribution of vibration signals, extracting their instantaneous energy and kurtosis factor values. Instantaneous energy is primarily used to quantify the intensity of the impact event, i.e., the amount of energy released during the impact. Kurtosis factor, on the other hand, is a statistical measure used to assess the "sharpness" or "tailing" of the signal distribution. It is highly sensitive to impact signals and can effectively reflect the presence of abnormal impact components in the signal. By combining instantaneous energy and kurtosis factor, the intensity and impact characteristics of impact events can be more comprehensively characterized.

[0045] Energy envelope characteristics refer to the analysis of the energy envelope of transient high-frequency impact events in the time-frequency domain, extracting its decay rate and duration. Impact events typically exhibit a rapid energy rise followed by a decay process, and the shape of the energy envelope reflects the characteristics of the impact source and its propagation path. The decay rate describes how quickly the impact energy dissipates, while the duration represents the length of time from the occurrence of the impact event to its near-complete end. These parameters help distinguish between different types of impacts, such as those caused by wear, cracks, or spalling, whose energy decay characteristics may differ.

[0046] Frequency variation characteristics refer to the analysis of frequency changes in the time-frequency domain of transient high-frequency impact events, extracting their offset and bandwidth. Early damage may cause slight changes in the natural frequency of the lead screw guide or introduce new frequency components into the vibration signal. The frequency offset quantifies the degree of deviation of the dominant frequency from the normal state during an impact event, while the bandwidth describes the frequency range encompassed by the impact event. These frequency domain characteristics are of great significance for identifying resonance or frequency modulation phenomena caused by damage.

[0047] This application's solution refines transient high-frequency impact characteristics into instantaneous energy and kurtosis factor characteristics, energy envelope characteristics, and frequency variation characteristics, enabling a more comprehensive and refined characterization of early damage to lead screw guides from multiple dimensions. Instantaneous energy and kurtosis factor characteristics directly reflect the intensity and impact of the impact event, while the energy envelope characteristics further reveal the duration and attenuation characteristics of the impact event. The frequency variation characteristics capture subtle changes in the frequency spectrum caused by damage. This combination of multi-dimensional features allows the system to more accurately identify weak signals related to early damage when faced with complex vibration signals, thereby improving the sensitivity and accuracy of damage assessment.

[0048] As one embodiment of the present invention, the step of extracting transient high-frequency impact features from a time-frequency distribution includes: identifying transient high-frequency impact events in the time-frequency distribution; it should be noted that, in the time-frequency distribution, transient high-frequency impact events typically manifest as local regions where energy is highly concentrated in time and frequency. Identifying these events can be done by setting an energy threshold, applying image processing techniques (e.g., connected component analysis), or using machine learning-based methods. For example, wavelet transform or short-time Fourier transform can be used to generate a time-frequency distribution map, and potential impact events can be located by analyzing its local peaks and energy density.

[0049] For each transient high-frequency impact event, its instantaneous energy and kurtosis factor are extracted to obtain instantaneous energy and kurtosis factor features. It should be noted that for each identified transient high-frequency impact event, its instantaneous energy can be calculated as the sum or average of the energies of all points in the time-frequency distribution of the event, to quantify the intensity of the impact. The kurtosis factor is used to measure the sharpness or impactness of the impact signal; a high peak value typically indicates the presence of an impact-related fault. These instantaneous energy and kurtosis factors are extracted and combined to form instantaneous energy and kurtosis factor features, thereby providing quantitative information about the amplitude and characteristics of the impact event.

[0050] For each transient high-frequency impact event, its energy envelope in the time-frequency domain is analyzed, and its decay rate and duration are extracted to obtain energy envelope characteristics. It should be noted that the energy envelope in the time-frequency domain is analyzed for each transient high-frequency impact event. The decay rate of the energy envelope reflects the rate of impact energy dissipation, while the duration represents the length of time from the start to the end of the impact event. These parameters are important for distinguishing different types of damage (e.g., crack propagation versus surface spalling) because the shock wave decay characteristics and durations generated by different damage mechanisms can vary significantly. Energy envelope characteristics can be obtained by extracting the decay rate and duration of the energy envelope.

[0051] For each transient high-frequency impact event, its frequency variation in the time-frequency domain is analyzed, and the offset and bandwidth of the frequency variation are extracted to obtain frequency variation characteristics. It should be noted that the offset of the frequency variation indicates the drift of the center frequency of the impact event relative to the normal state, while the bandwidth reflects the distribution range of the frequency components of the impact signal. These frequency characteristics are crucial for identifying the type and severity of damage, as damage may cause changes in the system's inherent frequencies or excite new resonant frequencies. The frequency variation characteristics can be obtained by extracting the offset and bandwidth of the frequency variation.

[0052] By combining instantaneous energy and kurtosis factor features, energy envelope features, and frequency variation features, transient high-frequency impact characteristics are formed. This combination can be achieved through feature vector concatenation, weighted averaging, or other multi-feature fusion techniques, aiming to provide a comprehensive description with higher sensitivity and robustness to the early damage state of lead screw guides.

[0053] This application's solution employs refined analysis of the time-frequency distribution of the ball screw guide vibration signal and extracts transient high-frequency impact features from multiple dimensions, thereby enabling a more comprehensive and accurate capture of weak signals from early damage. Traditional feature extraction methods may focus on only a single indicator, are easily affected by noise interference, or fail to distinguish between different types of damage. By identifying transient high-frequency impact events and further extracting their instantaneous energy, kurtosis factor, energy envelope characteristics, and frequency variation characteristics, damage can be characterized from multiple perspectives, including impact intensity, impact sharpness, energy dissipation characteristics, and frequency response changes. This multi-dimensional feature extraction method effectively distinguishes between the actual impact signal caused by early damage and background noise or vibrations generated during normal operation, even under complex operating conditions and noise environments.

[0054] The above technical solutions significantly improve the accuracy and reliability of early damage detection in lead screw guides. Multi-dimensional extraction of transient high-frequency impact features enables the system to more sensitively capture weak, transient impact signals generated in the early stages of damage, avoiding false alarms or missed alarms that might occur with a single feature. Specifically, instantaneous energy and kurtosis factor features provide direct quantification of impact intensity, energy envelope features reveal the dynamic process of impact attenuation, and frequency variation features reflect the impact of damage on the system's dynamic characteristics. The comprehensive application of these features makes the identification of early damage more comprehensive and in-depth, thus providing a more solid data foundation for subsequent damage status assessment and graded early warning, contributing to earlier fault warnings and more accurate maintenance decisions.

[0055] As one embodiment of the present invention, the step of determining the early damage state of the lead screw guide rail based on transient high-frequency impact characteristics and judgment rules includes: preliminarily determining whether the lead screw guide rail exhibits early damage based on the transient high-frequency impact characteristics and judgment rules; it should be noted that "preliminarily determining whether the lead screw guide rail exhibits early damage" refers to conducting an initial assessment of the current health status of the lead screw guide rail based on transient high-frequency impact characteristics extracted from the time-frequency distribution and combined with judgment rules dynamically adjusted according to operating conditions, in order to determine whether there are potential signs of early damage. This preliminary judgment can be achieved using methods such as threshold comparison, pattern recognition, or machine learning, with the aim of quickly screening out potentially problematic equipment.

[0056] When the initial assessment indicates early damage, the system obtains the criticality level of the current machining task and the cumulative running time of the lead screw guide. It's important to note that "obtaining the criticality level of the current machining task" refers to the importance of the currently performed machining task within the overall production process, or its impact on product quality and production efficiency. For example, criticality levels can be categorized as high, medium, and low. High-criticality tasks may involve precision machining, long-term operation, or the production of high-value products; equipment malfunctions could lead to serious consequences. Low-criticality tasks may have a smaller impact on production. This criticality level can be pre-configured in the production management system and automatically read by the system when the task starts.

[0057] "Accumulated operating time of the lead screw guide" refers to the total operating time of the lead screw guide since it was put into use or since the last major overhaul. Accumulated operating time is an important indicator for measuring equipment wear and fatigue accumulation, and it is usually positively correlated with the probability of equipment failure. This data can be recorded and maintained by the equipment's own controller or production management system.

[0058] By combining the preliminary assessment results, the criticality level, and the cumulative operating time, the risk of early damage is evaluated. It should be noted that this step involves comprehensively considering the initially assessed damage state, the criticality level of the current task, and the cumulative operating time of the lead screw guide rail to quantify the potential risk of early damage. For example, even if the initial assessment indicates minor damage, if it occurs in a high-criticality task and the lead screw guide rail has already accumulated a long operating time, the risk assessment result will be significantly higher. Risk assessment can be conducted using methods such as weighted summation, fuzzy logic reasoning, or risk matrices, with the aim of providing a more comprehensive and practically accurate quantitative risk indicator.

[0059] Based on the assessment results of early damage risk, the early warning priority is adjusted to form the early damage status of the lead screw guide.

[0060] It should be noted that this step refers to dynamically adjusting the urgency and priority of the early warning information based on the assessed early damage risk. For example, high-risk damage may trigger an immediate shutdown or emergency maintenance warning, while low-risk damage may only trigger a routine inspection or planned maintenance recommendation. In this way, a single "damage exists" state can be refined into "early damage states" with different risk levels and priority levels, thus providing a more accurate basis for subsequent graded early warnings.

[0061] This application's solution, based on a preliminary assessment of early damage to the lead screw guide, further incorporates the criticality level of the current processing task and the cumulative operating time of the lead screw guide as evaluation factors, thereby transforming a single damage assessment into a multi-dimensional, contextualized early damage risk assessment. Specifically, when initial signs of damage are detected, the system does not immediately issue a uniform warning. Instead, it first acquires key information related to the current production environment and equipment history. The criticality level of the processing task allows the system to identify which damages have the greatest impact on production, thus prioritizing equipment issues related to high-risk tasks when resources are limited. The cumulative operating time of the lead screw guide provides background information on equipment aging and fatigue, allowing even minor damage to be assigned a higher risk weight as the equipment approaches the end of its service life. Through this comprehensive consideration, the system can more accurately assess the potential impact and urgency of early damage, avoiding the problems of over-warning or under-warning that may occur in traditional methods. Therefore, the warning priority can be dynamically adjusted, ensuring that the warning information matches the actual risk, thus forming a more instructive assessment of the early damage status of the lead screw guide.

[0062] As one embodiment of the present invention, the step of issuing a graded early warning based on the judgment result of the early damage state includes: identifying the role type of the person receiving the early warning information, including operators, maintenance engineers, and production managers; it should be noted that identifying the role type of the person receiving the early warning information means that the system distinguishes the receivers with different responsibilities according to preset user permissions or configurations. For example, operators usually focus on immediate operation instructions, maintenance engineers need detailed diagnostic data for troubleshooting, while production managers are more concerned with production plans and cost impacts.

[0063] Based on the assessment of early damage status and the recipient's role, the content of the early warning information is determined. This information includes concise instructions for operators, diagnostic data for maintenance engineers, and production impact assessments for production managers. It's important to note that the content of the early warning information is customized based on the severity of the assessed early damage and the recipient's role. For example, for operators, the warning might only contain concise instructions such as "Please stop the machine immediately for inspection" or "Please reduce the operating speed," avoiding unnecessary technical details. For maintenance engineers, detailed vibration signal analysis data, damage location inferences, and historical operating trends are provided for in-depth analysis. For production managers, the warning focuses on assessing potential downtime, production losses, spare parts requirements, and other production impacts.

[0064] Based on role type, determine the channels for sending early warning information. These channels include machine tool control panels, mobile applications, and enterprise messaging systems. It's important to note that determining the sending channel means selecting the most appropriate notification method based on the recipient's daily work habits and the level of urgency. For example, operators might receive immediate instructions via warning lights or pop-ups on the machine tool control panel; maintenance engineers might receive detailed diagnostic reports and maintenance tasks via mobile applications; and production managers might receive production impact assessments and scheduling recommendations via enterprise messaging systems (such as email or OA system notifications).

[0065] Based on role type, determine the action guidelines for early warning information. These guidelines include shutdown or speed reduction instructions for operators, inspection or maintenance procedures for maintenance engineers, and production scheduling suggestions for production managers. It's important to note that determining action guidelines for early warning information means providing specific and actionable follow-up operational suggestions for different roles. For example, for operators, the action guidelines might be explicit shutdown or speed reduction instructions to prevent further damage. For maintenance engineers, the action guidelines might include detailed inspection or maintenance procedures, guiding them on how to locate and repair faults. For production managers, the action guidelines might provide production scheduling suggestions, such as adjusting production plans and arranging backup equipment, to minimize the impact of production interruptions.

[0066] By sending warning information and action guidelines to different role types through the warning information sending channel, a tiered warning system can be established.

[0067] This application's solution effectively addresses the problems of inaccurate information delivery and unclear action guidance in traditional early warning methods by identifying the role types of those receiving early warning information and dynamically determining the content, transmission channel, and action guidance accordingly. Specifically, by differentiating between roles such as operators, maintenance engineers, and production managers, the system can provide them with the information they need, avoiding information overload or insufficiency. For example, operators can respond quickly after receiving concise instructions, maintenance engineers can efficiently troubleshoot faults after obtaining diagnostic data, and production managers can adjust production strategies in a timely manner based on production impact assessments. This customized information distribution mechanism ensures the effective reach and efficient utilization of early warning information, thereby improving the overall response efficiency and decision-making quality of early damage warnings.

[0068] As one embodiment of the present invention, the step of adaptive noise suppression of the preprocessed vibration signal includes: acquiring the current operating condition parameters of the lead screw guide and the current operating status of surrounding equipment; it should be noted that the above step refers to acquiring in real time the operating condition parameters of the lead screw guide, such as its operating speed, load, and acceleration, as well as the operating status information of surrounding equipment such as cooling pumps, other machine tools, and ventilation systems, through sensors or control system interfaces. This information is the basis for assessing the current noise environment.

[0069] Based on the current operating parameters and the current operating status of surrounding equipment, determine the characteristic category of the current environmental noise. It should be noted that this step can be understood as classifying the current noise environment. For example, when the lead screw guide is running at high speed and the surrounding cooling pump is running, it may correspond to a characteristic category of a mixture of high-frequency fluid noise and mechanical vibration noise; when the lead screw guide is running at low speed under no-load, it may be mainly affected by ambient background noise, corresponding to another characteristic category. This step aims to identify the main sources and characteristics of noise, and its purpose is to provide a targeted basis for subsequent noise suppression.

[0070] Based on the characteristic categories of environmental noise, a corresponding set of noise suppression parameters is dynamically selected. It should be noted that this step specifically refers to selecting the most suitable noise suppression algorithm and its specific parameter configuration from a pre-established parameter library based on the identified noise characteristic categories. For example, for broadband random noise, wavelet thresholding or Wiener filtering can be selected; for periodic noise, notch filtering or adaptive LMS algorithm can be selected. The parameter set can include filter type, cutoff frequency, order, threshold size, etc., with the aim of ensuring the effectiveness of noise suppression and signal fidelity.

[0071] Adaptive noise suppression is performed on the preprocessed vibration signal using a noise suppression parameter set. It should be noted that this step refers to applying the selected noise suppression algorithm and its parameters to the vibration signal. This process is dynamic; the parameter set is not fixed but adjusted according to changes in environmental noise.

[0072] During adaptive noise suppression, the spectral characteristics of residual noise are monitored in real time, and the noise suppression parameter set is quickly adjusted according to the spectral characteristics.

[0073] It should be noted that the above step can be understood as a feedback control mechanism. By performing spectral analysis on the signal after initial suppression, the level and characteristics of residual noise are evaluated. If the residual noise is still significant or has new characteristics, the system will immediately fine-tune the current noise suppression parameter set, such as changing the filter cutoff frequency or threshold, to further optimize the noise suppression effect. The goal is to achieve more accurate and thorough noise removal while preserving useful signal components to the maximum extent.

[0074] The proposed solution acquires real-time operating parameters of the lead screw guide and the operating status of surrounding equipment, enabling accurate perception of the current working environment. Based on this environmental information, the system can intelligently identify the characteristic categories of current environmental noise, thus avoiding the limitations of traditional fixed-parameter noise suppression methods under complex and variable working conditions. The ability to dynamically select the noise suppression parameter set that best matches the current noise characteristics makes the noise suppression process highly adaptable. Furthermore, by monitoring residual noise in real-time and making rapid adjustments during the suppression process, this solution forms a closed-loop adaptive control mechanism, ensuring continuous optimization of noise suppression. This effectively separates the target vibration signal from the complex noise background, providing high-quality input for subsequent transient high-frequency impact feature extraction.

[0075] As one embodiment of the present invention, the step of performing time-frequency domain transformation on the noise-suppressed vibration signal to obtain the time-frequency distribution of the vibration signal includes: selecting basis functions with different time-frequency localization characteristics. It should be noted that this step refers to selecting one or more sets of mathematical functions with different resolutions in time and frequency, based on the characteristics of the vibration signal to be analyzed and the analysis requirements. For example, for analyses that require simultaneous attention to the transient characteristics and frequency components of the signal, wavelet basis functions can be selected, which have good frequency resolution and poor time resolution in the low-frequency part, and good time resolution and poor frequency resolution in the high-frequency part. Alternatively, window functions in the Short-Time Fourier Transform (STFT) can be selected, and their time-frequency localization characteristics can be changed by adjusting the length and shape of the window function. The purpose is to provide suitable analysis tools for subsequent multi-scale decomposition to adapt to the complex changes of the vibration signal in different time scales and frequency ranges.

[0076] Multi-scale decomposition is performed on the noise-suppressed vibration signal using basis functions. It should be noted that this step can be understood as decomposing the noise-suppressed vibration signal into a series of components with different time scales and frequency ranges under the action of selected basis functions. For example, when using wavelet transform, by performing continuous or discrete wavelet transforms on the signal, wavelet coefficients at different scales can be obtained. These coefficients reflect the energy distribution of the signal at different frequency bands and time locations. The purpose of multi-scale decomposition is to examine the characteristics of the vibration signal from both macroscopic and microscopic levels, thereby more comprehensively capturing weak signals that may indicate early damage.

[0077] Based on the multi-scale decomposition results, the time-frequency distribution of the vibration signal at different scales is obtained.

[0078] It should be noted that this step specifically involves mapping the components or coefficients obtained from multi-scale decomposition onto the time-frequency plane using appropriate reconstruction or visualization methods, forming a time-frequency distribution map of the vibration signal at different scales. For example, for wavelet decomposition results, the squared modulus values ​​of the wavelet coefficients at each scale can be calculated and arranged on the time and frequency axes to obtain a wavelet time-frequency map. This time-frequency distribution can intuitively show how the energy of the vibration signal changes with time and frequency, providing a data foundation for subsequent feature extraction.

[0079] This application's solution overcomes the limitations of single-resolution analysis methods by selecting basis functions with different time-frequency localization characteristics and using these basis functions to perform multi-scale decomposition on noise-suppressed vibration signals. Vibration signals, especially impact signals caused by early damage, often exhibit transient and non-stationary characteristics; their energy may be concentrated at a specific time point and over a wide frequency range, or exhibit low-frequency variations over a longer period. Multi-scale decomposition simultaneously considers the characteristics of the signal at different time scales and frequency ranges, allowing weak early damage signals to be clearly displayed in the high-frequency range, while low-frequency components caused by background noise and changes in operating conditions are effectively separated in the low-frequency range. This layered, multi-angle analysis method ensures that the time-frequency distribution can more comprehensively and accurately reflect the true vibration state of the lead screw guide.

[0080] Figure 2 shows a data acquisition system for a lead screw guide rail. The system includes: a vibration signal acquisition module 201 for acquiring vibration signals from the lead screw guide rail; a preprocessing module 202 for preprocessing the vibration signals to obtain preprocessed vibration signals; an adaptive noise suppression module 203 for adaptively suppressing noise in the preprocessed vibration signals to obtain noise-suppressed vibration signals; a time-frequency domain conversion module 204 for performing time-frequency domain conversion on the noise-suppressed vibration signals to obtain the time-frequency distribution of the vibration signals; a feature extraction module 205 for extracting transient high-frequency impact features from the time-frequency distribution; a judgment rule adjustment module 206 for dynamically adjusting the judgment rules for early damage states based on the operating conditions of the lead screw guide rail; a damage state judgment module 207 for judging the early damage state of the lead screw guide rail based on the transient high-frequency impact features and the judgment rules; and an early warning module 208 for issuing graded early warnings based on the judgment results of the early damage state.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for acquiring data from a lead screw guide rail, characterized in that, The method includes the following steps: acquiring the vibration signal of the lead screw guide; preprocessing the vibration signal to obtain a preprocessed vibration signal; performing adaptive noise suppression on the preprocessed vibration signal to obtain a noise-suppressed vibration signal; performing time-frequency domain transformation on the noise-suppressed vibration signal to obtain the time-frequency distribution of the vibration signal; extracting transient high-frequency impact features from the time-frequency distribution; dynamically adjusting the judgment rules for early damage state according to the operating conditions of the lead screw guide; judging the early damage state of the lead screw guide according to the transient high-frequency impact features and the judgment rules; and issuing a graded early warning based on the judgment result of the early damage state.

2. The data acquisition method for a lead screw guide rail according to claim 1, characterized in that, The step of dynamically adjusting the judgment rules for early damage state based on the operating conditions of the lead screw guide includes: acquiring a set of operating parameters for the lead screw guide; acquiring multiple predefined operating state categories based on the set of operating parameters; establishing a high-frequency vibration characteristic baseline for each operating state category under normal operating conditions; matching the operating parameters of the currently acquired set of operating parameters with the operating state categories in real time to identify the current operating state category to which the lead screw guide belongs; and dynamically adjusting the judgment threshold for early damage state based on the identified current operating state category and the corresponding high-frequency vibration characteristic baseline, so as to adjust the judgment rules for early damage state.

3. The data acquisition method for a lead screw guide rail according to claim 2, characterized in that, The operating parameter group includes operating speed, acceleration / deceleration, and load.

4. The data acquisition method for a lead screw guide rail according to claim 1, characterized in that, The transient high-frequency impact characteristics include instantaneous energy and kurtosis factor characteristics, energy envelope characteristics, and frequency variation characteristics.

5. The data acquisition method for a lead screw guide rail according to claim 4, characterized in that, The steps for extracting transient high-frequency impact features from the time-frequency distribution include: identifying transient high-frequency impact events in the time-frequency distribution; for each transient high-frequency impact event, extracting its instantaneous energy and kurtosis factor to obtain instantaneous energy and kurtosis factor features; for each transient high-frequency impact event, analyzing its energy envelope in the time-frequency domain, extracting the decay rate and duration of the energy envelope to obtain energy envelope features; for each transient high-frequency impact event, analyzing its frequency change in the time-frequency domain, extracting the offset and bandwidth of the frequency change to obtain frequency change features; and combining the instantaneous energy and kurtosis factor features, the energy envelope features, and the frequency change features to form the transient high-frequency impact features.

6. The data acquisition method for a lead screw guide rail according to claim 1, characterized in that, The step of determining the early damage state of the lead screw guide rail based on the transient high-frequency impact characteristics and the judgment rules includes: making a preliminary judgment on whether the lead screw guide rail has an early damage state based on the transient high-frequency impact characteristics and the judgment rules; when the preliminary judgment result indicates that an early damage state exists, obtaining the criticality level of the current processing task and the cumulative running time of the lead screw guide rail; assessing the early damage risk by combining the preliminary judgment result, the criticality level, and the cumulative running time; and adjusting the warning priority based on the assessment result of the early damage risk to form the early damage state of the lead screw guide rail.

7. The data acquisition method for a lead screw guide rail according to claim 1, characterized in that, The step of issuing a tiered early warning based on the assessment results of the early damage status includes: identifying the role types of the personnel receiving the early warning information, including operators, maintenance engineers, and production managers; determining the content of the early warning information based on the assessment results of the early damage status and the role types, including concise instructions for operators, diagnostic data for maintenance engineers, and production impact assessments for production managers; determining the early warning information transmission channel based on the role types, including machine tool operation panels, mobile terminal applications, and enterprise messaging systems; determining action guidelines for the early warning information based on the role types, including stop or speed reduction instructions for operators, inspection or maintenance procedures for maintenance engineers, and production scheduling suggestions for production managers; and sending the early warning information content and action guidelines to the respective role types through the early warning information transmission channels to issue a tiered early warning.

8. The data acquisition method for a lead screw guide rail according to claim 1, characterized in that, The step of adaptively suppressing noise in the preprocessed vibration signal includes: acquiring the current operating condition parameters of the lead screw guide and the current operating status of the surrounding equipment; determining the characteristic category of the current environmental noise based on the current operating condition parameters and the current operating status of the surrounding equipment; dynamically selecting a corresponding noise suppression parameter set based on the characteristic category of the environmental noise; using the noise suppression parameter set to adaptively suppress noise in the preprocessed vibration signal; and during the adaptive noise suppression process, monitoring the spectral characteristics of residual noise in real time and rapidly adjusting the noise suppression parameter set according to the spectral characteristics.

9. The data acquisition method for a lead screw guide rail according to claim 1, characterized in that, The step of performing time-frequency domain transformation on the noise-suppressed vibration signal to obtain the time-frequency distribution of the vibration signal includes: selecting basis functions with different time-frequency localization characteristics; using the basis functions to perform multi-scale decomposition on the noise-suppressed vibration signal; and obtaining the time-frequency distribution of the vibration signal at different scales based on the multi-scale decomposition results.

10. A lead screw guide rail data acquisition system, used to execute the lead screw guide rail data acquisition method as described in any one of claims 1-9, characterized in that, The system includes: a vibration signal acquisition module for acquiring vibration signals of the lead screw guide; a preprocessing module for preprocessing the vibration signals to obtain preprocessed vibration signals; an adaptive noise suppression module for adaptively suppressing the preprocessed vibration signals to obtain noise-suppressed vibration signals; a time-frequency domain conversion module for performing time-frequency domain conversion on the noise-suppressed vibration signals to obtain the time-frequency distribution of the vibration signals; a feature extraction module for extracting transient high-frequency impact features from the time-frequency distribution; a judgment rule adjustment module for dynamically adjusting the judgment rules for early damage states according to the operating conditions of the lead screw guide; a damage state judgment module for judging the early damage state of the lead screw guide based on the transient high-frequency impact features and the judgment rules; and an early warning module for issuing graded early warnings based on the judgment results of the early damage states.

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