A method and system for intelligent online control of a high-speed frame stranding machine

Abstract

This invention relates to the field of general control and regulation technology, specifically to an intelligent online control method and system for a high-speed frame winch. The method includes: acquiring speed and vibration signals; decomposing the vibration signal into high-frequency and low-frequency sub-signals; determining the trend consistency between the low-frequency sub-signal and the speed signal; performing time-frequency analysis on the high-frequency sub-signal; in the time domain, determining the signal impact intensity from peak value and kurtosis, and calculating the coefficient of variation of the amplitude; in the frequency domain, assessing the energy concentration based on the entropy value of energy, calculating the coefficient of variation of the frequency, and determining the time-frequency consistency based on the difference between the two coefficients of variation; combining the signal impact intensity and the energy concentration, quantifying the anomaly degree of the high-frequency sub-signal; comprehensively determining the operating condition anomaly index by combining the trend consistency between the low-frequency sub-signal and the speed signal and the anomaly index of the high-frequency sub-signal, and controlling the high-speed frame winch. This method improves the operational stability of the high-speed frame winch.

Description

Technical Field

[0001] This invention relates to the field of general control and regulation technology. Specifically, it relates to an intelligent online control method and system for a high-speed frame winch. Background Technology

[0002] High-speed wire stranding machines are used in the production of high-end cables such as communication cables and high-voltage transmission lines. Their main function is to achieve precise stranding of conductors through the synchronous rotation of multiple spools, thereby improving production efficiency through high speed. During high-speed operation, high-speed wire stranding machines have extremely high requirements for precision and stability to ensure uniform strand pitch and reduce the breakage rate.

[0003] To achieve efficient and stable operation of high-speed frame stranding machines, modern intelligent control systems have been widely used. These control systems integrate microprocessors, multi-source sensor networks, and real-time control algorithms. By monitoring and analyzing operating parameters in real time, they can precisely control the high-speed frame stranding machine based on the analysis results, thereby ensuring the stable operation of the equipment.

[0004] Currently, a Chinese patent document with publication number CN118151591B discloses an intelligent control method for a high-speed frame winch based on speed sensors. This technical solution involves installing speed sensors on each spool to monitor the spool rotation speed in real time and transmitting the data to a central microprocessor. The microprocessor performs anomaly analysis on the received rotation speed data and then assesses the severity of any anomalies in real time, thereby enabling control.

[0005] However, the aforementioned technical solutions primarily rely on speed information for analysis, and this single-dimensional control strategy based on speed analysis has certain limitations. Specifically, due to the complex and variable nature of the spool's operation, speed information is low-frequency information. This means that when speed anomalies are detected, the fault has usually already progressed to a more serious state, often manifesting as large fluctuations or significant jumps. This makes it impossible to detect early potential faults and provide timely warnings, thereby affecting the system's ability to respond to potential risks.

[0006] Therefore, there is an urgent need to develop a more precise and sensitive intelligent online control method to identify potential faults and anomalies in advance, optimize control strategies, and ensure the stability and efficiency of equipment during the production process. Summary of the Invention

[0007] To address the problem that existing intelligent control methods for high-speed frame winches based on speed sensors rely on low-frequency speed information, making it difficult to detect early potential faults and provide early warnings, thus resulting in insufficient system response to potential risks, this invention proposes an intelligent online control method and system for high-speed frame winches.

[0008] In a first aspect, the present invention provides an intelligent online control method for a high-speed frame winch, comprising:

[0009] The speed and vibration signals of the high-speed frame winch are collected during operation, and the high-frequency and low-frequency sub-signals of the vibration signal are obtained through signal decomposition technology.

[0010] For low-frequency sub-signals, trend analysis is used to determine the consistency of trends between the low-frequency sub-signals and the velocity signal; for high-frequency sub-signals, time-frequency analysis is performed, including:

[0011] In the time domain, the signal impulse intensity of the high-frequency sub-signal is determined by the peak value and kurtosis of the high-frequency sub-signal, and a first coefficient of variation is calculated based on the amplitude of the high-frequency sub-signal at all sampling points; in the frequency domain, the energy concentration of the high-frequency sub-signal is evaluated based on the entropy value of the energy of the high-frequency sub-signal at all frequencies, and a second coefficient of variation is calculated based on all frequencies of the high-frequency sub-signal; the time-frequency consistency of the high-frequency sub-signal is determined based on the difference between the first coefficient of variation and the second coefficient of variation.

[0012] The degree of anomaly in the high-frequency sub-signal is determined by fusing the signal impulse intensity, energy concentration, and time-frequency consistency.

[0013] Based on the consistency of trends between low-frequency sub-signals and speed signals, as well as the degree of anomaly in high-frequency sub-signals, the operating condition anomaly index of the high-speed frame winch is comprehensively determined, and the high-speed frame winch is controlled according to the operating condition anomaly index.

[0014] This technical solution utilizes wavelet decomposition to break down vibration signals into high-frequency and low-frequency sub-signals, enabling multi-source information analysis of the macroscopic motion state and microscopic characteristics of the high-speed frame winch. For the low-frequency sub-signals, trend analysis is used to determine their coordinated variation with the velocity signal. Since low-frequency vibration is mainly driven by the periodic motion of rotating parts, there is a physical coupling between the low-frequency sub-signals and the velocity signal. This analysis can quickly quantify the macroscopic operational stability of the equipment and preliminarily determine whether any abnormal operating conditions have occurred. For the high-frequency sub-signals, time-frequency joint analysis is used to analyze the detailed characteristics of the high-frequency sub-signals to comprehensively and accurately quantify the degree of abnormality. Finally, by combining the trend consistency of the low-frequency and velocity sub-signals with the degree of abnormality of the high-frequency sub-signals, an abnormal operating condition index is determined. This achieves an organic combination of macroscopic stability and microscopic faults, comprehensively assessing the operating condition of the high-speed frame winch and forming a complete logical chain from early fault identification to hierarchical intelligent control. This enables timely detection of early potential faults and improves the operational stability of the high-speed frame winch.

[0015] Preferably, the trend consistency between the low-frequency sub-signal and the velocity signal is determined as follows: Based on the amplitudes of the low-frequency sub-signal and the velocity signal at all sampling points, the Pearson correlation coefficient between the low-frequency sub-signal and the velocity signal is calculated, and this Pearson correlation coefficient is used as the global trend consistency. The direction of amplitude change of the low-frequency sub-signal and the velocity signal at each sampling point is determined based on the difference between the amplitude at each sampling point and the amplitude at the previous sampling point. The local trend consistency of the amplitudes of the low-frequency sub-signal and the velocity signal is determined based on the direction of amplitude change of the low-frequency sub-signal and the velocity signal at the same sampling point. The global trend consistency and the local trend consistency indices are fused to obtain the trend consistency between the low-frequency sub-signal and the velocity signal.

[0016] This technical solution uses the Pearson correlation coefficient as a global trend consistency indicator, which reflects the linear correlation strength between low-frequency sub-signals and velocity signals in the overall distribution, demonstrating macroscopic synergy. Through local trend consistency, it can accurately capture the synchronicity of low-frequency sub-signals and velocity signals in microscopic time series, making up for the deficiency of global analysis insensitivity to local fluctuations. Finally, it integrates global and local indicators, which not only retains the grasp of the overall operating trend, but also enhances the perception of local abnormal changes.

[0017] Preferably, the signal impulse intensity of the high-frequency sub-signal is determined based on the following method:

[0018] Calculate the first deviation between the peak value of the high-frequency sub-signal and the reference peak value, and the second deviation between the kurtosis of the high-frequency sub-signal and the reference kurtosis. Use the normalized value of the product of the first deviation and the second deviation as the signal impulse intensity of the high-frequency sub-signal.

[0019] This technical solution quantifies the impact intensity of high-frequency sub-signals by multiplying peak deviation and kurtosis deviation. Its design strictly adheres to the physical nature of mechanical faults: the first deviation directly reflects the magnitude of the signal impact, and the second deviation reflects whether the signal impact is dense. Since the impact caused by mechanical faults must have both high intensity and high density, the product of the two can specifically amplify this composite anomaly, and more accurately distinguish between real fault impacts and false interference.

[0020] Preferably, the first coefficient of variation is the ratio of the standard deviation to the mean of the amplitude of the high-frequency sub-signal at all sampling points, and the second coefficient of variation is the ratio of the standard deviation to the mean of all frequencies of the high-frequency sub-signal.

[0021] Preferably, the time-frequency consistency of the high-frequency sub-signal is determined by calculating the difference between the first coefficient of variation and the second coefficient of variation, and using this difference as a negative correlation index of time-frequency consistency to quantify time-frequency consistency.

[0022] Preferably, the energy concentration of the high-frequency sub-signal is determined as follows: the entropy value of the energy of the high-frequency sub-signal at all frequencies is normalized to obtain a normalized entropy value, and the value obtained by subtracting the normalized entropy value from 1 is taken as the energy concentration of the high-frequency sub-signal.

[0023] Preferably, the degree of anomaly of the high-frequency sub-signal is determined by multiplying the signal impulse intensity, energy concentration, and time-frequency consistency as the degree of anomaly of the high-frequency sub-signal.

[0024] This technical solution constructs a three-dimensional evaluation system for the degree of anomaly of high-frequency sub-signals. The signal impact intensity reflects the morphological characteristics of the fault impact, the energy concentration quantifies the significance of the fault characteristics, and the time-frequency consistency verifies the synergy of the fault characteristics in the time-frequency domain. This improves the sensitivity to early weak faults and makes the quantitative results of the degree of anomaly more accurately reflect the true state of the equipment.

[0025] Preferably, the operating condition anomaly index of the high-speed frame winch is determined as follows: the trend consistency between the low-frequency sub-signal and the speed signal is used as the first parameter, the anomaly degree of the high-frequency sub-signal is used as the second parameter, the first parameter is converted into a negative correlation index of the operating condition anomaly index by subtracting one, and the product of the second parameter and the negative correlation index after performing an increment operation is used as the operating condition anomaly index.

[0026] This technical solution achieves coordinated quantification of macroscopic stability and microscopic anomaly degree through this negatively correlated product design. It avoids misjudging early failures from a single dimension and can amplify potential early failures. By dynamically combining local anomalies and overall stability through the product, the operating condition assessment results are more accurate and reliable.

[0027] Preferably, the method for controlling the high-speed frame winch is as follows: setting a primary threshold and a secondary threshold for the abnormal operating condition index; if the abnormal operating condition index is less than the primary threshold, it is determined that no early abnormality has occurred in the operating condition, and the high-speed frame winch continues to operate with the parameters corresponding to the current operating condition; if the abnormal operating condition index is greater than the primary threshold but not greater than the secondary threshold, it is determined that a minor early fault has occurred in the operating condition, and the parameters corresponding to the current operating condition are reduced by a preset percentage to continue operation, and the staff is prompted to pay continuous attention; if the abnormal operating condition index is greater than the secondary threshold, it is determined that a significant early fault has occurred in the operating condition, and the speed is immediately reduced to idle speed, and the staff is prompted to stop the machine for inspection.

[0028] Secondly, the present invention also provides an intelligent online control system for a high-speed frame winch, the intelligent online control system comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of any of the online control methods described above.

[0029] The present invention has the following effects:

[0030] This solution decomposes vibration signals into high-frequency and low-frequency sub-signals, enabling multi-dimensional analysis of the equipment's macroscopic state and microscopic characteristics. Trend consistency analysis of the low-frequency sub-signals can quickly capture changes in the equipment's macroscopic stability and determine whether abnormal operating conditions have occurred. Time-frequency joint analysis of the high-frequency sub-signals can accurately quantify the signal's impact intensity, energy concentration, and time-frequency consistency, detecting anomalies from multiple dimensions. This improves the early warning capability for faults and the operational stability of high-speed frame winches. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0032] Figure 2 This is a schematic diagram of the method flow for step S3 of the present invention;

[0033] Figure 3 This is a schematic diagram of the method flow for step S4 of the present invention. Detailed Implementation

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0035] This invention provides an intelligent online control method for a high-speed frame stranding machine, such as... Figure 1 As shown, it includes:

[0036] S1: Collect speed and vibration signals of the high-speed frame stranding machine during operation.

[0037] High-precision vibration and speed sensors are deployed on each spool of the high-speed frame stranding machine to collect vibration and (rotational) speed data of the spool in real time at the same high frequency (5kHz). Since each moment is a sampling point, a vibration data and a rotational speed data are generated at each sampling point.

[0038] At any given moment To obtain the current moment, 5000 sampling points generated within 1 second prior to the current moment (based on experience) are acquired. These are then used as the sampling points for the current moment. Based on the sequence of vibration data and the sequence of velocity data from all historical sampling points, the velocity signal for the current moment is obtained. and vibration signals .

[0039] By accurately collecting the speed and vibration signals of the spool, a signal reflecting the real-time operating status of the equipment is constructed. This process ensures the continuity and synchronization of the vibration and speed signals in the time dimension, and balances real-time performance with the integrity of historical characteristics through a 1-second time window (5000 sampling points), providing a reliable data foundation for subsequent analysis.

[0040] S2: Extract the high-frequency and low-frequency sub-signals of the vibration signal through signal decomposition technology.

[0041] The speed signal reflects the core dynamic characteristics of the spindle rotation in a high-speed frame stranding machine and is a key indicator for measuring the macroscopic operational stability of the equipment. It directly characterizes the instantaneous rotational speed, speed fluctuation amplitude, and long-term operating trend of the spindle. The fundamental frequency and harmonics of the speed signal are closely related to the power output of the equipment and the load state of the transmission system. Under normal operating conditions, the speed signal exhibits a stable periodic change with small and regular fluctuations. When the spindle experiences mechanical jamming, the drive motor output becomes unstable, or the transmission mechanism wears down, the speed signal will exhibit abnormal fluctuations, such as sudden increases or decreases, or increased periodic deviations.

[0042] Vibration signals reflect various physical characteristics of the spool rotation in high-speed frame winches. The low-frequency components of the vibration signals mainly reflect macroscopic motion characteristics, such as periodic fluctuations in spool rotation and vibrations caused by overall imbalance, while the high-frequency components of the vibration signals focus on microscopic fault characteristics, such as the impact between bearing rollers and tracks, and minute frictions during gear meshing.

[0043] The low-frequency components of vibration signals are primarily influenced by macroscopic structural factors such as the unbalanced forces of rotating parts and bearing concentricity deviations. Furthermore, these low-frequency components are closely related to the fundamental frequency and harmonics of the velocity signal. Under normal operating conditions, the low-frequency components (low-frequency vibration) and velocity signal maintain a stable energy transfer through the mechanical structure, exhibiting a strong correlation. However, when equipment malfunctions, such as bearing wear or coupling loosening, changes in the stiffness and damping characteristics of the mechanical structure distort the energy transfer path, significantly reducing the correlation between the low-frequency components of the vibration signal and the velocity signal. Therefore, analyzing the changes in the correlation between the low-frequency components of the vibration signal and the velocity signal provides crucial information for early fault identification and effectively assists in assessing the equipment's operating status.

[0044] In signal decomposition techniques, wavelet decomposition can effectively separate different frequency components of a signal and can be adapted to the multi-frequency characteristics of vibration signals from high-speed frame winches.

[0045] Therefore, based on the above logical analysis in this step, the vibration signal... Performing a 5-level (empirical value) wavelet decomposition yields 6 wavelet coefficients, which are as follows: , , , , and And each wavelet coefficient corresponds to a sub-signal, namely 、 、 、 、 and .in, The corresponding sub-signal is the low-frequency sub-signal of the vibration signal, covering the low-frequency range of the vibration signal. to The corresponding sub-signals are all high-frequency sub-signals of the vibration signal, covering the high-frequency band of the vibration signal.

[0046] S3: Determine the trend consistency of the low-frequency sub-signals of the velocity signal and vibration signal.

[0047] As mentioned earlier, the velocity signal reflects the core dynamic characteristics of the spindle rotation. Its fundamental frequency and harmonics are closely related to the load of the transmission system. The low-frequency components of the vibration signal are not only affected by macroscopic structural factors, but also closely related to the fundamental frequency and harmonics of the velocity signal. Under normal working conditions, the two maintain stable energy transfer through the mechanical structure and show a strong correlation.

[0048] Therefore, by judging the trend consistency of the low-frequency sub-signals of the speed signal and vibration signal, the coordination of the macroscopic operating state of the equipment can be verified from the signal characteristics level. The stronger the trend consistency, the more it indicates that the dynamic characteristics of the spindle rotation and the energy transfer path of the low-frequency vibration have not undergone significant distortion, indirectly confirming that the equipment transmission system, mechanical structure and other macroscopic aspects are operating normally. If the trend consistency is weak, it suggests that there may be early faults such as mechanical jamming, increased bearing concentricity deviation or loose coupling, which leads to obstruction or distortion of energy transfer. Trend consistency provides a more direct and reliable basis for equipment condition assessment and fault early warning, and is a further deepening of the correlation analysis between the two.

[0049] Therefore, based on the above logical analysis in this step, the trend consistency of the low-frequency sub-signals of the velocity signal and vibration signal is obtained according to the following method, such as... Figure 2 As shown, it includes:

[0050] S31: Use Pearson correlation coefficient to assess global trend consistency.

[0051] The Pearson correlation coefficient essentially measures the strength of the linear correlation between two variables, with a value range of [-1, 1]. Here, the Pearson correlation analysis is performed based on the amplitude of the signal at all sampling points, which can objectively reflect the overall trend of the signal's change over the entire analysis period.

[0052] As discussed above, the low-frequency sub-signals of vibration signals and velocity signals exhibit stable energy transfer and characteristic correlations at the macroscopic operational level, and this correlation shows a significant trend consistency under normal operating conditions. Therefore, the Pearson correlation coefficient can quantify this trend consistency, avoiding subjective judgment bias and making the correlation analysis at the global level more objective and operable.

[0053] Specifically, based on the amplitudes of the low-frequency sub-signal and the velocity signal at all sampling points, the Pearson correlation coefficient between the low-frequency sub-signal and the velocity signal is calculated to serve as the global trend consistency between the low-frequency sub-signal and the velocity signal.

[0054] Among them, the low-frequency sub-signal of the vibration signal and speed signal The Pearson correlation coefficient between them satisfies the following relationship:

[0055]

[0056] In the formula, for and The Pearson correlation coefficient between them, i.e. and Consistency of global trends. The sampling point number is... The total number of sampling points is the same for both velocity and vibration signals, as the time lengths are the same. Speed ​​signal In the The amplitude at each sampling point for The mean of the amplitude at all sampling points, for In the The amplitude at each sampling point for The mean of the amplitude at all sampling points.

[0057] In the formula, The larger the value, the stronger the correlation between the low-frequency sub-signals of the velocity and vibration signals. This indicates that the low-frequency sub-signals of the speed signal and the vibration signal have a tendency to increase or decrease simultaneously. The more obvious the consistency of the trend, the more likely the frame winch is to be in normal steady-state operation. At this time, the low-frequency vibration is generated by the regular mechanical rotation caused by the rotation speed. The smaller the value, the weaker the correlation and the weaker the trend consistency between the low-frequency sub-signals of the velocity and vibration signals. If there is no correlation and no trend consistency, it is more likely that the winch is malfunctioning, causing additional motion interference that disrupts normal energy transfer. This disrupts the low-frequency sub-signals of the vibration signal, significantly reducing its correlation with the velocity signal, and making the winch more likely to malfunction. This indicates that the low-frequency sub-signals of the velocity signal and vibration signal are completely negatively correlated and have opposite trends, which is more likely to indicate that there is a reverse force in the mechanical structure, suggesting that there is a equipment failure.

[0058] To facilitate subsequent analysis, this step also maps the Pearson correlation coefficient to a simple linear transformation. Within the interval. Specifically, based on the following conversion formula, from Mapped to :

[0059]

[0060] In this formula, for The range of the transformed value is in Within the interval, The larger the value, the closer it is to 1, indicating a low-frequency sub-signal of the vibration signal. With speed signal The stronger the global trend consistency, the closer it is to 0, indicating that the low-frequency sub-signals of the vibration signal are stronger. With speed signal The weaker the consistency of the overall trend.

[0061] Finally, As and Consistency of global trends.

[0062] S32: Determine the consistency of local trends based on the amplitude differences at adjacent sampling points.

[0063] First, from the perspective of equipment failure mechanism, the development of most mechanical abnormalities is characterized by the gradual spread from local abnormalities to the whole. In the early stage of the failure, local abnormalities often only occur under specific operating conditions (such as sudden changes in speed or instantaneous load fluctuations). As the failure intensifies, it will evolve into a significant global abnormality.

[0064] The consistency of the global trend between the low-frequency sub-signals of the vibration signal and the velocity signal is based on the overall characteristic calculation of all sampling points, and the result is essentially a macroscopic estimate of the signal correlation. However, early equipment failures often manifest as local, instantaneous abnormal fluctuations (such as jamming of the transmission mechanism at a certain moment, or short-term irregular impact of bearing rollers), and these anomalies usually occur at a few adjacent sampling points.

[0065] Therefore, by analyzing the amplitude differences at adjacent sampling points to measure local trend consistency, it is possible to capture local dynamic distortions that are difficult to reflect in global analysis, and to detect early abnormal features. Local trend consistency analysis is a necessary supplement to global trend consistency analysis. By focusing on the subtle correlation features of signals at adjacent sampling points, it can improve the sensitivity of identifying early and local faults. Furthermore, combined with global analysis, it can construct a trend consistency assessment system that combines macroscopic and microscopic perspectives, providing a more comprehensive and accurate basis for equipment status judgment.

[0066] In one embodiment, the consistency of the local trends of the low-frequency sub-signals of the vibration signal and the velocity signal is obtained by the following method:

[0067] S321: Determine the direction of amplitude variation of the low-frequency sub-signal and velocity signal of the vibration signal at each sampling point.

[0068] For the low-frequency sub-signals of vibration signals :

[0069] calculate The difference between the amplitude at each sampling point and the amplitude at the previous sampling point:

[0070]

[0071] In the formula, for In the Amplitude at each sampling point and In the Amplitude at each sampling point The differences between them.

[0072] like , In the The trend of amplitude change at each sampling point is increasing;

[0073] like , In the The trend of amplitude change at each sampling point is decreasing;

[0074] like , In the The amplitude at each sampling point shows no change trend.

[0075] For speed signals :

[0076] calculate The difference between the amplitude at each sampling point and the amplitude at the previous sampling point:

[0077]

[0078] In the formula, for In the Amplitude at each sampling point and In the Amplitude at each sampling point The differences between them.

[0079] like , In the The trend of amplitude change at each sampling point is increasing;

[0080] like , In the The trend of amplitude change at each sampling point is decreasing;

[0081] like , In the The amplitude at each sampling point shows no change trend.

[0082] S322: Determine the consistency of the amplitude trends of the low-frequency sub-signal of the vibration signal and the velocity signal at the same sampling point based on the direction of amplitude change of the two at the same sampling point.

[0083] If the amplitude changes of the low-frequency sub-signal of the vibration signal and the velocity signal at the sampling point are in the same direction, it indicates that at that sampling point, the velocity signal and the low-frequency sub-signal of the vibration signal show a consistent trend, such as the vibration increasing synchronously when the velocity increases, or the vibration decreasing synchronously when the velocity decreases. This consistent trend conforms to the energy transfer law under normal working conditions. The macroscopic motion (velocity) and low-frequency vibration of the mechanical system are driven by the same transmission chain, and instantaneous changes should maintain dynamic coordination without additional interference.

[0084] If the amplitude changes of the low-frequency sub-signal of the vibration signal and the velocity signal at a sampling point are inconsistent, it indicates that at that sampling point, the velocity signal and the low-frequency sub-signal of the vibration signal show opposite trends. For example, the vibration suddenly weakens when the velocity increases, or the vibration strengthens when the velocity decreases. This deviation violates the normal energy transfer logic and usually suggests a local instantaneous anomaly, such as a sudden increase in velocity but a sudden decrease in vibration caused by jamming of transmission components, or a sudden increase in vibration caused by a change in bearing clearance.

[0085] In one embodiment, the consistency of amplitude trends between the low-frequency sub-signal of the vibration signal and the velocity signal at the same sampling point is first calculated:

[0086]

[0087] In the formula, for and In the Consistency in the trend of amplitude at each sampling point It is a symbolic function.

[0088] when and When both are positive or both are negative, it means and In the The amplitude changes at each sampling point follow the same trend. , ,at this time and In the The amplitude trends at each sampling point show the greatest consistency. .

[0089] when and One is positive and the other is negative, which means and In the The amplitude variation trends at each sampling point are different. , ,at this time and In the The trend consistency of the amplitude at each sampling point is minimal. .

[0090] S323: Determine the local trend consistency between the low-frequency sub-signal and the velocity signal of the vibration signal based on the trend consistency of their amplitudes at the same sampling point.

[0091] In one embodiment, the local trend consistency between the low-frequency sub-signal of the vibration signal and the velocity signal is determined by the following method:

[0092] The mean of the amplitude trend consistency between the low-frequency sub-signal and the velocity signal at all sampling points is calculated as the local trend consistency between the low-frequency sub-signal and the velocity signal. Specifically, this means obtaining... and speed signal At all sampling points Then calculate all The mean, as and speed signal Consistency of local trends.

[0093] S33: Combine global trend consistency and local trend consistency to obtain the final trend consistency.

[0094] The global and local trend consistency of the low-frequency sub-signal and velocity signal of the vibration signal are fused to obtain the trend consistency between the low-frequency sub-signal and the velocity signal.

[0095] The greater the global trend consistency and the greater the local trend consistency between the low-frequency sub-signals and velocity signals of the vibration signal, the more consistent their trends are both overall and locally, indicating that the high-speed frame winch is more likely to be in a normal and stable operating state at the current moment. Conversely, the smaller the global trend consistency and the smaller the local trend consistency, the less consistent their trends are both overall and locally, indicating that the high-speed frame winch is more likely to experience abnormal operating conditions at the current moment.

[0096] Therefore, fusion can be achieved by multiplying the global trend consistency of the low-frequency sub-signals of the vibration signal and the velocity signal, and the local trend consistency of the low-frequency sub-signals of the vibration signal and the velocity signal, and the product can be used as the trend consistency of the low-frequency sub-signals of the vibration signal and the velocity signal.

[0097] for and In terms of trend consistency, it is as follows:

[0098]

[0099] In this formula, for and Consistency of trends for and Consistency of local trends for and Consistency of global trends.

[0100] In summary, by quantifying the correlation between the low-frequency sub-signals of the speed signal and vibration signal of the high-speed frame winch through steps S31-S33, it is possible to quickly determine whether the system is in a stable operating state from a macroscopic dynamic perspective. This provides necessary preliminary screening for the subsequent refined analysis of high-frequency fault characteristics, avoids redundant calculations for normal operating conditions, and accurately locates abnormal states that require in-depth evaluation.

[0101] S4: Determine the degree of abnormality of the high-frequency sub-signals of the vibration signal through time-frequency analysis.

[0102] As mentioned earlier, the low-frequency sub-signals of vibration signals mainly reflect the macroscopic motion characteristics of the equipment (such as periodic fluctuations in spool rotation, overall imbalance, etc.), and their consistency with the velocity signal can indicate whether macroscopic energy transfer is normal. However, early equipment failures, such as minor impacts between bearing rollers and tracks, localized wear on gear teeth, and slight loosening of connecting parts, often manifest in high-frequency vibrations. These microscopic anomalies generate high-frequency impact or frictional vibrations, with frequencies much higher than the macroscopic motion frequencies covered by the low-frequency sub-signals, making them difficult to capture with conventional low-frequency velocity information. In the early stages of a failure, such anomalies have minimal impact on macroscopic velocity signals and low-frequency vibrations, but produce obvious abnormal characteristics in high-frequency vibrations.

[0103] Therefore, by analyzing the degree of abnormality of the high-frequency sub-signals of the vibration signal, this step can keenly capture the early characteristics of micro-faults in equipment, filling the gap in macro-trend analysis for identifying subtle anomalies.

[0104] In one embodiment, the method for obtaining the abnormality level of the high-frequency sub-signal of the vibration signal is as follows: Figure 3 As shown, it includes:

[0105] S41: Perform time-frequency analysis on high-frequency sub-signals.

[0106] The Fourier transform is a common time-frequency analysis technique. Its core function is to convert a time-domain signal (a signal that varies with time) into a frequency-domain representation, thereby revealing the frequency components and characteristics of the signal. Using the Fourier transform, we can obtain the frequency distribution of a signal and the energy information at each frequency. This is a core method for analyzing the frequency domain characteristics of signals in signal processing.

[0107] Therefore, by performing a Fourier transform on each high-frequency sub-signal of the vibration signal, we can obtain all the frequencies contained in each high-frequency sub-signal, as well as the energy value at each frequency.

[0108] S42: Determine the signal impulse intensity based on the time-domain characteristics of the high-frequency sub-signal.

[0109] When identifying anomalous features of high-frequency sub-signals of vibration signals through time-frequency analysis, focusing on the quantification of impact intensity in the time domain is of great significance. The most prominent manifestation of mechanical faults (such as pitting impact between bearing rollers and tracks, and meshing impact caused by gear tooth surface cracks) in high-frequency sub-signals is non-stationary instantaneous impact characteristics. These impact signals are usually short in duration and have obvious amplitude abrupt changes, and time domain characteristics can directly capture the nature of their instantaneous abrupt changes.

[0110] The peak value, as the maximum amplitude of a signal in the time domain, directly corresponds to the energy release intensity of a single impact event, and is a direct reflection of the impact force. For mechanical faults, the magnitude of the peak value directly reflects the scale of energy transfer from the fault source during the instantaneous impact process, and the peak value is a key parameter for measuring the physical strength of the impact.

[0111] Kurtosis is significantly sensitive to extreme values ​​of a signal. Under normal operating conditions, vibration signals typically exhibit a near-Gaussian distribution with a kurtosis value close to 3. However, when a local fault exists, sparsely occurring impact signals thicken the tails of the probability distribution, significantly increasing the kurtosis value. This characteristic allows it to effectively amplify the difference between sparse impacts and background steady vibrations, accurately characterizing the degree of deviation of impact events from the norm, and overcoming the limitation that peak values ​​only reflect amplitude magnitude and cannot reflect the frequency and distribution characteristics of impact occurrence.

[0112] Therefore, for each high-frequency sub-signal:

[0113] The deviation between the peak value and the reference peak value of the high-frequency sub-signal, as well as the deviation between the kurtosis of the high-frequency sub-signal and the reference kurtosis, are calculated. The product of these two deviations is taken as the signal impulse intensity of the high-frequency sub-signal. This quantifies the intensity of the impulse characteristics of the high-frequency sub-signal and helps to sensitively identify sparse impulse signals generated by early mechanical faults.

[0114] For the vibration signal High-frequency sub-signals The signal impulse intensity is:

[0115]

[0116] In this formula, for The signal impulse intensity, for peak value The reference peak value is obtained by pre-collecting a vibration signal under normal operating conditions (with the same length as the vibration signal in step S1) as a reference vibration signal. The peak value of each high-frequency sub-signal of the reference vibration signal is obtained following the same operation as in this invention, and the average of the peak values ​​of all high-frequency sub-signals of the reference vibration signal is taken as the reference peak value. , for The steepness, The reference kurtosis is the average of the kurtosis of all high-frequency sub-signals of the reference vibration signal.

[0117] In this formula, The vibration signal is the first High-frequency sub-signals The deviation between the peak value and the reference peak value, The vibration signal is the first High-frequency sub-signals The deviation between the kurtosis and the baseline kurtosis. The combined effect of the two is amplified by multiplication, improving the ability to identify minor fault impacts and enhancing sensitivity to impact faults.

[0118] The greater the deviation between the peak value and the reference peak value of a high-frequency sub-signal of the vibration signal, and the greater the deviation between the kurtosis of the high-frequency sub-signal and the reference kurtosis, the stronger the signal impact intensity of the high-frequency sub-signal, and the more likely it is to be an impact characteristic caused by a fault. Conversely, the smaller the signal impact intensity of the high-frequency sub-signal, the more likely it is to be in a normal and stable operating state.

[0119] Next, the sigmoid function (such as the sigmoid function) is used to... Perform normalization operation (that is, normalize the normalized ... and (Normalize the product of the products) so that In Within the range.

[0120] S43: Determine the degree of energy concentration based on the frequency domain characteristics of the high-frequency sub-signals.

[0121] Frequency domain characteristics reflect the energy distribution of a signal across different frequency components. In mechanical fault diagnosis, the degree of energy concentration in a signal is a key indicator of the significance of fault characteristic frequencies. Mechanical faults (such as bearing pitting and gear cracks) typically exhibit energy accumulation at specific frequencies.

[0122] Specifically, this step assesses the energy concentration of the high-frequency sub-signal based on the entropy values ​​of its energy at all frequencies. First, the entropy values ​​of the high-frequency sub-signal's energy at all frequencies are normalized to obtain a normalized entropy value. The value obtained by subtracting this normalized entropy value from 1 is then taken as the energy concentration of the high-frequency sub-signal.

[0123] For the vibration signal High-frequency sub-signals Its entropy value at all frequencies is:

[0124]

[0125] In this formula, The vibration signal is the first High-frequency sub-signals The entropy of energy at all frequencies. This refers to the index of the frequencies contained in the vibration signal. The total number of frequencies contained in the vibration signal. for In the Energy value at each frequency for The sum of energy values ​​at all frequencies. Quantified The degree of disorder in the frequency domain energy distribution, The larger the value, the more dispersed the frequency domain energy distribution; conversely, the smaller the value, the more dispersed the frequency domain energy distribution. The smaller the value, the more concentrated the frequency domain energy distribution.

[0126] Next, through right Perform a normalization operation to make In Within the interval, This represents the total number of sampling points.

[0127] Ultimately, the energy concentration is:

[0128]

[0129] In this formula, The vibration signal is the first High-frequency sub-signals The degree of energy concentration The vibration signal is the first High-frequency sub-signals The entropy of energy at all frequencies. This represents the total number of sampling points.

[0130] when The more concentrated the frequency domain energy distribution, the greater the degree of energy concentration. The smaller, When the frequency domain energy distribution is completely concentrated at a single frequency, At this point, the energy concentration is at its maximum. .when The more uniform the frequency domain energy distribution, the smaller the degree of energy concentration. The larger, when When the frequency domain energy distribution is completely uniform, At this point, the energy concentration is at its minimum. .

[0131] S44: Analyze the time-frequency consistency of high-frequency sub-signals.

[0132] Under normal operating conditions, the high-frequency sub-signals of vibration signals typically possess stable time-frequency characteristics. However, faults (such as impacts, friction, and cracks) can cause abnormal coupling between the time and frequency domain characteristics of these high-frequency sub-signals. Mechanical faults (such as gear meshing impacts) often produce responses simultaneously in both the time domain (manifested as impact pulses) and the frequency domain (manifested as sudden increases in energy of specific frequency components). If the time-domain impact characteristics and frequency-domain energy concentration characteristics of the high-frequency sub-signals appear simultaneously, the likelihood of a fault occurring is higher.

[0133] Therefore, the first coefficient of variation is calculated based on the amplitude of the high-frequency sub-signal at all sampling points, and the second coefficient of variation is calculated based on all frequencies of the high-frequency sub-signal. The difference between the first coefficient of variation and the second coefficient of variation is used as a negative correlation index of time-frequency consistency to quantify time-frequency consistency.

[0134] For the vibration signal High-frequency sub-signals Its time-frequency consistency satisfies the following relationship:

[0135]

[0136] In this formula, The vibration signal is the first High-frequency sub-signals Time-frequency consistency. for The first coefficient of variation, i.e., the first... of the vibration signal High-frequency sub-signals Calculate the coefficient of variation for all amplitudes at all sampling points. The specific value is The ratio of the standard deviation to the mean of the amplitude at all sampling points. for The second coefficient of variation, i.e., the vibration signal's... High-frequency sub-signals The coefficient of variation for all frequencies, The specific value is The ratio of the standard deviation to the mean of all frequencies, where || represents the absolute value. It is a natural exponential function.

[0137] In this formula, The difference between the first and second coefficients of variation is denoted by . A smaller difference indicates better time-frequency consistency, and vice versa. Therefore, the natural exponential function can be used to... Build and A negative correlation.

[0138] In short, Essentially, it is the quantification of the first High-frequency sub-signals The synergy of time-frequency characteristic changes: if abnormal fluctuations occur simultaneously in both the time and frequency domains, then the changes in both are considered to be consistent. The more likely such abnormal fluctuations are to be caused by a common response due to a fault.

[0139] S45: Determine the degree of anomaly by fusing the signal impulse intensity, energy concentration, and time-frequency consistency.

[0140] From the theoretical perspective of signal processing, the signal impulse intensity reflects the transient impulse component of the signal, which is directly related to the pulse response caused by local faults (such as bearing pitting). The degree of energy concentration can effectively identify resonant frequency band shifts or abnormal energy accumulation phenomena. Time-frequency consistency quantifies the stability of time-frequency distribution and can capture modulation characteristics under non-stationary conditions. By fusing these three indicators to determine the degree of anomaly, it not only conforms to the physical mechanism of the fault but also improves the detection robustness through feature complementarity.

[0141] Specifically, for each high-frequency sub-signal of the vibration signal, the product of the signal impact intensity, energy concentration, and time-frequency consistency of the high-frequency sub-signal is taken as the degree of abnormality of the high-frequency sub-signal.

[0142] The vibration signal High-frequency sub-signals The degree of abnormality is:

[0143]

[0144] In this formula, The vibration signal is the first High-frequency sub-signals The degree of abnormality, for The degree of energy concentration for Time-frequency consistency, for The signal impact intensity.

[0145] Finally, the average of the anomalies of all high-frequency sub-signals is taken as the anomaly level of the high-frequency sub-signals of the vibration signal, that is, the average of the anomalies of all high-frequency sub-signals of the vibration signal. The mean value is used as the degree of abnormality of the high-frequency sub-signal of the vibration signal.

[0146] This is because an anomaly in a single high-frequency sub-signal may be local interference, while the mean can integrate information from multiple frequency bands. If multiple sub-signals show anomalies, the mean will increase significantly, better reflecting the overall high-frequency anomaly trend of the equipment; if only a few high-frequency sub-signals are abnormal, the mean will be diluted, reducing local interference.

[0147] S5: Based on the consistency of trends between low-frequency sub-signals and speed signals, as well as the degree of anomaly in high-frequency sub-signals, the operating condition anomaly index of the high-speed frame winch is determined comprehensively.

[0148] The low-frequency information (low-frequency sub-signals of speed and vibration signals) during the operation of a high-speed frame winch reflects the macroscopic state of the winch's operation, i.e., the degree of matching between speed and low-frequency vibration. A higher degree of matching between speed and low-frequency vibration results in more coordinated changes, conforming more closely to normal load variations, leading to smoother operation and more normal working conditions for the high-speed frame winch. Conversely, a lower degree of matching between speed and low-frequency vibration results in less coordinated changes, less conforming to normal load variations, indicating potential mechanical faults such as loose transmission systems, bearing wear, or sudden load changes, indicating a clearly abnormal state requiring priority warning. The high-frequency information (high-frequency sub-signals of vibration signals) during the operation of a high-speed frame winch reflects microscopic transient anomalies, such as impacts, friction, and localized damage, and is typically used for early warning.

[0149] Therefore, the better the consistency between the low-frequency sub-signals and velocity signals of the vibration signal, and the smaller the degree of abnormality in the high-frequency sub-signals of the vibration signal, the more likely the high-speed frame winch is in a normal and stable operating state. Conversely, the worse the consistency between the low-frequency sub-signals and velocity signals of the vibration signal, and the greater the degree of abnormality in the high-frequency sub-signals of the vibration signal, the more likely the high-speed frame winch has malfunctioned.

[0150] Specifically, the abnormal operating condition index of the high-speed frame winch is determined as follows:

[0151] The consistency of trends between low-frequency sub-signals and speed signals is used as the first parameter, and the degree of anomaly of high-frequency sub-signals is used as the second parameter. The first parameter is converted into a negative correlation index of the operating condition anomaly index by subtracting one. The product of the second parameter and the negative correlation index after performing an increment operation is used as the operating condition anomaly index.

[0152] Expressed as a formula:

[0153]

[0154] Where, For high-speed frame stranding machines The abnormal operating condition index at any given time. Low-frequency sub-signal and speed signal The trend consistency (first parameter). The degree of abnormality of the high-frequency sub-signal of the vibration signal (second parameter).

[0155] In this formula, through Build and A negative correlation. When The larger the value, and the closer it is to 1, the stronger the low-frequency sub-signal. and speed signal The stronger the trend consistency, the less obvious the operating condition abnormality of the high-speed frame winch. The smaller the value, the closer it is to 0, and the more abnormal the operating condition index becomes. Take the lead, at this time The larger the value, the greater the abnormality of the high-frequency sub-signals of the vibration signal, indicating that the high-speed frame winch is more likely to have a microscopic fault. The smaller the value, the closer it is to 0, indicating a low-frequency sub-signal. and speed signal The weaker the trend consistency, the more likely it is that the high-speed frame winch has significant operating abnormalities. At this time... The larger the value, the greater the operating condition anomaly index. At this point, the operating condition anomaly index changes from... To take the lead, if at this time The larger the value, the more it amplifies macroscopic anomalies and enhances the sensitivity of fault early warning. At this point, if... The smaller, the better avoid Macroeconomic anomalies Unreasonable reduction of [something] ensures that macroscopic anomalies trigger fault warnings first.

[0156] In summary, this design can capture obvious anomalies when low-frequency trends are inconsistent, and can also discover potential faults through high-frequency information when low-frequency trends are consistent, thus achieving an effective fusion of two-dimensional anomaly monitoring and balancing the timeliness and accuracy of fault detection.

[0157] S6: Control the high-speed frame winch based on the abnormal operating condition index.

[0158] The primary and secondary thresholds for the abnormal operating condition index are set to 0.3 and 0.7 respectively, both of which are empirical values. At the current moment, if the abnormal operating condition index obtained from steps S1 to S5 is less than 0.3, it is determined that no early abnormality has occurred, and the high-speed frame winch continues to operate with the parameters corresponding to the current operating condition. If the abnormal operating condition index obtained from steps S1 to S5 is greater than 0.3 and less than or equal to 0.7, it is determined that a minor early fault has occurred, and the parameters corresponding to the current operating condition are reduced by a preset percentage (empirical value) of 10%, and the operator is prompted to continue monitoring. If the abnormal operating condition index obtained from steps S1 to S5 is greater than 0.7, it is determined that the high-speed frame winch has experienced a significant early fault, and the speed is immediately reduced to idle speed, and the operator is prompted to stop the machine for inspection.

[0159] The present invention provides an intelligent online control system for a high-speed frame stranding machine, comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the operations of steps S1-S6, thereby performing intelligent online control of the high-speed frame stranding machine.

[0160] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only.

Claims

1. A method for intelligent online control of a high-speed frame winch, characterized in that, include: The speed and vibration signals of the high-speed frame winch are collected during operation, and the high-frequency and low-frequency sub-signals of the vibration signal are obtained through signal decomposition technology. For low-frequency sub-signals, trend analysis is used to determine the consistency of trends between low-frequency sub-signals and velocity signals; For high-frequency sub-signals, perform time-frequency analysis, including: In the time domain, the signal impulse intensity of the high-frequency sub-signal is determined by the peak value and kurtosis of the high-frequency sub-signal, and the first coefficient of variation is calculated based on the amplitude of the high-frequency sub-signal at all sampling points. In the frequency domain, the energy concentration of a high-frequency sub-signal is assessed based on its entropy values ​​across all frequencies. This includes: normalizing the entropy values ​​of the high-frequency sub-signal's energy across all frequencies to obtain a normalized entropy value; and subtracting this normalized entropy value from 1 to obtain the energy concentration of the high-frequency sub-signal. The energy concentration is as follows: , The first vibration signal High-frequency sub-signals The degree of energy concentration The first vibration signal High-frequency sub-signals The entropy of energy at all frequencies. This represents the total number of sampling points; The second coefficient of variation is calculated based on all frequencies of the high-frequency sub-signal; the time-frequency consistency of the high-frequency sub-signal is determined based on the difference between the first and second coefficients of variation. The degree of anomaly of a high-frequency sub-signal is determined by fusing signal impulse intensity, energy concentration, and time-frequency consistency. This includes using the product of signal impulse intensity, energy concentration, and time-frequency consistency as the degree of anomaly of the high-frequency sub-signal. Based on the trend consistency of the low-frequency sub-signal and the speed signal, and the degree of anomaly of the high-frequency sub-signal, a comprehensive determination of the operating condition anomaly index of the high-speed frame winch is made. This includes: using the trend consistency of the low-frequency sub-signal and the speed signal as the first parameter, and the degree of anomaly of the high-frequency sub-signal as the second parameter; converting the first parameter into a negative correlation index by subtracting one; and multiplying the second parameter by this negative correlation index after adding one to it to obtain the operating condition anomaly index. The operating condition anomaly index is: , For high-speed frame stranding machines The abnormal operating condition index at any given time. Low-frequency sub-signal and speed signal Consistency of trends To determine the degree of abnormality of the high-frequency sub-signals of the vibration signal, by... Build and A negative correlation; The high-speed frame winch is controlled based on the abnormal operating condition index.

2. The intelligent online control method according to claim 1, characterized in that, The consistency of trends between the low-frequency sub-signal and the velocity signal is determined based on the following method: Based on the amplitudes of the low-frequency sub-signal and the velocity signal at all sampling points, the Pearson correlation coefficient between the low-frequency sub-signal and the velocity signal is calculated, and this Pearson correlation coefficient is used as the global trend consistency. The direction of amplitude change of the low-frequency sub-signal and velocity signal at each sampling point is determined based on the difference between the amplitude at each sampling point and the amplitude at the previous sampling point. Based on the direction of amplitude change of the low-frequency sub-signal and the velocity signal at the same sampling point, the local trend consistency of the amplitude of the low-frequency sub-signal and the velocity signal is determined. The global trend consistency index and the local trend consistency index are fused to obtain the trend consistency between the low-frequency sub-signal and the velocity signal.

3. The intelligent online control method according to claim 1, characterized in that, The signal impulse intensity of the high-frequency sub-signal is determined based on the following method: Calculate the first deviation between the peak value of the high-frequency sub-signal and the reference peak value, and the second deviation between the kurtosis of the high-frequency sub-signal and the reference kurtosis. Use the normalized value of the product of the first deviation and the second deviation as the signal impulse intensity of the high-frequency sub-signal.

4. The intelligent online control method according to claim 1, characterized in that, The first coefficient of variation is the ratio of the standard deviation to the mean of the amplitude of the high-frequency sub-signal at all sampling points, and the second coefficient of variation is the ratio of the standard deviation to the mean of all frequencies of the high-frequency sub-signal.

5. The intelligent online control method according to claim 1, characterized in that, The time-frequency consistency of high-frequency sub-signals is determined based on the following method: The difference between the first and second coefficients of variation is calculated and used as a negative correlation indicator of time-frequency consistency to quantify time-frequency consistency.

6. The intelligent online control method according to claim 1, characterized in that, The method for controlling a high-speed frame stranding machine is as follows: Set the primary and secondary thresholds for the abnormal operating condition index; If the abnormal operating condition index is less than the first-level threshold, it is determined that no early abnormality has occurred in the operating condition, and the high-speed frame winch continues to operate with the parameters corresponding to the current operating condition. If the abnormal operating condition index is greater than the first-level threshold but not greater than the second-level threshold, it is determined that a minor early fault has occurred in the operating condition. The parameters corresponding to the current operating condition are reduced by a preset percentage and the operation continues, and the staff is prompted to pay close attention. If the abnormal operating condition index is greater than the secondary threshold, it is determined that the operating condition has caused an obvious early fault. The speed should be immediately reduced to idle speed, and the staff should be prompted to stop the machine for inspection.

7. An intelligent online control system for a high-speed frame stranding machine, characterized in that, The intelligent online control system includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the online control method as described in any one of claims 1-6.

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