Rotary table online flutter control method based on multi-sensor fusion

Abstract

The invention relates to the technical field of industrial automation, and discloses a rotary table online flutter control method based on multi-sensor fusion, which comprises the following steps: performing signal quality evaluation on a vibration signal sequence and an attitude signal set to obtain an effective vibration sequence and an effective attitude set; generating state description information corresponding to the effective attitude set; segmenting and splitting the effective vibration sequence to obtain a split vibration sequence, and calculating a segmentation deviation measure corresponding to the split vibration sequence to split and adjust the effective vibration sequence to obtain an adjusted vibration sequence; generating flutter characterization data of the turntable system, and calculating a coupling correlation degree between flutter characterization factors to determine a dominant flutter characterization factor in the flutter characterization factors; and determining a chatter source of the rotary table system, and executing rotary table chatter suppression processing on the rotary table system to obtain a processing result. The accuracy of online flutter control of the rotary table can be improved.

Description

Technical Field

[0001] This invention relates to an online chatter control method for turntables based on multi-sensor fusion, belonging to the field of industrial automation technology. Background Technology

[0002] In fields such as precision testing and high-end security, the stable operation of a turntable system directly determines the accuracy of operations. However, it often faces the problem of flutter caused by multi-source environmental interference. Although the vibration signals and attitude data generated during turntable operation contain flutter characteristic information, the actual acquired signals have significant technical difficulties: First, the vibration signals are mixed with broadband noise and mechanical resonance components, and the attitude signals are affected by transmission clearance and have periodic drift, resulting in a low signal-to-noise ratio of the original data. Second, there is an implicit spatiotemporal correlation between the two types of signals. For example, in strong wind environments, the vibration amplitude and attitude angle deviation are nonlinearly coupled, and traditional single signal analysis is difficult to capture this correlation feature.

[0003] Existing technologies mostly employ independent sensor data processing and fixed parameter control: In the data processing stage, they rely on manually setting thresholds to filter effective signals, which is difficult to adapt to dynamic noise changes in extreme environments such as high temperature and dust; in the feature extraction stage, vibration frequency or attitude offset is analyzed in isolation, ignoring the correlation between video domain features and spatiotemporal distribution, leading to deviations in flutter source identification; and they cannot cope with flutter mode variations caused by changes in turntable load or sudden changes in environmental interference intensity, thus reducing the accuracy of online turntable flutter control. Summary of the Invention

[0004] This invention provides a turntable online chatter control method based on multi-sensor fusion, the main purpose of which is to improve the accuracy of turntable online chatter control.

[0005] To achieve the above objectives, this invention provides an online flutter control method for turntables based on multi-sensor fusion, comprising:

[0006] Receive multi-source sensor data from the turntable system, perform signal separation processing on the multi-source sensor data to obtain a vibration signal sequence and an attitude signal set, and perform signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set.

[0007] Identify the flutter-related variables in the effective attitude set, and extract the frequency domain spatial feature vector from the effective attitude set by combining the preset flutter feature library with the flutter-related variables to generate the state description information corresponding to the effective attitude set.

[0008] The effective vibration sequence is segmented to obtain a segmented vibration sequence. The segment deviation measure corresponding to the segmented vibration sequence is calculated to adjust the effective vibration sequence and obtain an adjusted vibration sequence.

[0009] Combining the state description information with the adjusted vibration sequence, flutter characterization data of the turntable system is generated, flutter characterization factors are extracted from the flutter characterization data, and the coupling correlation between the flutter characterization factors is calculated to determine the dominant flutter characterization factor among the flutter characterization factors.

[0010] Based on the dominant flutter characterization factor, the flutter source of the turntable system is determined. Combining the flutter source with the coupling correlation, turntable flutter suppression processing is performed on the turntable system to obtain the processing result.

[0011] Optionally, the step of performing signal separation processing on the multi-source sensing data to obtain a vibration signal sequence and an attitude signal set includes:

[0012] The multi-source sensing data is subjected to frequency domain feature decomposition to obtain high-frequency components and low-frequency components;

[0013] The high-frequency components are subjected to vibration enhancement processing to obtain an enhanced vibration signal;

[0014] Attitude extraction is performed on the low-frequency components to obtain a preliminary attitude signal;

[0015] The system characteristics of the turntable system are analyzed, and based on these characteristics, the enhanced vibration signal and the preliminary attitude signal are subjected to component harmonic processing to obtain a vibration signal sequence and an attitude signal set.

[0016] Optionally, the step of performing signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set includes:

[0017] The integrity of the vibration signal sequence is checked to obtain candidate vibration sequences;

[0018] The candidate vibration sequences are subjected to signal-to-noise ratio evaluation to obtain reliable vibration sequences;

[0019] Outlier removal is performed on the reliable vibration sequence to obtain the valid vibration sequence;

[0020] Based on the effective vibration sequence, the attitude signal set is subjected to a matching verification process to obtain an effective attitude set.

[0021] Optionally, the step of extracting frequency domain spatial feature vectors from the effective attitude set by combining a preset flutter feature library with the flutter correlation variables includes:

[0022] Extract the flutter-related variables from the set of effective attitudes;

[0023] The flutter feature library is structured to obtain a feature index set;

[0024] Analyze the matching degree between the flutter-related variables and the feature index set;

[0025] Based on the matching degree, the associated feature terms corresponding to the flutter associated variables are selected from the feature index set, and the frequency domain feature set of the associated feature terms is extracted;

[0026] By combining the spatial distribution information of the effective attitude set with the frequency domain feature set, a corresponding frequency domain spatial feature vector is generated.

[0027] Optionally, calculating the segmented deviation measure corresponding to the split vibration sequence includes:

[0028] The split vibration sequence is subjected to time-frequency feature mapping to obtain a time-frequency distribution set;

[0029] Based on the aforementioned time-frequency distribution set, analyze the time-frequency collaborative characteristics corresponding to the split vibration sequence;

[0030] Based on the aforementioned time-frequency co-occurrence characteristics, the split vibration sequence is classified into stationary segments and non-stationary segments.

[0031] The number of segments corresponding to the stationary signal segments and the non-stationary signal segments is counted to obtain the number of stationary segments and the number of non-stationary segments.

[0032] By combining the number of stationary segments, the time-frequency co-occurrence characteristics, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated.

[0033] Optionally, by combining the number of stationary segments, the time-frequency co-occurrence feature, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated, including:

[0034] Based on the aforementioned time-frequency co-operational characteristics, the stationarity index corresponding to each signal segment in the split vibration sequence is calculated;

[0035] By combining the number of stationary segments and the number of non-stationary segments, the stationarity ratio corresponding to the split vibration sequence is calculated;

[0036] Based on the stationarity ratio, set the stationarity weight factor corresponding to the split vibration sequence;

[0037] Combining the stationarity weighting factor, the stationarity index, the number of stationary segments, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated using the following formula:

[0038]

[0039] Where B represents the segmented deviation measure corresponding to the split vibration sequence. Indicates a stationary weighting factor. This represents the stationarity index corresponding to the j-th signal segment in the split vibration sequence. Indicates the number of stationary segments. denoted by j, which represents the number of non-stationary segments, and m represents the number of signal segments in the split vibration sequence.

[0040] Optionally, calculating the stationarity index corresponding to each signal segment in the split vibration sequence based on the time-frequency co-features includes:

[0041] The time-frequency co-operation features are processed by signal component extraction to obtain the frequency-time domain correlated composition;

[0042] The frequency-time domain correlation components are enhanced to obtain enhanced coherence features;

[0043] The enhanced coherence features are subjected to robust constraint processing to obtain a constrained feature set;

[0044] The constraint feature set is subjected to multidimensional aggregation processing to obtain aggregated constraint features;

[0045] Analyze the scale attribute of the aggregation constraint feature and calculate the attribute contribution corresponding to the scale attribute;

[0046] By combining the attribute contribution and the aggregation constraint features, the stationarity index corresponding to each signal segment in the split vibration sequence is calculated.

[0047] Optionally, calculating the coupling correlation between the flutter indices includes:

[0048] Calculate the interaction strength between the flutter indices and determine the amplitude mapping value corresponding to the flutter indices;

[0049] Combining the interaction strength and the amplitude mapping value, the coupling correlation between the flutter indices is calculated using the following formula:

[0050]

[0051] Where F represents the coupling correlation between flutter indices, This represents the interaction strength between the p-th flutter index and the (p+1)-th flutter index. This represents the p-th flutter index. This represents the (p+1)th flutter index. This represents the amplitude mapping value of the p-th flutter index. This represents the amplitude mapping value of the (p+1)th flutter index, where p represents the sequence number of the flutter index and Q represents the number of flutter indices.

[0052] Optionally, determining the flutter source of the turntable system based on the dominant flutter characterization factor includes:

[0053] The physical meaning of the dominant flutter characterization factor is analyzed, and based on the physical meaning, the characteristic correlation topology corresponding to the dominant flutter characterization factor is constructed.

[0054] Fault derivation processing is performed on the aforementioned characteristic-related topology to obtain a hierarchical derivation structure;

[0055] Extract the dominant source factors from the hierarchical derivation structure, trace the source of the dominant source factors, and obtain the factor tracing results;

[0056] Calculate the influence factor of the dominant source factor in the hierarchical derivation structure;

[0057] Based on the aforementioned influencing factors, the results of the factor tracing are verified to obtain the flutter source corresponding to the turntable system.

[0058] Optionally, calculating the influence factor of the dominant source factor in the hierarchical derivation structure includes:

[0059] Calculate the structural importance of the source dominant factor in the hierarchical derivation structure;

[0060] The topological depth of the source-dominant factors in the hierarchical derivation structure is statistically analyzed.

[0061] Combining the structural importance and the topological depth, the influence factor of the source dominance factor in the hierarchical derivation structure is calculated using the following formula:

[0062]

[0063] Where I represents the influence factor of the source dominant factor in the hierarchical derivation structure, S represents the structural importance of the factors among the source dominant factors, R represents the physical correlation strength of the factors among the source dominant factors, D represents the topological depth of the factors among the source dominant factors, and k represents the depth decay coefficient.

[0064] To address the aforementioned problems, this invention also provides an online flutter control system for turntables based on multi-sensor fusion, the system comprising:

[0065] The sensor data processing module is used to receive multi-source sensor data from the turntable system, perform signal separation processing on the multi-source sensor data to obtain a vibration signal sequence and an attitude signal set, and perform signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set.

[0066] The attitude feature extraction module is used to identify flutter-related variables in the effective attitude set, and extract frequency domain spatial feature vectors from the effective attitude set by combining the preset flutter feature library with the flutter-related variables to generate state description information corresponding to the effective attitude set.

[0067] The vibration sequence adjustment module is used to segment the effective vibration sequence to obtain a segmented vibration sequence, calculate the segment deviation measure corresponding to the segmented vibration sequence, and then segment and adjust the effective vibration sequence to obtain an adjusted vibration sequence.

[0068] The dominant factor determination module is used to combine the state description information with the adjusted vibration sequence to generate flutter characterization data of the turntable system, extract flutter characterization factors from the flutter characterization data, calculate the coupling correlation degree between the flutter characterization factors, and determine the dominant flutter characterization factor among the flutter characterization factors.

[0069] The flutter suppression processing module is used to determine the flutter source of the turntable system based on the dominant flutter characterization factor, and to perform turntable flutter suppression processing on the turntable system in combination with the flutter source and the coupling correlation degree to obtain the processing result.

[0070] Compared to the problems described in the background art, this invention, by performing signal separation processing on the multi-source sensor data, can decompose the mixed raw sensor data into independent signal components characterizing different physical phenomena, providing clear data objects for subsequent signal quality evaluation and effectively improving the accuracy of turntable system state perception. Furthermore, by identifying flutter-related variables in the effective attitude set, this invention can locate key variables related to flutter phenomena from the attitude data, making subsequent analysis more focused on core influencing factors and facilitating the capture of flutter-related attitude change characteristics. Moreover, by segmenting the effective vibration sequence to obtain a segmented vibration sequence, this invention can initially divide continuous vibration data into multiple analysis units; and by calculating the segmentation deviation measure corresponding to the segmented vibration sequence, it can quantify each segment... The internal consistency of the segmented signals and the rationality of the segmentation boundaries support improved processing of the segmentation process. Furthermore, by combining the state description information with the adjusted vibration sequence, this invention generates flutter characterization data for the turntable system, thereby obtaining a comprehensive data expression of the turntable system's flutter state. By extracting flutter characterization factors from the flutter characterization data, key physical quantity identifiers of the flutter state can be obtained, providing a basis for subsequent calculation of the coupling correlation between the flutter characterization factors. Finally, this invention determines the flutter source of the turntable system based on the dominant flutter characterization factors, thereby locating the root cause of turntable flutter and providing a clear target for subsequent targeted flutter suppression processing, thus facilitating improvements in the accuracy of online flutter control of the turntable system. Therefore, the turntable online flutter control method based on multi-sensor fusion provided by this invention can improve the accuracy of online turntable flutter control. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating an online flutter control method for a turntable based on multi-sensor fusion, provided in an embodiment of the present invention.

[0072] Figure 2 This is a schematic diagram of the vibration sequence splitting and adjustment process in the turntable online flutter control method based on multi-sensor fusion provided by the present invention;

[0073] Figure 3 This is a schematic diagram of a module for implementing a turntable online flutter control system based on multi-sensor fusion, according to an embodiment of the present invention.

[0074] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0075] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0076] This application provides a turntable online flutter control method based on multi-sensor fusion. The executing entity of this multi-sensor fusion-based turntable online flutter control method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the multi-sensor fusion-based turntable online flutter control method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0077] Reference Figure 1 The diagram shown is a flowchart illustrating an online turntable flutter control method based on multi-sensor fusion according to an embodiment of the present invention. In this embodiment, the online turntable flutter control method based on multi-sensor fusion includes:

[0078] S1. Receive multi-source sensor data from the turntable system, perform signal separation processing on the multi-source sensor data to obtain a vibration signal sequence and an attitude signal set, and perform signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set.

[0079] This invention performs signal separation processing on the multi-source sensor data, which can decompose the mixed raw sensor data into independent signal components that characterize different physical phenomena, providing clear data objects for subsequent signal quality evaluation and effectively improving the accuracy of turntable system status perception.

[0080] The multi-source sensor data refers to the raw measurement data collected by various types of sensors during the operation of the turntable system, reflecting the overall state of the system, such as time-domain signal sequences obtained by accelerometers, gyroscopes, and displacement sensors; the vibration signal sequence refers to the signal sequence mainly containing turntable vibration components separated from the multi-source sensor data, reflecting the mechanical vibration state of the turntable; the attitude signal set refers to the signal set separated from the multi-source sensor data used to describe the spatial orientation and motion attitude of the turntable, such as the instantaneous value of the rotation angle, angular velocity, and angular acceleration.

[0081] As an embodiment of the present invention, the step of performing signal separation processing on the multi-source sensing data to obtain a vibration signal sequence and an attitude signal set includes:

[0082] The multi-source sensing data is subjected to frequency domain feature decomposition to obtain high-frequency components and low-frequency components;

[0083] The high-frequency components are subjected to vibration enhancement processing to obtain an enhanced vibration signal;

[0084] Attitude extraction is performed on the low-frequency components to obtain a preliminary attitude signal;

[0085] The system characteristics of the turntable system are analyzed, and based on these characteristics, the enhanced vibration signal and the preliminary attitude signal are subjected to component harmonic processing to obtain a vibration signal sequence and an attitude signal set.

[0086] Wherein, the high-frequency component and the low-frequency component are different frequency band signal outputs obtained after the multi-source sensing data is divided into frequency domains; the enhanced vibration signal is the signal after the high-frequency component has been enhanced to highlight the vibration characteristics; the preliminary attitude signal is the basic attitude information obtained after the low-frequency component has been extracted and processed; the system characteristics are the inherent physical properties and sensing configuration information of the turntable system; the vibration signal sequence and attitude signal set are the final signal outputs that are purified and represent vibration and attitude respectively after component harmonization.

[0087] Furthermore, the multi-source sensor data can be decomposed in the frequency domain using Fast Fourier Transform or Wavelet Transform to obtain high-frequency and low-frequency components. For example, a specific frequency threshold can be set as a boundary to separate segments mainly containing high-frequency vibration noise and segments mainly containing low-frequency attitude changes. The high-frequency components can be enhanced using envelope analysis or resonance demodulation techniques to obtain a strengthened vibration signal, such as calculating the envelope of the vibration signal to highlight its impact characteristics. The low-frequency components can be used for attitude extraction using Kalman filtering or complementary filtering to obtain a preliminary attitude signal, such as estimating the real-time angle and angular velocity of the turntable by fusing gyroscope and accelerometer data. Based on the system characteristics, the strengthened vibration signal and the preliminary attitude signal can be harmonic-computed using sensor calibration parameters and system kinematics to obtain a vibration signal sequence and an attitude signal set. For example, the phase difference between signals can be compensated based on the sensor installation position, or coupling interference caused by vibration in the attitude signal can be eliminated according to the kinematic equations to ensure that the two types of signals are independent and accurate.

[0088] This invention obtains effective vibration sequences and effective attitude sets by evaluating the signal quality of the vibration signal sequence and the attitude signal set. It can identify highly reliable sensor information that can be used for subsequent flutter analysis, and eliminate low-quality data caused by sensor failure, external interference, or abnormal signal transmission, ensuring the reliability of the data entering the subsequent processing flow. The effective vibration sequence and the effective attitude set are respectively the signal sequence and signal set that meet the quality requirements after evaluation from the vibration signal sequence and the attitude signal set.

[0089] As an embodiment of the present invention, the step of performing signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set includes:

[0090] The integrity of the vibration signal sequence is checked to obtain candidate vibration sequences;

[0091] The candidate vibration sequences are subjected to signal-to-noise ratio evaluation to obtain reliable vibration sequences;

[0092] Outlier removal is performed on the reliable vibration sequence to obtain the valid vibration sequence;

[0093] Based on the effective vibration sequence, the attitude signal set is subjected to a matching verification process to obtain an effective attitude set.

[0094] The candidate vibration sequence is a preliminary qualified signal after the vibration signal sequence has undergone integrity checks and eliminated serious damage problems such as signal breakage and data loss; the reliable vibration sequence is a signal of the candidate vibration sequence that has undergone signal-to-noise ratio evaluation, meets the signal-to-noise ratio requirements, and ensures that the proportion of effective information in the signal is higher than that of interference; the effective vibration sequence is a stable signal of the reliable vibration sequence after outlier removal, removal of abnormal data such as sudden pulses and extreme deviations; the fit verification process is a verification process based on the spatiotemporal characteristics and data patterns of the effective vibration sequence to verify whether the attitude signal set has a reasonable correlation with the vibration signal; the effective attitude set is a precise attitude signal set after fit verification processing and removal of abnormal attitude data that is unrelated to or out of sync with the effective vibration sequence.

[0095] Furthermore, the integrity of the vibration signal sequence can be checked through signal continuity analysis and data missing rate statistics to obtain candidate vibration sequences. For example, a data missing rate threshold can be set (e.g., below 5%). If the missing rate of a segment of the vibration signal exceeds the threshold, it is determined to be a damaged segment and is removed, and the remaining segments form the candidate vibration sequences. The signal-to-noise ratio (SNR) can be calculated and the power spectral density analyzed to evaluate the candidate vibration sequences and obtain reliable vibration sequences. For example, the ratio of the effective signal value to the effective noise value can be calculated using the time-domain SNR formula, and a SNR greater than 20dB can be set as a qualified standard to screen out reliable vibration sequences that meet the standard. The reliability of the candidate vibration sequences can be evaluated using the 3σ criterion or the box plot method. Outlier removal is performed on the vibration sequence to obtain the valid vibration sequence. For example, the mean and standard deviation of the reliable vibration sequence are calculated, and data that deviates from the mean by more than 3 times the standard deviation are identified as outliers and deleted. The remaining data are the valid vibration sequences. Based on the valid vibration sequences, the attitude signal set is subjected to matching and verification through spatiotemporal correlation and signal synchronization to obtain the valid attitude set. For example, based on the acquisition timestamp of the valid vibration sequence, the attitude data at the corresponding time point in the attitude signal set is verified to see if it conforms to the turntable motion logic (e.g., the attitude angle change corresponding to the vibration peak period should be within a reasonable range). Attitude data that is out of sync or has contradictory motion logic are removed, and finally the valid attitude set is obtained.

[0096] S2. Identify the flutter-related variables in the effective attitude set, and extract the frequency domain spatial feature vector from the effective attitude set by combining the preset flutter feature library with the flutter-related variables, so as to generate the state description information corresponding to the effective attitude set.

[0097] This invention identifies flutter-related variables in the effective attitude set, enabling the location of key variables associated with flutter phenomena from attitude data. This allows subsequent analysis to focus more on core influencing factors and facilitates the capture of flutter-related attitude change characteristics. The flutter feature library refers to a pre-defined set storing flutter-related feature information, such as recording the attitude angle fluctuation range and vibration frequency interval corresponding to different flutter intensities. The flutter-related variables refer to attitude parameters identified from the effective attitude set that are associated with flutter phenomena, such as the attitude angle change rate, angular velocity fluctuation value, and spatial position deviation. For example, by analyzing parameters in the attitude data that change significantly with increasing flutter, the angular velocity fluctuation value is determined to be a flutter-related variable. Optionally, the identification of flutter-related variables in the effective attitude set can be achieved using statistical analysis methods, such as calculating the correlation coefficient between each attitude parameter and the flutter phenomenon, and selecting parameters with high correlation as flutter-related variables.

[0098] As an embodiment of the present invention, the step of extracting frequency domain spatial feature vectors from the effective attitude set by combining a preset flutter feature library with the flutter correlation variables includes:

[0099] Extract the flutter-related variables from the set of effective attitudes;

[0100] The flutter feature library is structured to obtain a feature index set;

[0101] Analyze the matching degree between the flutter-related variables and the feature index set;

[0102] Based on the matching degree, the associated feature terms corresponding to the flutter associated variables are selected from the feature index set, and the frequency domain feature set of the associated feature terms is extracted;

[0103] By combining the spatial distribution information of the effective attitude set with the frequency domain feature set, a corresponding frequency domain spatial feature vector is generated.

[0104] The flutter-related variables refer to attitude parameters directly related to flutter phenomena extracted from the effective attitude set, such as attitude angle fluctuation rate and shaft deviation amplitude. The feature index set is an indexed feature set formed by structuring the flutter feature library, containing index information such as feature classification labels and feature value ranges. The matching degree is a quantitative indicator that measures the degree of fit between the flutter-related variables and feature items in the feature index set, such as feature attribute overlap and numerical interval overlap rate. The associated feature items are feature entries in the feature index set that meet the set-standard matching degree with the flutter-related variables, such as specific flutter frequency features that match the shaft deviation amplitude. The frequency domain feature set is a set of frequency dimension features extracted from the associated feature items, including vibration dominant frequency, spectral bandwidth, and harmonic components. The spatial distribution information is the spatial distribution characteristics of attitude parameters in the effective attitude set, such as the attitude deviation distribution at different measurement points and spatial gradient changes. The frequency domain spatial feature vector is a vector-form feature data formed by fusing the frequency domain feature set and spatial distribution information, simultaneously reflecting the comprehensive characteristics of flutter in both frequency and spatial dimensions.

[0105] Furthermore, flutter-related variables can be extracted from the effective attitude set through signal interception and feature recognition, such as intercepting flutter-sensitive segments in the attitude data and identifying parameters with significant fluctuations; the flutter feature library can be structured through feature classification and index coding to obtain a feature index set, for example, classifying features according to flutter intensity levels and assigning unique index codes; the matching degree between the flutter-related variables and the feature index set can be analyzed through feature attribute comparison and numerical range calculation, such as calculating the percentage of overlap in the feature dimensions; based on the matching degree, a minimum matching threshold (e.g., 70%) is set to filter related feature items from the feature index set, and frequency domain feature sets of the related feature items are extracted using spectral decomposition techniques, such as obtaining multi-band features through wavelet packet decomposition; the spatial distribution information of the effective attitude set and the frequency domain feature set can be combined through feature dimension alignment and numerical normalization to generate corresponding frequency domain spatial feature vectors, for example, arranging spatial coordinate features and frequency features in dimensional order and standardizing them to the same numerical range.

[0106] This invention generates state description information corresponding to the effective attitude set, which can transform abstract frequency domain spatial feature vectors into intuitive textual descriptions, facilitating the understanding of the flutter-related states reflected by the effective attitude set. The state description information refers to the explanatory content of the state reflected by the effective attitude set, formed through textual expression based on the frequency domain spatial feature vector. Furthermore, based on the frequency domain spatial feature vector, the state description information corresponding to the effective attitude set is generated. For example, if the frequency domain spatial feature vector shows that 8Hz is the dominant vibration frequency and the spatial distribution deviation in the Y-axis direction is large, the state description information can be expressed as "There is flutter dominated by 8Hz in the effective attitude set, the attitude deviation in the Y-axis direction is significantly distributed in space, and the overall attitude stability is weak."

[0107] S3. The effective vibration sequence is segmented to obtain a segmented vibration sequence. The segment deviation measure corresponding to the segmented vibration sequence is calculated to adjust the effective vibration sequence to obtain an adjusted vibration sequence.

[0108] This invention obtains a segmented vibration sequence by segmenting an effective vibration sequence, which can initially divide continuous vibration data into multiple analysis units; and by calculating the segment deviation measure corresponding to the segmented vibration sequence, the internal consistency of each segment signal and the rationality of the segment boundary can be quantified, thereby supporting the improvement of the segmentation process.

[0109] Furthermore, the effective vibration sequence can be segmented and split using a signal segmenter to obtain a segmented vibration sequence. The signal segmenter refers to a pre-defined method or tool for dividing the vibration signal into intervals according to specific conditions or characteristics. It adaptively segments the signal based on its own characteristics, such as amplitude variation, zero crossings, and energy distribution. For example, for a turntable vibration signal, the signal segmenter can divide the signal into continuous segments representing different vibration states based on the extreme points of the signal envelope or the abrupt change points of the root mean square value. The signal segmenter can be constructed using traditional signal processing methods based on sliding windows and threshold discrimination.

[0110] As an embodiment of the present invention, the calculation of the segmented deviation measure corresponding to the split vibration sequence includes:

[0111] The split vibration sequence is subjected to time-frequency feature mapping to obtain a time-frequency distribution set;

[0112] Based on the aforementioned time-frequency distribution set, analyze the time-frequency collaborative characteristics corresponding to the split vibration sequence;

[0113] Based on the aforementioned time-frequency co-occurrence characteristics, the split vibration sequence is classified into stationary segments and non-stationary segments.

[0114] The number of segments corresponding to the stationary signal segments and the non-stationary signal segments is counted to obtain the number of stationary segments and the number of non-stationary segments.

[0115] By combining the number of stationary segments, the time-frequency co-occurrence characteristics, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated.

[0116] The time-frequency distribution set is the energy distribution representation of each segment in the time-frequency domain obtained after the split vibration sequence is mapped by time-frequency features, such as obtaining the spectrum of each signal segment through short-time Fourier transform; the time-frequency co-operation feature is the degree of uniformity and stability of the time-frequency distribution within each segment of the split vibration sequence; the stationary signal segment and the non-stationary signal segment are vibration signal segments that conform to or do not conform to the stationarity assumption after the split vibration sequence is classified based on the time-frequency co-operation feature. For example, a vibration with relatively constant amplitude and frequency belongs to the stationary signal segment, while a vibration containing impact or frequency abrupt change belongs to the non-stationary signal segment.

[0117] Furthermore, time-frequency analysis tools (such as short-time Fourier transform and wavelet transform) can be used to perform time-frequency feature mapping on the split vibration sequence to obtain a time-frequency distribution set. For example, wavelet transform can be used to analyze the scale map of each signal segment to obtain its joint distribution in time and frequency. Based on the time-frequency distribution set, statistical feature extraction methods are used to analyze the time-frequency co-function characteristics corresponding to the split vibration sequence, such as calculating the variance or entropy of the spectrum of each segment to measure the stability of its frequency components. Based on the time-frequency co-function characteristics, clustering methods are used to classify the split vibration sequence by stationarity to obtain stationary signal segments and non-stationary signal segments.

[0118] Furthermore, as an optional embodiment of the present invention, the segmentation deviation measure corresponding to the split vibration sequence is calculated by combining the number of stationary segments, the time-frequency co-occurrence feature, and the number of non-stationary segments, including:

[0119] Based on the aforementioned time-frequency co-operational characteristics, the stationarity index corresponding to each signal segment in the split vibration sequence is calculated;

[0120] By combining the number of stationary segments and the number of non-stationary segments, the stationarity ratio corresponding to the split vibration sequence is calculated;

[0121] Based on the stationarity ratio, set the stationarity weight factor corresponding to the split vibration sequence;

[0122] By combining the stationarity weight factor, the stationarity index, the number of stationary segments, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated.

[0123] The stationarity index is a metric that quantifies whether the statistical characteristics of a vibration signal remain stable over time; the stationarity ratio is the proportion of stationary segments to non-stationary segments in the segmented vibration sequence, used to reflect the overall stationarity distribution of the segmentation results; the stationarity weighting factor is an adjustment coefficient set to balance the difference in the number of stationary and non-stationary segments and highlight the influence of non-stationary segments; furthermore, based on the stationarity ratio, the stationarity weighting factor can be set by a logarithmic function or a linear scaling method.

[0124] Furthermore, as another embodiment of the present invention, the segmentation deviation measure corresponding to the split vibration sequence is calculated using the following formula, combining the stationarity weight factor, the stationarity index, the number of stationary segments, and the number of non-stationary segments:

[0125]

[0126] Where B represents the segmented deviation measure corresponding to the split vibration sequence. Indicates a stationary weighting factor. This represents the stationarity index corresponding to the j-th signal segment in the split vibration sequence. Indicates the number of stationary segments. denoted by j, which represents the number of non-stationary segments, and m represents the number of signal segments in the split vibration sequence.

[0127] Furthermore, as an optional embodiment of the present invention, the step of calculating the stationarity index corresponding to each signal segment in the split vibration sequence based on the time-frequency co-operational characteristics includes:

[0128] The time-frequency co-operation features are processed by signal component extraction to obtain the frequency-time domain correlated composition;

[0129] The frequency-time domain correlation components are enhanced to obtain enhanced coherence features;

[0130] The enhanced coherence features are subjected to robust constraint processing to obtain a constrained feature set;

[0131] The constraint feature set is subjected to multidimensional aggregation processing to obtain aggregated constraint features;

[0132] Analyze the scale attribute of the aggregation constraint feature and calculate the attribute contribution corresponding to the scale attribute;

[0133] By combining the attribute contribution and the aggregation constraint features, the stationarity index corresponding to each signal segment in the split vibration sequence is calculated.

[0134] Wherein, the frequency-time domain correlation component is the set of frequency and time components obtained after decomposing the time-frequency coherent features; the enhanced coherence feature is the output of the frequency-time domain correlation component after coherence enhancement processing; the constraint feature set is the feature set of the enhanced coherence feature after robustness optimization; the aggregated constraint feature is the result of the constraint feature set fused and converged in multiple dimensions; the scale attribute is the signal characteristics presented by the aggregated constraint feature at different resolution levels; and the attribute contribution degree represents the degree of influence of each component determined according to the scale attribute on the stationarity measure.

[0135] Furthermore, the time-frequency coherent features can be processed by intrinsic mode decomposition to extract signal components, resulting in a frequency-time domain correlated composition. This frequency-time domain correlated composition can then be enhanced using coherence function calculation or spectral coherence analysis to obtain enhanced coherence features. These enhanced coherence features can be robustly constrained using regularization constraints or sparse coding to obtain a constrained feature set. The constrained feature set can be multidimensionally aggregated using wavelet packet analysis or hierarchical feature fusion to obtain aggregated constrained features. The scale attribute of the aggregated constrained features can be analyzed using variance analysis or entropy calculation. The attribute contribution degree corresponding to the scale attribute can be calculated using principal component contribution rate calculation or entropy weighting. Finally, combining the attribute contribution degree and the aggregated constrained features, a stationarity index corresponding to each signal segment in the split vibration sequence can be obtained through weighted fusion or regression calculation.

[0136] This invention improves the accuracy of vibration signal segmentation by dividing and adjusting the effective vibration sequence based on the segmentation deviation measure, thereby making the segmentation boundaries more closely match the actual changes in the vibration state and reducing the impact of missegmentation and omission. The adjusted vibration sequence is an optimized segmentation result obtained by iteratively adjusting the segmentation position and number based on the segmentation deviation measure of the effective vibration sequence to minimize the deviation of the segmentation result (i.e., consistent vibration characteristics within segments and significant differences in vibration characteristics between segments). Further details can be found below. Figure 2This diagram illustrates the vibration sequence splitting and adjustment process in the online turntable flutter control method based on multi-sensor fusion provided by the present invention. This process is based on segmented deviation measurement. On one hand, it performs iterative updates of the vibration segmenter's parameters (adjusting the vibration feature weight coefficients in the segmentation algorithm based on the deviation feedback of each segmentation round). On the other hand, it dynamically calibrates the segmentation interval threshold (combining the turntable's rated vibration range to correct the upper and lower limits of the segment length in real time). Simultaneously, it performs cross-dimensional deviation fusion (incorporating time-domain vibration deviation and frequency-domain flutter deviation into a unified calculation framework to improve the comprehensiveness of deviation assessment). Then, the effective vibration sequence is re-splitted using the vibration segmenter after the above operations. If the segmentation deviation still exceeds the preset allowable range, the parameter update, threshold calibration, and deviation fusion steps are repeated until the deviation of the segmentation results meets the requirements. Finally, an adjusted vibration sequence is generated. Through multi-path optimization (parameter update, threshold calibration, and cross-dimensional fusion), the vibration sequence splitting achieves precise adaptation to the changes in the turntable flutter state, ensuring that each adjusted vibration sequence accurately corresponds to the different stages of turntable flutter characteristics.

[0137] S4. Combining the state description information with the adjusted vibration sequence, generate flutter characterization data of the turntable system, extract flutter characterization factors from the flutter characterization data, calculate the coupling correlation between the flutter characterization factors, and determine the dominant flutter characterization factor among the flutter characterization factors.

[0138] This invention generates flutter characterization data of the turntable system by combining the state description information with the adjusted vibration sequence, thereby obtaining a comprehensive data expression of the flutter state of the turntable system. By extracting the flutter characterization factors from the flutter characterization data, the key physical quantity identifiers of the flutter state can be obtained, providing a basis for the subsequent calculation and processing of the coupling correlation between the flutter characterization factors.

[0139] The flutter characterization data is a dataset that comprehensively describes the flutter state of the turntable, obtained by fusing the state description information and the adjusted vibration sequence. The flutter index is a key physical quantity extracted from the flutter characterization data that characterizes specific flutter properties. Furthermore, by combining the state description information and the adjusted vibration sequence, flutter characterization data corresponding to the turntable system can be generated through data fusion and feature alignment methods. If the state description information is "the turntable exhibits weak high-frequency vibration at 15Hz, accompanied by 37Hz intermediate-frequency modulation," and the adjusted vibration sequence is a time-domain vibration signal that has been segmented and optimized, then through data fusion and feature alignment, the state description and the precise signal are integrated to generate flutter characterization data containing specific frequency components and their amplitude and phase. Flutter indexes in the flutter characterization data can be extracted using physical quantity extraction methods, such as using peak detection and frequency estimation techniques to extract vibration amplitude, dominant frequency, and frequency band energy.

[0140] This invention calculates the coupling correlation degree between the flutter indices to understand the interaction strength between them, providing a quantitative basis for determining the flutter characterization factors in the flutter indices. The coupling correlation degree represents the degree of mutual influence and coordinated change trend of the flutter indices under the dynamic characteristics of the turntable system.

[0141] As an embodiment of the present invention, the calculation of the coupling correlation between the flutter indices includes:

[0142] Calculate the interaction strength between the flutter indices and determine the amplitude mapping value corresponding to the flutter indices;

[0143] The coupling correlation between the flutter indices is calculated by combining the interaction strength and the amplitude mapping value.

[0144] Wherein, the interaction strength represents the degree of influence between the flutter indices (i.e., the degree to which a change in one index causes a change in another index); the amplitude mapping value represents the physical quantity value (such as vibration amplitude, energy value) corresponding to the flutter indices; furthermore, the interaction strength between the flutter indices can be calculated by transfer function analysis or coherence function, such as by calculating the coherence coefficient of two vibration measurement point signals in a specific frequency band to obtain their interaction strength; the amplitude mapping value corresponding to the flutter indices can be obtained by direct reading or normalization processing, such as converting the vibration acceleration amplitude from physical units (m / s²) to per-unit values.

[0145] Furthermore, as another embodiment of the present invention, the coupling correlation between the flutter indices is calculated using the following formula, combining the interaction strength and the amplitude mapping value:

[0146]

[0147] Where F represents the coupling correlation between flutter indices, This represents the interaction strength between the p-th flutter index and the (p+1)-th flutter index. This represents the p-th flutter index. This represents the (p+1)th flutter index. This represents the amplitude mapping value of the p-th flutter index. This represents the amplitude mapping value of the (p+1)th flutter index, where p represents the sequence number of the flutter index and Q represents the number of flutter indices.

[0148] This invention determines the dominant flutter characterization factor among the flutter indices based on the coupling correlation degree, thereby screening out the key indicators that have the greatest impact on the flutter state of the system and are most representative, thus improving the efficiency and accuracy of subsequent flutter analysis and control. The flutter characterization factor is a physical quantity that plays a dominant role in the overall flutter behavior of the system, selected from the flutter indices by the coupling correlation degree. Furthermore, when the coupling correlation degree is greater than a preset correlation degree, the flutter characterization factor among the flutter indices is determined. The preset correlation degree can be set according to the actual system characteristics and accuracy requirements.

[0149] S5. Based on the dominant flutter characterization factor, determine the flutter source of the turntable system, and combine the flutter source with the coupling correlation degree to perform turntable flutter suppression processing on the turntable system to obtain the processing result.

[0150] This invention determines the source of chatter in the turntable system based on the dominant chatter characterization factor, thereby locating the root cause of the chatter and providing a clear objective for subsequent targeted chatter suppression treatment. This facilitates improving the accuracy of online chatter control in the turntable system. The source of chatter is the specific physical component, structure, or external excitation in the turntable system that causes or significantly affects chatter.

[0151] As an embodiment of the present invention, determining the flutter source of the turntable system based on the dominant flutter characterization factor includes:

[0152] The physical meaning of the dominant flutter characterization factor is analyzed, and based on the physical meaning, the characteristic correlation topology corresponding to the dominant flutter characterization factor is constructed.

[0153] Fault derivation processing is performed on the aforementioned characteristic-related topology to obtain a hierarchical derivation structure;

[0154] Extract the dominant source factors from the hierarchical derivation structure, trace the source of the dominant source factors, and obtain the factor tracing results;

[0155] Calculate the influence factor of the dominant source factor in the hierarchical derivation structure;

[0156] Based on the aforementioned influencing factors, the results of the factor tracing are verified to obtain the flutter source corresponding to the turntable system.

[0157] Wherein, the characteristic association topology is the physical characteristic association topology structure corresponding to the dominant flutter characterization factor; the hierarchical derivation structure is the hierarchical causal structure of the characteristic association topology after fault tree derivation; the source dominant factor is the core physical factor in the hierarchical derivation structure; the factor tracing result is the preliminary source location obtained by tracing the source of the core structural factor; and the influence factor is the quantitative value of the degree of influence of the source dominant factor on flutter formation in the hierarchical derivation structure.

[0158] Furthermore, the physical meaning of the dominant flutter characterization factor can be analyzed through physical meaning interpretation; based on the physical meaning, the characteristic correlation topology corresponding to the dominant flutter characterization factor can be constructed through system structural relationships; the characteristic correlation topology can be deduced using the fault tree analysis (FTA) method to obtain a hierarchical derivation structure; the source dominant factor in the hierarchical derivation structure can be extracted using the key factor identification method; the source of the core structural factor can be traced using the causal tracing method to obtain the factor tracing result, such as tracing the high-frequency vibration to "main shaft bearing wear" or "poor drive gear meshing" by combining mode shape test results and structural modal analysis; when the influence factor is lower than a preset threshold, the factor tracing result is verified to obtain the flutter source corresponding to the turntable system.

[0159] Furthermore, as an optional embodiment of the present invention, calculating the influence factor of the source dominant factor in the hierarchical derivation structure includes:

[0160] Calculate the structural importance of the source dominant factor in the hierarchical derivation structure;

[0161] The topological depth of the source-dominant factors in the hierarchical derivation structure is statistically analyzed.

[0162] Combining the structural importance and the topological depth, the influence factor of the source dominance factor in the hierarchical derivation structure is calculated using the following formula:

[0163]

[0164] Where I represents the influence factor of the source dominant factor in the hierarchical derivation structure, S represents the structural importance of the factors among the source dominant factors, R represents the physical correlation strength of the factors among the source dominant factors, D represents the topological depth of the factors among the source dominant factors, and k represents the depth decay coefficient.

[0165] Wherein, the structural importance indicates the degree of importance of the source dominant factor in the occurrence of the top-level event (flutter) in the hierarchical derivation structure; the topological depth indicates the hierarchical position of the source dominant factor in the hierarchical derivation structure; the physical correlation strength indicates the strength of the physical correlation between the source dominant factor and the flutter phenomenon; and the depth attenuation coefficient indicates a quantification factor in which the influence of the factor decreases with increasing topological depth.

[0166] Furthermore, the structural importance of the dominant source factor in the hierarchical derivation structure can be calculated through fault tree minimum cut set analysis; the topological depth of the dominant source factor in the hierarchical derivation structure can be calculated by counting the number of path edges from the factor node to the top event node; the physical correlation strength can be obtained through physical simulation, correlation analysis of experimental data, or expert experience evaluation; and the depth decay coefficient can be obtained by preset empirical values ​​or training based on historical fault data.

[0167] This invention combines the sources of chatter with the coupling correlation to perform chatter suppression processing on the turntable system, obtaining processing results and thus achieving effective control of turntable chatter, improving the stability of turntable operation. Furthermore, by combining the sources of chatter with the coupling correlation, the invention performs chatter suppression processing on the turntable system, first identifying the root cause of the problem (such as "bearing wear" or "mass imbalance"), then determining the primary and secondary impacts of each source based on the coupling correlation; finally, generating and executing corresponding suppression operation commands based on the source type and degree of impact, for example, "for spindle bearing wear (primary source, coupling correlation 0.95), execute speed reduction operation and trigger an early warning; for slight mass imbalance (secondary source, coupling correlation 0.6), output dynamic balance correction weights and phase suggestions," ultimately obtaining a result where system chatter is reduced or eliminated.

[0168] Compared to the problems described in the background art, this invention, by performing signal separation processing on the multi-source sensor data, can decompose the mixed raw sensor data into independent signal components characterizing different physical phenomena, providing clear data objects for subsequent signal quality evaluation and effectively improving the accuracy of turntable system state perception. Furthermore, by identifying flutter-related variables in the effective attitude set, this invention can locate key variables related to flutter phenomena from the attitude data, making subsequent analysis more focused on core influencing factors and facilitating the capture of flutter-related attitude change characteristics. Moreover, by segmenting the effective vibration sequence to obtain a segmented vibration sequence, this invention can initially divide continuous vibration data into multiple analysis units; and by calculating the segmentation deviation measure corresponding to the segmented vibration sequence, it can quantify each segment... The internal consistency of the segmented signals and the rationality of the segmentation boundaries support improved processing of the segmentation process. Furthermore, by combining the state description information with the adjusted vibration sequence, this invention generates flutter characterization data for the turntable system, thereby obtaining a comprehensive data expression of the turntable system's flutter state. By extracting flutter characterization factors from the flutter characterization data, key physical quantity identifiers of the flutter state can be obtained, providing a basis for subsequent calculation of the coupling correlation between the flutter characterization factors. Finally, this invention determines the flutter source of the turntable system based on the dominant flutter characterization factors, thereby locating the root cause of turntable flutter and providing a clear target for subsequent targeted flutter suppression processing, thus facilitating improvements in the accuracy of online flutter control of the turntable system. Therefore, the turntable online flutter control method based on multi-sensor fusion provided by this invention can improve the accuracy of online turntable flutter control.

[0169] like Figure 3 The diagram shown is a functional block diagram of an online flutter control system for a turntable based on multi-sensor fusion according to the present invention.

[0170] The online turntable flutter control system 300 based on multi-sensor fusion described in this invention can be installed in an electronic device. Depending on the functions implemented, the online turntable flutter control system based on multi-sensor fusion may include a sensor data processing module 301, an attitude feature extraction module 302, a vibration sequence adjustment module 303, a dominant factor determination module 304, and a flutter suppression processing module 305. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0171] In this embodiment of the invention, the functions of each module / unit are as follows:

[0172] The sensor data processing module 301 is used to receive multi-source sensor data from the turntable system, perform signal separation processing on the multi-source sensor data to obtain a vibration signal sequence and an attitude signal set, and perform signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set.

[0173] The attitude feature extraction module 302 is used to identify flutter-related variables in the effective attitude set, and extract frequency domain spatial feature vectors from the effective attitude set by combining a preset flutter feature library with the flutter-related variables, so as to generate state description information corresponding to the effective attitude set.

[0174] The vibration sequence adjustment module 303 is used to segment the effective vibration sequence to obtain a segmented vibration sequence, calculate the segment deviation measure corresponding to the segmented vibration sequence, and adjust the effective vibration sequence to obtain an adjusted vibration sequence.

[0175] The dominant factor determination module 304 is used to combine the state description information and the adjusted vibration sequence to generate flutter characterization data of the turntable system, extract flutter characterization factors from the flutter characterization data, calculate the coupling correlation degree between the flutter characterization factors, and determine the dominant flutter characterization factor among the flutter characterization factors.

[0176] The flutter suppression processing module 305 is used to determine the flutter source of the turntable system based on the dominant flutter characterization factor, and perform turntable flutter suppression processing on the turntable system in combination with the flutter source and the coupling correlation degree to obtain the processing result.

[0177] In detail, the modules in the turntable online flutter control system 300 based on multi-sensor fusion described in this embodiment of the invention employ the same methods as described above. Figure 1 The method used is the same as the online flutter control method for turntables based on multi-sensor fusion described in the article, and can produce the same technical effect, so it will not be elaborated here.

[0178] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0179] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for online flutter control of a turntable based on multi-sensor fusion, characterized in that, The method includes: Receive multi-source sensor data from the turntable system, perform signal separation processing on the multi-source sensor data to obtain a vibration signal sequence and an attitude signal set, and perform signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set. Identify the flutter-related variables in the effective attitude set, and extract the frequency domain spatial feature vector from the effective attitude set by combining the preset flutter feature library with the flutter-related variables to generate the state description information corresponding to the effective attitude set. The effective vibration sequence is segmented to obtain a segmented vibration sequence. The segment deviation measure corresponding to the segmented vibration sequence is calculated to adjust the effective vibration sequence and obtain an adjusted vibration sequence. By combining the state description information with the adjusted vibration sequence, flutter characterization data of the turntable system is generated. Flutter characterization factors are extracted from the flutter characterization data, and the coupling correlation between the flutter characterization factors is calculated to determine the dominant flutter characterization factor among the flutter characterization factors. Based on the dominant flutter characterization factor, the flutter source of the turntable system is determined. Combining the flutter source with the coupling correlation, turntable flutter suppression processing is performed on the turntable system to obtain the processing result.

2. The online flutter control method for turntables based on multi-sensor fusion as described in claim 1, characterized in that, The step of performing signal separation processing on the multi-source sensing data to obtain a vibration signal sequence and an attitude signal set includes: The multi-source sensing data is subjected to frequency domain feature decomposition to obtain high-frequency components and low-frequency components; The high-frequency components are subjected to vibration enhancement processing to obtain an enhanced vibration signal; Attitude extraction is performed on the low-frequency components to obtain a preliminary attitude signal; The system characteristics of the turntable system are analyzed, and based on these characteristics, the enhanced vibration signal and the preliminary attitude signal are subjected to component harmonic processing to obtain a vibration signal sequence and an attitude signal set.

3. The online flutter control method for turntables based on multi-sensor fusion as described in claim 1, characterized in that, The step of performing signal quality evaluation on the vibration signal sequence and the attitude signal set to obtain an effective vibration sequence and an effective attitude set includes: The integrity of the vibration signal sequence is checked to obtain candidate vibration sequences; The candidate vibration sequences are subjected to signal-to-noise ratio evaluation to obtain reliable vibration sequences; Outlier removal is performed on the reliable vibration sequence to obtain the valid vibration sequence; Based on the effective vibration sequence, the attitude signal set is subjected to a matching verification process to obtain an effective attitude set.

4. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 1, characterized in that, The step of combining a preset flutter feature library with the flutter-related variables to extract frequency domain spatial feature vectors from the effective attitude set includes: Extract the flutter-related variables from the set of effective attitudes; The flutter feature library is structured to obtain a feature index set; Analyze the matching degree between the flutter-related variables and the feature index set; Based on the matching degree, the associated feature terms corresponding to the flutter associated variables are selected from the feature index set, and the frequency domain feature set of the associated feature terms is extracted; By combining the spatial distribution information of the effective attitude set with the frequency domain feature set, a corresponding frequency domain spatial feature vector is generated.

5. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 1, characterized in that, The calculation of the segmented deviation measure corresponding to the split vibration sequence includes: The split vibration sequence is subjected to time-frequency feature mapping to obtain a time-frequency distribution set; Based on the aforementioned time-frequency distribution set, analyze the time-frequency collaborative characteristics corresponding to the split vibration sequence; Based on the aforementioned time-frequency co-occurrence characteristics, the split vibration sequence is classified into stationary segments and non-stationary segments. The number of segments corresponding to the stationary signal segments and the non-stationary signal segments is counted to obtain the number of stationary segments and the number of non-stationary segments. By combining the number of stationary segments, the time-frequency co-occurrence characteristics, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated.

6. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 5, characterized in that, Combining the number of stationary segments, the time-frequency co-occurrence feature, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated, including: Based on the aforementioned time-frequency co-operational characteristics, the stationarity index corresponding to each signal segment in the split vibration sequence is calculated; By combining the number of stationary segments and the number of non-stationary segments, the stationarity ratio corresponding to the split vibration sequence is calculated; Based on the stationarity ratio, set the stationarity weight factor corresponding to the split vibration sequence; Combining the stationarity weighting factor, the stationarity index, the number of stationary segments, and the number of non-stationary segments, the segmentation deviation measure corresponding to the split vibration sequence is calculated using the following formula: ； Where B represents the segmented deviation measure corresponding to the split vibration sequence. Indicates a stationary weighting factor. This represents the stationarity index corresponding to the j-th signal segment in the split vibration sequence. Indicates the number of stationary segments. denoted by j, which represents the number of non-stationary segments, and m represents the number of signal segments in the split vibration sequence.

7. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 6, characterized in that, The calculation of the stationarity index corresponding to each signal segment in the split vibration sequence based on the time-frequency co-occurrence characteristics includes: The time-frequency co-operation features are processed by signal component extraction to obtain the frequency-time domain correlated composition; The frequency-time domain correlation components are enhanced to obtain enhanced coherence features; The enhanced coherence features are subjected to robust constraint processing to obtain a constrained feature set; The constraint feature set is subjected to multidimensional aggregation processing to obtain aggregated constraint features; Analyze the scale attribute of the aggregation constraint feature and calculate the attribute contribution corresponding to the scale attribute; By combining the attribute contribution and the aggregation constraint features, the stationarity index corresponding to each signal segment in the split vibration sequence is calculated.

8. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 1, characterized in that, The calculation of the coupling correlation between the flutter indices includes: Calculate the interaction strength between the flutter indices and determine the amplitude mapping value corresponding to the flutter indices; Combining the interaction strength and the amplitude mapping value, the coupling correlation between the flutter indices is calculated using the following formula: ； Where F represents the coupling correlation between flutter indices, This represents the interaction strength between the p-th flutter index and the (p+1)-th flutter index. This represents the p-th flutter index. This represents the (p+1)th flutter index. This represents the amplitude mapping value of the p-th flutter index. This represents the amplitude mapping value of the (p+1)th flutter index, where p represents the sequence number of the flutter index and Q represents the number of flutter indices.

9. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 1, characterized in that, The step of determining the flutter source of the turntable system based on the dominant flutter characterization factor includes: The physical meaning of the dominant flutter characterization factor is analyzed, and based on the physical meaning, the characteristic correlation topology corresponding to the dominant flutter characterization factor is constructed. Fault derivation processing is performed on the aforementioned characteristic-related topology to obtain a hierarchical derivation structure; Extract the dominant source factors from the hierarchical derivation structure, trace the source of the dominant source factors, and obtain the factor tracing results; Calculate the influence factor of the dominant source factor in the hierarchical derivation structure; Based on the aforementioned influencing factors, the results of the factor tracing are verified to obtain the flutter source corresponding to the turntable system.

10. The online flutter control method for a turntable based on multi-sensor fusion as described in claim 9, characterized in that, The calculation of the influence factor of the dominant source factor in the hierarchical derivation structure includes: Calculate the structural importance of the source dominant factor in the hierarchical derivation structure; The topological depth of the source-dominant factors in the hierarchical derivation structure is statistically analyzed. Combining the structural importance and the topological depth, the influence factor of the source dominance factor in the hierarchical derivation structure is calculated using the following formula: ； Where I represents the influence factor of the source dominant factor in the hierarchical derivation structure, S represents the structural importance of the factors among the source dominant factors, R represents the physical correlation strength of the factors among the source dominant factors, D represents the topological depth of the factors among the source dominant factors, and k represents the depth decay coefficient.

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