Intelligent operation and maintenance management method and system for CNC machine tools

Abstract

The present application provides a method and system for intelligent operation and maintenance management of CNC machine tools, which relates to the field of intelligent equipment management. It uses artificial intelligence-based data analysis and processing technology to perform timing analysis on the time queue of vibration amplitude values, the time queue of current values, and the time queue of voltage values. In this way, the motor state detection result is intelligently obtained based on the characteristics obtained by timing association of current and voltage and the timing synergistic response characteristics between the timing characteristics of vibration amplitude, which can capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. It can also automatically extract features from the data and identify local timing synergy patterns between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance.

Description

Technical Field

[0001] The present application relates to the field of intelligent equipment management, and more specifically, to an intelligent operation and maintenance management method and system for CNC machine tools. Background Art

[0002] CNC machine tools are key equipment in modern manufacturing and are widely used in many industries such as automobile manufacturing, aerospace, and precision instruments. With the continuous improvement of the level of industrial automation, CNC machine tools play a vital role in improving production efficiency and ensuring processing accuracy. Since CNC machine tools play an important role in the production process, their operating status is directly related to product quality and production efficiency. Therefore, effective operation and maintenance management of CNC machine tools has become the key to ensure smooth production.

[0003] However, traditional CNC machine tool operation and maintenance management systems may only rely on a limited number of sensors or manual input for data collection, which limits the dimension and depth of the data and is insufficient to fully reflect the actual operating status of the machine tool motor. For example, traditional systems may only monitor basic operating parameters such as speed and temperature, while ignoring more subtle vibrations, sounds, or current fluctuations, which may contain early signals of faults. In addition, traditional systems may only use simple threshold judgments or statistical analysis methods in data analysis methods. Although these methods are easy to implement, they often lack the ability to identify deep patterns and anomalies between local time series of data, making it difficult to capture complex equipment failure modes and early warning signals. For example, the initial wear of the motor may not immediately cause a significant temperature rise or speed drop, but if local small vibration changes or current fluctuations are monitored, these problems may be discovered at an early stage.

[0004] Therefore, an optimized CNC machine tool operation and maintenance management solution is needed to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method and system for intelligent operation and maintenance management of CNC machine tools, which uses artificial intelligence-based data analysis and processing technology to perform timing analysis on the time queue of vibration amplitude values, the time queue of current values, and the time queue of voltage values, so as to intelligently obtain the motor state detection result based on the characteristics obtained by timing correlation of current and voltage and the timing coordinated response characteristics between the vibration amplitude timing characteristics, and can capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. And it can automatically extract features from the data and identify local timing coordination patterns between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance.

[0006] According to one aspect of the present application, there is provided a CNC machine tool intelligent operation and maintenance management system, which includes:

[0007] The data acquisition layer includes a data acquisition module and a data transmission module, wherein the data acquisition module is used to receive a time queue of motor state parameters collected by a sensor group of a motor deployed on a CNC machine tool, wherein the motor state parameters include vibration amplitude values, current values, and voltage values; and the data transmission module is used to transmit the time queue of the motor state parameters to the data processing layer;

[0008] The data processing layer is used to group the time queue of the motor state parameters according to the parameter sample dimension to obtain a time queue of vibration amplitude values, a time queue of current values, and a time queue of voltage values;

[0009] The data analysis layer is used to perform feature extraction and correlation analysis on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value to obtain a motor state detection result, and the motor state detection result is used to indicate whether the motor state is abnormal;

[0010] The application layer is used to generate an alarm notification in response to the motor state detection result indicating that the motor state is abnormal.

[0011] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the data analysis layer includes: a motor state parameter timing encoding module, which is used to perform timing sequence encoding on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value respectively to obtain the vibration amplitude timing correlation feature vector, the current timing correlation feature vector and the voltage timing correlation feature vector; a motor working power factor timing correlation representation generation module, which is used to input the current timing correlation feature vector and the voltage timing correlation feature vector into the motor working power timing analysis module to obtain the motor working power factor timing correlation representation vector; a motor power factor-vibration coordinated response analysis module, which is used to perform motor power factor-vibration timing feature coordinated response analysis on the motor working power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector to obtain the motor power factor-vibration coordinated response feature; a motor state detection result generation module, which is used to generate the motor state detection result. Based on the motor power factor-vibration coordinated response feature, the motor state detection result is obtained; wherein the motor power factor-vibration coordinated response analysis module includes: a vibration amplitude time series correlation feature refinement unit, which is used to perform feature refinement on the vibration amplitude time series correlation feature vector to obtain a sequence of vibration amplitude local time series correlation feature vectors; a motor work power factor-vibration correlation unit, which is used to calculate the correlation matrix between the motor work power factor time series correlation representation vector and each vibration amplitude local time series correlation feature vector in the sequence of the vibration amplitude local time series correlation feature vector to obtain a sequence of motor work power factor-vibration correlation matrices; a motor power factor-vibration local-global joint response unit, which is used to perform local and global joint response evaluation on the sequence of the motor work power factor-vibration correlation matrix to obtain a motor power factor-vibration response interactive expression matrix as the motor power factor-vibration coordinated response feature.

[0012] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor state parameter timing encoding module is used to: input the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value into the timing sequence encoder based on the Bi-LSTM model respectively to obtain the vibration amplitude timing association feature vector, the current timing association feature vector and the voltage timing association feature vector.

[0013] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor working power factor-vibration correlation unit includes: a motor working power factor timing correlation representation vector nonlinear transformation subunit, which is used to perform a nonlinear transformation on the motor working power factor timing correlation representation vector to obtain the motor working power factor timing correlation representation vector after the nonlinear transformation; a motor working power factor-vibration correlation matrix sequence calculation subunit, which is used to calculate the correlation matrix between the motor working power factor timing correlation representation vector after the nonlinear transformation and each vibration amplitude local timing correlation feature vector in the sequence of the vibration amplitude local timing correlation feature vector to obtain the sequence of the motor working power factor-vibration correlation matrix.

[0014] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor power factor-vibration local and global joint response unit includes: a motor work power factor-vibration correlation characteristic interaction energy factor calculation subunit, which is used to calculate the characteristic interaction energy factors of each motor work power factor-vibration correlation matrix in the sequence of the motor work power factor-vibration correlation matrix to obtain a sequence of motor work power factor-vibration correlation characteristic interaction energy factors; a motor work power factor-vibration correlation characteristic interaction energy factor normalization subunit, which is used to input the sequence of the motor work power factor-vibration correlation characteristic interaction energy factors into a Sigmoid function to obtain a sequence of normalized motor work power factor-vibration correlation characteristic interaction energy factors; a motor power factor-vibration response interaction expression matrix generation subunit, which is used to use the sequence of normalized motor work power factor-vibration correlation characteristic interaction energy factors as a sequence of weights, and calculate the position-weighted sum of the sequence of the motor work power factor-vibration correlation matrix to obtain the motor power factor-vibration response interaction expression matrix.

[0015] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor working power factor-vibration associated characteristic interaction energy factor calculation subunit includes: a motor working power factor-vibration associated kurtosis value calculation secondary subunit, which is used to calculate the kurtosis value of each motor working power factor-vibration association matrix in the sequence of the motor working power factor-vibration association matrix to obtain a sequence of motor working power factor-vibration associated kurtosis values; a motor working power factor-vibration interaction energy factor calculation secondary subunit, which is used to calculate the characteristic interaction energy factor of each motor working power factor-vibration association matrix based on each motor working power factor-vibration associated kurtosis value in the sequence of the motor working power factor-vibration associated kurtosis values ​​to obtain a sequence of the motor working power factor-vibration associated characteristic interaction energy factors.

[0016] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor working power factor-vibration associated kurtosis value calculation secondary subunit is used to: respectively calculate the mean and variance of the motor working power factor-vibration associated matrix to obtain the motor working power factor-vibration associated mean and the motor working power factor-vibration associated variance; calculate the positional difference between the motor working power factor-vibration associated matrix and the motor working power factor-vibration associated mean to obtain the motor working power factor-vibration associated difference matrix; calculate the fourth power of each eigenvalue in the motor working power factor-vibration associated difference matrix to obtain the motor working power factor-vibration associated modulation difference matrix; calculate the expected value of the motor working power factor-vibration associated modulation difference matrix to obtain the motor working power factor-vibration associated expected value; divide the motor working power factor-vibration associated expected value by the square of the motor working power factor-vibration associated variance to obtain the motor working power factor-vibration associated kurtosis value.

[0017] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor working power factor-vibration interaction energy factor calculation secondary subunit is used to: calculate the sum of the motor working power factor-vibration correlation variance and the hyperparameter to obtain the motor working power factor-vibration correlation first energy factor; calculate the square of the difference between the motor working power factor-vibration correlation kurtosis value and the motor working power factor-vibration correlation mean to obtain the motor working power factor-vibration correlation difference value; add the value obtained by multiplying the motor working power factor-vibration correlation variance by constant two and the value obtained by multiplying the hyperparameter by constant two to the motor working power factor-vibration correlation difference value to obtain the motor working power factor-vibration correlation second energy factor; divide the motor working power factor-vibration correlation first energy factor by the motor working power factor-vibration correlation second energy factor to obtain the motor working power factor-vibration correlation characteristic interaction energy factor corresponding to the motor working power factor-vibration correlation matrix.

[0018] In the above-mentioned intelligent operation and maintenance management system for CNC machine tools, the motor state detection result generation module is used to: input the motor power factor-vibration response interaction expression matrix into the classifier-based motor state detection module to obtain the motor state detection result.

[0019] According to another aspect of the present application, there is provided a method for intelligent operation and maintenance management of a CNC machine tool, comprising:

[0020] A time queue for collecting motor state parameters by a sensor group of a motor deployed on a CNC machine tool, wherein the motor state parameters include vibration amplitude value, current value and voltage value;

[0021] The time queue of the motor state parameters is grouped according to the parameter sample dimension to obtain a time queue of vibration amplitude values, a time queue of current values, and a time queue of voltage values;

[0022] Performing feature extraction and correlation analysis on the time queue of the vibration amplitude value, the time queue of the current value, and the time queue of the voltage value to obtain a motor state detection result, wherein the motor state detection result is used to indicate whether the motor state is abnormal;

[0023] In response to the motor state detection result indicating that the motor state is abnormal, an alarm notification is generated.

[0024] The present application has at least the following technical effects: Compared with the prior art, the CNC machine tool intelligent operation and maintenance management method and system provided by the present application adopts artificial intelligence-based data analysis and processing technology to perform timing analysis on the time queue of vibration amplitude value, the time queue of current value and the time queue of voltage value, so as to intelligently obtain the motor state detection result based on the characteristics obtained by timing association of current and voltage and the timing synergistic response characteristics between the timing characteristics of vibration amplitude, and can capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. And it can automatically extract features from the data and identify the local timing synergy mode between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0026] Figure 1 It is a block diagram of the intelligent operation and maintenance management system of CNC machine tools according to an embodiment of the present application.

[0027] Figure 2 It is a block diagram of the data analysis layer in the intelligent operation and maintenance management system of CNC machine tools according to an embodiment of the present application.

[0028] Figure 3 This is a schematic diagram of data flow in the data analysis layer in the intelligent operation and maintenance management system for CNC machine tools according to an embodiment of the present application.

[0029] Figure 4 It is a block diagram of the motor dynamic factor-vibration coordinated response analysis module in the intelligent operation and maintenance management system of CNC machine tools according to an embodiment of the present application.

[0030] Figure 5 It is a block diagram of the motor working power factor-vibration correlation unit in the intelligent operation and maintenance management system of CNC machine tools according to an embodiment of the present application.

[0031] Figure 6 It is a block diagram of the motor dynamic factor-vibration local-global joint response unit in the intelligent operation and maintenance management system of CNC machine tools according to an embodiment of the present application.

[0032] Figure 7 It is a flow chart of the intelligent operation and maintenance management method of CNC machine tools according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0034] As the core equipment of modern manufacturing industry, CNC machine tools play a vital role in many fields such as automobile manufacturing, aerospace and precision instrument manufacturing. With the development of industrial automation, CNC machine tools are of great significance to improving production efficiency and ensuring processing accuracy. Their operating status will affect product quality and production efficiency. Effective operation and maintenance management is critical to ensure smooth production.

[0035] However, traditional CNC machine tool operation and maintenance management systems have limitations in data collection. They usually rely on limited sensor data or manual input, which limits the comprehensive understanding of the machine tool's operating status. For example, these systems may only monitor basic parameters such as speed and temperature, while ignoring more subtle indicators such as vibration, sound, and current fluctuations, which may indicate early signs of failure. In addition, traditional systems may rely too much on simple threshold judgments or statistical analysis in data analysis, and lack the ability to identify deep patterns and anomalies in the data, which limits the capture of complex equipment failure modes and early warning signals. For example, the initial wear of the motor may not immediately lead to a significant temperature increase or speed drop, but if small vibration changes or current fluctuations can be monitored, it is possible to detect and solve the problem at an early stage.

[0036] Based on this, the present application proposes an intelligent operation and maintenance management system for CNC machine tools. Figure 1 FIG. 1 is a block diagram of an intelligent operation and maintenance management system for CNC machine tools according to an embodiment of the present application. Figure 1As shown, in the intelligent operation and maintenance management system 100 of CNC machine tools, it includes: a data acquisition layer 110, a data processing layer 120, a data analysis layer 130 and an application layer 140; the data acquisition layer 110 includes a data acquisition module and a data transmission module, the data acquisition module is used to receive the time queue of motor state parameters collected by the sensor group of the motor deployed on the CNC machine tool, wherein the motor state parameters include vibration amplitude value, current value and voltage value; the data transmission module is used to transmit the time queue of the motor state parameters to the data processing layer; the data processing layer 120 is used to group the time queue of the motor state parameters according to the parameter sample dimension to obtain the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value; the data analysis layer 130 is used to extract features and perform correlation analysis on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value to obtain the motor state detection result, and the motor state detection result is used to indicate whether the motor state is abnormal; the application layer 140 is used to generate an alarm notification in response to the motor state detection result indicating that the motor state is abnormal.

[0037] In an embodiment of the present application, the data acquisition module in the data acquisition layer 110 is used to receive a time queue of motor state parameters collected by a sensor group of a motor deployed in a CNC machine tool, wherein the motor state parameters include vibration amplitude value, current value and voltage value. Among them, vibration is an important indicator of the operating state of mechanical equipment. When the machine tool is operating normally, the vibration signal of the motor should be stable. When the motor has problems such as wear and imbalance, the vibration signal will change abnormally, and these changes can be captured before other parameters such as temperature or speed change. The current value of the motor directly reflects the load of the motor. When the machine tool is operating normally, the current value should fluctuate within a specific range, and when the motor has problems such as stuck and overload, the current value will change significantly, which may appear as a sudden increase or drop. By monitoring the current, the abnormal load of the motor can be discovered in time, so as to provide an early warning. The voltage value of the motor reflects the stability and quality of the power supply. Voltage fluctuations may cause unstable operation of the motor or even damage the motor. If the voltage is too high or too low, it may damage the insulation system of the motor, leading to motor failure. In general, the time queue data of the motor state parameters of vibration amplitude value, current value and voltage value is the basis for judging the abnormal state of the motor. These data can not only provide comprehensive monitoring of the motor operation state, but also help the operation and maintenance management system to more accurately identify potential faults through time series analysis and multi-dimensional collaborative analysis, thereby achieving early warning and efficient operation and maintenance. Accordingly, considering that data dispersion analysis will lead to increased complexity of data management, in order to effectively integrate and analyze monitoring data from different data sources, in the technical solution of the present application, the data transmission module in the data acquisition layer 110 is used to transmit the time queue of the motor state parameters to the data processing layer.

[0038] In the embodiment of the present application, the data processing layer 120 is used to group the time queue of the motor state parameters according to the parameter sample dimension to obtain the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value. Among them, there is a temporal correlation between the motor state parameters, and grouping the motor state parameter monitoring data collected by different sensors can make the data structure clearer, facilitate subsequent temporal analysis and correlation processing, so as to better help the system to perform fault diagnosis.

[0039] In an embodiment of the present application, the data analysis layer 130 is used to perform feature extraction and association analysis on the time queue of the vibration amplitude value, the time queue of the current value, and the time queue of the voltage value to obtain a motor state detection result, and the motor state detection result is used to indicate whether the motor state is abnormal. It should be understood that the motor is a core component of a CNC machine tool, and its operating state directly affects the safety and production efficiency of the equipment. By analyzing the collected time queue data of the vibration amplitude, current value, and voltage value, it is possible to detect whether the motor state is abnormal, which is conducive to timely discovering potential faults and facilitating enterprises to respond quickly, thereby reducing the risk of unexpected downtime, ensuring the continuity and stability of the production line, and reducing economic losses caused by faults.

[0040] In the embodiment of the present application, the application layer 140 is used to generate an alarm notification in response to the motor state detection result indicating that the motor state is abnormal. Specifically, once the motor state is detected to be abnormal, the application layer can quickly generate an alarm notification to ensure that relevant operation and maintenance personnel can be informed of the abnormal situation in a timely manner and take measures to deal with it.

[0041] Accordingly, in the data analysis layer, the technical concept of the present application is to use artificial intelligence-based data analysis and processing technology to perform time series analysis on the time queues of vibration amplitude values, current values, and voltage values ​​obtained by grouping the data processing layer, so as to intelligently obtain the motor state detection results based on the characteristics obtained by time series association of current and voltage and the time series synergistic response characteristics between the vibration amplitude time series characteristics, so as to capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. And it can automatically extract features from the data and identify the local time series synergy patterns between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance.

[0042] Figure 2 It is a block diagram of the data analysis layer in the intelligent operation and maintenance management system of CNC machine tools according to an embodiment of the present application. Figure 3 Schematic diagram of data flow in the data analysis layer of the intelligent operation and maintenance management system for CNC machine tools according to an embodiment of the present application. Figure 2 and Figure 3As shown, the data analysis layer 130 includes: a motor state parameter timing encoding module 131, which is used to perform timing sequence encoding on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value respectively to obtain a vibration amplitude timing correlation feature vector, a current timing correlation feature vector and a voltage timing correlation feature vector; a motor work power factor timing correlation representation generation module 132, which is used to input the current timing correlation feature vector and the voltage timing correlation feature vector into the motor work power timing analysis module to obtain a motor work power factor timing correlation representation vector; a motor power factor-vibration coordinated response analysis module 133, which is used to perform a motor power factor-vibration timing feature coordinated response analysis on the motor work power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector to obtain a motor power factor-vibration coordinated response feature; a motor state detection result generation module 134, which is used to obtain the motor state detection result based on the motor power factor-vibration coordinated response feature.

[0043] In an embodiment of the present application, the motor state parameter timing encoding module 131 is used to perform timing sequence encoding on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value to obtain the vibration amplitude timing correlation feature vector, the current timing correlation feature vector and the voltage timing correlation feature vector. Specifically, in an embodiment of the present application, the motor state parameter timing encoding module is used to: input the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value into the timing sequence encoder based on the Bi-LSTM model to obtain the vibration amplitude timing correlation feature vector, the current timing correlation feature vector and the voltage timing correlation feature vector. It should be understood that there are different timing correlation characteristics between each data point at different times in the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value, such as the change trend of the vibration amplitude and the current value fluctuation or the periodic change of the voltage value. Taking into account that the Bi-LSTM model can simultaneously consider the forward and reverse information of the input time queue, so as to more comprehensively understand the dynamic characteristics of the input time queue, based on this, in order to better capture and refine the timing correlation characteristics between data parameters between each time span, in the technical solution of the present application, the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value are respectively input into the timing sequence encoder based on the Bi-LSTM model to obtain the vibration amplitude timing correlation feature vector, the current timing correlation feature vector and the voltage timing correlation feature vector.

[0044] In the embodiment of the present application, the motor working power due to timing association representation generation module 132 is used to input the current timing association feature vector and the voltage timing association feature vector into the motor working power timing analysis module to obtain the motor working power due to timing association representation vector. Specifically, in the embodiment of the present application, the motor working power due to timing association representation generation module is used to: calculate the position point multiplication between the current timing association feature vector and the voltage timing association feature vector to obtain the motor working power due to timing association representation vector. It should be understood that during the operation of the machine tool motor, there is a close timing association relationship between voltage and current. For example, under the same load conditions, if the voltage is reduced, the current may increase accordingly to maintain the power output required by the motor. Therefore, in the technical solution of the present application, the current timing association feature vector and the voltage timing association feature vector are input into the motor working power timing analysis module to capture and dig out the timing dynamic association relationship between voltage and current, and obtain the motor working power due to timing association representation vector.

[0045] In an embodiment of the present application, the motor power factor-vibration coordinated response analysis module 133 is used to perform a motor power factor-vibration timing characteristic coordinated response analysis on the motor working power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector to obtain the motor power factor-vibration coordinated response feature. Accordingly, it is considered that the motor working power factor timing correlation representation vector expresses the dynamic characteristics of the motor that change with time during operation, that is, the influence of the current and voltage changes of the motor at different time points on the motor power output. The vibration amplitude timing correlation feature vector expresses the vibration characteristics of the motor that change with time during operation. Moreover, these two vectors have different degrees of correlation within the local time. Therefore, in order to analyze the timing relationship between the power characteristics and vibration conditions of the motor in fine granularity, so as to comprehensively and fully identify the correlation between the power change and the vibration amplitude of the motor when it is working, in the technical solution of the present application, the motor working power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector are subjected to the motor power factor-vibration timing feature coordinated response analysis to obtain the motor power factor-vibration response interaction expression matrix as the motor power factor-vibration coordinated response feature. That is, the motor power factor-vibration timing feature coordinated response analysis is a feature processing technology that combines feature refinement and feature relationship measurement, which is used to carefully analyze and fuse various information in the feature vector, accurately identify and understand the relationship between different features, and thus provide more reliable data support for subsequent abnormality detection.

[0046] Specifically, first, the vibration amplitude time series correlation feature vector is subjected to feature refinement processing to obtain a sequence of vibration amplitude local time series correlation feature vectors. It should be understood that feature refinement processing can more accurately analyze the change of vibration amplitude in a short time (i.e., in a local time window), more carefully understand the local subtle pattern of vibration amplitude changing over time, and enhance the ability to capture local features. Then, the motor working power factor time series correlation representation vector is subjected to nonlinear transformation processing to enrich the complex time series representation of the motor power factor to obtain the motor working power factor time series correlation representation vector after nonlinear transformation. Next, the correlation matrix between each vibration amplitude local feature and the nonlinear transformation feature is calculated to obtain a sequence of motor working power factor-vibration correlation matrix. That is, the correlation matrix can comprehensively reflect the time series mutual influence between the motor working power factor and the vibration amplitude. In this way, the relationship between the two can be more intuitively seen, which is convenient for analyzing how the dynamic change during motor operation affects the vibration amplitude. Subsequently, the characteristic interaction energy factor of each correlation matrix is ​​calculated to obtain a sequence of motor working power factor-vibration correlation feature interaction energy factors. In particular, the characteristic interaction energy factor can measure the energy size of the interaction between the motor working dynamic factor and the vibration amplitude. That is, by calculating these factors, the interaction strength and importance between the two can be specifically quantified, so as to better understand the impact of the dynamic change of the motor under different working conditions on the vibration. Then, each characteristic interaction energy factor is normalized by the Sigmoid function so that its value range falls within the interval (0, 1), and converted into an attention weight to intuitively show the contribution of each feature to the final detection result, while highlighting those features that are more important for motor state detection. Finally, the sequence of normalized factors is used as a sequence of weights to weight and calculate the sequence of the motor working dynamic factor-vibration association matrix to obtain the motor dynamic factor-vibration response interaction expression matrix. Specifically, the response information of the local features of the motor dynamic factor and the vibration amplitude is combined, and a comprehensive response representation is given at the global level, which improves the model's grasp of the overall feature response pattern.

[0047] Specifically, Figure 4 FIG. 1 is a block diagram of a motor dynamic factor-vibration coordinated response analysis module in an intelligent operation and maintenance management system for CNC machine tools according to an embodiment of the present application. Figure 4As shown, the motor power factor-vibration coordinated response analysis module 133 includes: a vibration amplitude time series correlation feature refinement unit 1331, which is used to perform feature refinement on the vibration amplitude time series correlation feature vector to obtain a sequence of vibration amplitude local time series correlation feature vectors; a motor work power factor-vibration correlation unit 1332, which is used to calculate the correlation matrix between the motor work power factor time series correlation representation vector and each vibration amplitude local time series correlation feature vector in the sequence of the vibration amplitude local time series correlation feature vector to obtain a sequence of motor work power factor-vibration correlation matrices; a motor power factor-vibration local-global joint response unit 1333, which is used to perform local and global joint response evaluation on the sequence of the motor work power factor-vibration correlation matrix to obtain a motor power factor-vibration response interactive expression matrix as the motor power factor-vibration coordinated response feature.

[0048] More specifically, Figure 5 FIG. 1 is a block diagram of a motor working power factor-vibration correlation unit in an intelligent operation and maintenance management system for CNC machine tools according to an embodiment of the present application. Figure 5 As shown, the motor working power factor-vibration correlation unit 1332 includes: a motor working power factor timing correlation representation vector nonlinear transformation subunit 13321, which is used to perform a nonlinear transformation on the motor working power factor timing correlation representation vector to obtain the motor working power factor timing correlation representation vector after the nonlinear transformation; a motor working power factor-vibration correlation matrix sequence calculation subunit 13322, which is used to calculate the correlation matrix between the motor working power factor timing correlation representation vector after the nonlinear transformation and each vibration amplitude local timing correlation feature vector in the sequence of the vibration amplitude local timing correlation feature vector to obtain the sequence of the motor working power factor-vibration correlation matrix.

[0049] In an embodiment of the present application, specifically, the nonlinear transformation subunit of the motor working power factor timing association representation vector is used to: multiply the motor working power factor timing association representation vector with the motor working power factor transformation matrix, and then add the obtained motor working power factor transformation vector and the motor working power factor bias vector according to position to obtain the motor working power factor timing association representation vector after the nonlinear transformation.

[0050] In an embodiment of the present application, specifically, the motor working power factor-vibration correlation matrix sequence calculation subunit is used to: vector multiply the transposed vector of the motor working power factor timing correlation representation vector after the nonlinear transformation and each vibration amplitude local timing correlation feature vector in the sequence of the vibration amplitude local timing correlation feature vector to obtain the sequence of the motor working power factor-vibration correlation matrix.

[0051] More specifically, Figure 6FIG. 1 is a block diagram of a motor dynamic factor-vibration local-global joint response unit in an intelligent operation and maintenance management system for CNC machine tools according to an embodiment of the present application. Figure 6 As shown, the motor power factor-vibration local-global joint response unit 1333 includes: a motor work power factor-vibration correlation characteristic interaction energy factor calculation subunit 13331, which is used to calculate the characteristic interaction energy factor of each motor work power factor-vibration correlation matrix in the sequence of the motor work power factor-vibration correlation matrix to obtain a sequence of motor work power factor-vibration correlation characteristic interaction energy factors; a motor work power factor-vibration correlation characteristic interaction energy factor normalization subunit 13332, which is used to input the sequence of the motor work power factor-vibration correlation characteristic interaction energy factors into a Sigmoid function to obtain a sequence of normalized motor work power factor-vibration correlation characteristic interaction energy factors; a motor power factor-vibration response interaction expression matrix generation subunit 13333, which is used to use the sequence of normalized motor work power factor-vibration correlation characteristic interaction energy factors as a sequence of weights, and calculate the position-weighted sum of the sequence of the motor work power factor-vibration correlation matrix to obtain the motor work power factor-vibration response interaction expression matrix.

[0052] More specifically, in an embodiment of the present application, the motor working power factor-vibration associated characteristic interaction energy factor calculation subunit includes: a motor working power factor-vibration associated kurtosis value calculation secondary subunit, which is used to calculate the kurtosis value of each motor working power factor-vibration association matrix in the sequence of the motor working power factor-vibration association matrix to obtain a sequence of motor working power factor-vibration associated kurtosis values; a motor working power factor-vibration interaction energy factor calculation secondary subunit, which is used to calculate the characteristic interaction energy factor of each motor working power factor-vibration association matrix based on each motor working power factor-vibration associated kurtosis value in the sequence of the motor working power factor-vibration associated kurtosis values ​​to obtain a sequence of the motor working power factor-vibration associated characteristic interaction energy factors.

[0053] More specifically, in an embodiment of the present application, the motor working power factor-vibration associated kurtosis value calculation secondary subunit is used to: respectively calculate the mean and variance of the motor working power factor-vibration associated matrix to obtain the motor working power factor-vibration associated mean and the motor working power factor-vibration associated variance; calculate the positional difference between the motor working power factor-vibration associated matrix and the motor working power factor-vibration associated mean to obtain the motor working power factor-vibration associated difference matrix; calculate the fourth power of each eigenvalue in the motor working power factor-vibration associated difference matrix to obtain the motor working power factor-vibration associated modulation difference matrix; calculate the expected value of the motor working power factor-vibration associated modulation difference matrix to obtain the motor working power factor-vibration associated expected value; divide the motor working power factor-vibration associated expected value by the square of the motor working power factor-vibration associated variance to obtain the motor working power factor-vibration associated kurtosis value.

[0054] More specifically, in an embodiment of the present application, the motor work power factor-vibration interaction energy factor calculation secondary subunit is used to: calculate the sum of the motor work power factor-vibration associated variance and the hyperparameter to obtain the motor work power factor-vibration associated first energy factor; calculate the square of the difference between the motor work power factor-vibration associated kurtosis value and the motor work power factor-vibration associated mean to obtain the motor work power factor-vibration associated difference value; add the value obtained by multiplying the motor work power factor-vibration associated variance by a constant two and the value obtained by multiplying the hyperparameter by a constant two to the motor work power factor-vibration associated difference value to obtain the motor work power factor-vibration associated second energy factor; divide the motor work power factor-vibration associated first energy factor by the motor work power factor-vibration associated second energy factor to obtain the motor work power factor-vibration associated characteristic interaction energy factor corresponding to the motor work power factor-vibration association matrix.

[0055] In the embodiment of the present application, specifically, the motor power factor-vibration coordinated response analysis module is used to: perform a coordinated response analysis between timing characteristics on the motor working power factor timing association representation vector and the vibration amplitude timing association feature vector, which can be expressed by the formula:

[0056]

[0057] in, is the vibration amplitude time series correlation feature vector, For feature refinement operation, are the first, second, ..., and third in the sequence of the local time series correlation feature vectors of the vibration amplitude. ,..., The local time series correlation feature vector of vibration amplitude, is the number of feature vectors in the sequence of the local time series associated feature vectors of the vibration amplitude, is a vector representing the timing relationship of the motor working power, and are the motor working power factor transformation matrix and the motor working power factor bias vector respectively, is the time-series correlation vector representing the motor working power after the nonlinear transformation, yes The transposed vector of represents vector multiplication, For the The motor working power factor-vibration correlation matrix between the local time series correlation feature vector of the vibration amplitude and the motor working power factor time series correlation representation vector after the nonlinear transformation, yes The eigenvalues ​​at each position in , yes The mean of To calculate the expected value of a matrix, yes The variance of for The kurtosis value of is a hyperparameter, yes The corresponding motor working power factor-vibration correlation characteristic interaction energy factor, yes function, yes The corresponding normalized motor working power factor-vibration correlation characteristic interaction energy factor, It is the point multiplication by position. is the motor dynamics factor-vibration response interaction expression matrix.

[0058] In an embodiment of the present application, the motor state detection result generation module 134 is used to obtain the motor state detection result based on the motor power factor-vibration coordinated response feature. Specifically, in an embodiment of the present application, the motor state detection result generation module is used to: input the motor power factor-vibration response interaction expression matrix into the motor state detection module based on the classifier to obtain the motor state detection result. That is, the motor power factor-vibration coordinated response feature obtained by performing coordinated response analysis between timing features using the motor working power factor timing association representation vector and the vibration amplitude timing association feature vector is classified and processed, so as to intelligently obtain the motor state detection result, which can capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. And it can automatically extract features from the data and identify local timing coordination patterns between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance.

[0059] Here, in the preferred example, the motor working power factor timing association representation vector and the vibration amplitude timing association feature vector represent the motor current-motor voltage timing synergistic association feature and the vibration amplitude timing association feature, respectively. Thus, when the motor working power factor timing association representation vector and the vibration amplitude timing association feature vector are input into the local-global collaborative response analysis module based on feature decoupling, the motor working power factor timing association representation vector and the vibration amplitude timing association feature vector will be transmitted to the local-global collaborative response analysis process based on feature decoupling due to the difference in source domain timing distribution and encoding method, so that the motor power factor-vibration response interaction expression matrix has a diversified set expression distribution of cross-modal aggregate response features. Therefore, it is expected to improve the balance between the regression mapping accuracy and completeness when the motor power factor-vibration response interaction expression matrix is ​​input into the motor state detection module based on the classifier for classification regression, thereby improving the accuracy of the motor state detection result.

[0060] In a preferred example, inputting the motor power factor-vibration response interaction expression matrix into a classifier-based motor state detection module to obtain a motor state detection result includes:

[0061] Expanding the motor power factor-vibration response interaction expression matrix into a motor power factor-vibration response interaction expression vector;

[0062] Performing L random sampling on the motor power factor-vibration response interaction expression vector to obtain L motor power factor-vibration response interaction random disturbance eigenvalues;

[0063] Arrange the L motor power factor-vibration response interaction random disturbance eigenvalues ​​in order from large to small to obtain a motor power factor-vibration response interaction random disturbance ordered vector;

[0064] Calculating the inner product of the motor power factor-vibration response interaction expression vector to obtain the motor power factor-vibration response interaction energy modulation eigenvalue;

[0065] Based on the motor power factor-vibration response interaction energy modulation characteristic value, the motor power factor-vibration response interaction random perturbation ordered vector is semantically scaled to obtain a semantically enhanced motor power factor-vibration response interaction random perturbation ordered vector;

[0066] Calculating the product between the random perturbation ordered vector of the semantic reinforcement motor dynamic factor-vibration response interaction and the transposed vector of the motor dynamic factor-vibration response interaction expression vector to obtain the motor dynamic factor-vibration response interaction best example prompt orthogonal modulation matrix;

[0067] Calculating the matrix product between the motor power factor-vibration response interaction best sample prompt orthogonal modulation matrix and the motor power factor-vibration response interaction expression vector to obtain an optimized motor power factor-vibration response interaction expression vector;

[0068] The optimized motor dynamic factor-vibration response interaction expression vector is input into the classifier-based motor state detection module to obtain the motor state detection result.

[0069] The above optimization process is expressed as follows:

[0070]

[0071] in, represents a normal distribution, represents the Gaussian density probability function, and Respectively represent the mean and variance of the motor dynamic factor-vibration response interaction expression vector, represents a random variable that follows a normal distribution, represents the number of random samplings, Indicates The random disturbance eigenvalue of the interaction between the motor dynamic factor and the vibration response, represents the motor dynamics factor-vibration response interaction expression vector, The first expression vector representing the motor dynamic factor-vibration response interaction eigenvalues, It means to calculate the average value of the vector. represents the length of the motor dynamics-vibration response interaction expression vector, It indicates that the random disturbance eigenvalues ​​of the interaction between the motor dynamic factor and the vibration response are arranged in an orderly manner from large to small. represents the ordered vector of random disturbance of motor dynamic factor-vibration response interaction, represents the motor dynamic factor-vibration response interaction energy modulation eigenvalue, represents the square of the vector's two-norm, It means point multiplication by position. represents matrix multiplication, represents the transpose of a vector, The best example of the interaction between motor dynamics and vibration response is indicated by the orthogonal modulation matrix, Represents the optimized motor force factor-vibration response interaction expression vector.

[0072] Accordingly, the motor power factor-vibration response interaction expression vector obtained by expanding the motor power factor-vibration response interaction expression matrix is ​​randomly sampled L times to obtain a set of new feature value sets based on the original features but containing slight changes, so as to introduce randomness to explore different aspects of the feature space, and by simulating the noise or variation in the actual data, it helps the model to better generalize to unseen data. Then, these motor power factor-vibration response interaction random perturbation feature values ​​are arranged in order from large to small to form a motor power factor-vibration response interaction random perturbation ordered vector to identify which perturbation directions may be the most important for the current problem. Such an ordering not only helps the algorithm focus on the feature differences that can best distinguish different categories, but also helps to reveal hidden data structures or patterns. Subsequently, the inner product of the motor dynamics factor-vibration response interaction expression vector is calculated to obtain the motor dynamics factor-vibration response interaction energy modulation eigenvalue, which reflects the overall strength or "energy" of the original feature vector and provides a benchmark for measuring the importance of the feature. Using this motor dynamics factor-vibration response interaction energy modulation eigenvalue as a reference point, the previously formed motor dynamics factor-vibration response interaction random perturbation ordered vector is semantically scaled to construct a semantically enhanced motor dynamics factor-vibration response interaction random perturbation ordered vector. In this way, those disturbances that appear to be more important in the original feature space are amplified, thereby enhancing the model's sensitivity to changes in key features, while also retaining relatively small but potentially useful disturbance information.

[0073] Then, the product of the random perturbation ordered vector of semantic reinforcement motor dynamic factor-vibration response interaction and the transpose of the motor dynamic factor-vibration response interaction expression vector is calculated to obtain the motor dynamic factor-vibration response interaction best example prompt orthogonal modulation matrix, the purpose of which is to find the interaction mode between features under two different perspectives. The formed matrix can be regarded as a conversion tool for capturing and representing the complex linear relationship between features. Finally, the final feature structure modulation is completed by applying the above-constructed motor dynamic factor-vibration response interaction best example prompt orthogonal modulation matrix to the original motor dynamic factor-vibration response interaction expression vector, so that the features are more in line with the requirements of the objective function, reducing redundant information, while maximizing the retention of useful information, so as to improve learning efficiency and prediction performance.

[0074] In summary, the feature manifold optimization process of the motor dynamic factor-vibration response interaction expression matrix is ​​explained, which extracts the best prompt sample based on the fine-grained target retrieval of random perturbations, and then guides the adversarial robustness learning based on the best prompt sample to complete the structure of the high-dimensional manifold of the feature vector in the feature space, so as to improve the detail and structure of the feature expression, so as to achieve the balanced executable between mapping accuracy and mapping completeness in the classification regression process based on the diversified feature distribution of the motor dynamic factor-vibration response interaction expression matrix, and improve the accuracy of the motor state detection result obtained by the motor state detection module based on the classifier input of the motor dynamic factor-vibration response interaction expression matrix, so as to capture more information about the operating status of the equipment, especially those subtle changes that may indicate early faults. And it can automatically extract features in the data and identify the local time series coordination mode between the data, so as to discover potential problems early, thereby improving the intelligent operation and maintenance management and predictive maintenance capabilities.

[0075] In summary, the CNC machine tool intelligent operation and maintenance management system 100 based on the embodiment of the present application is explained, which uses artificial intelligence-based data analysis and processing technology to perform timing analysis on the time queue of vibration amplitude values, the time queue of current values, and the time queue of voltage values, so as to intelligently obtain the motor state detection result based on the characteristics obtained by timing association of current and voltage and the timing coordinated response characteristics between the vibration amplitude timing characteristics, and can capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. And it can automatically extract features from the data and identify the local timing coordination mode between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance.

[0076] As described above, the CNC machine tool intelligent operation and maintenance management system 100 according to the embodiment of the present application can be implemented in various terminal devices, such as a server for intelligent operation and maintenance management of CNC machine tools. In one example, the CNC machine tool intelligent operation and maintenance management system 100 according to the embodiment of the present application can be integrated into the terminal device as a software module and / or a hardware module. For example, the CNC machine tool intelligent operation and maintenance management system 100 can be a software module in the operating system of the terminal device, or it can be an application developed for the terminal device; of course, the CNC machine tool intelligent operation and maintenance management system 100 can also be one of the many hardware modules of the terminal device.

[0077] Alternatively, in another example, the CNC machine tool intelligent operation and maintenance management system 100 and the terminal device may also be separate devices, and the CNC machine tool intelligent operation and maintenance management system 100 may be connected to the terminal device via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0078] Figure 7 Flowchart of the intelligent operation and maintenance management method of CNC machine tools according to an embodiment of the present application. Figure 7 As shown, in the intelligent operation and maintenance management method of CNC machine tools, it includes: S110, a sensor group of a motor deployed on the CNC machine tool collects a time queue of motor state parameters, wherein the motor state parameters include vibration amplitude value, current value and voltage value; S120, data grouping the time queue of the motor state parameters according to the parameter sample dimension to obtain a time queue of vibration amplitude value, a time queue of current value and a time queue of voltage value; S130, feature extraction and association analysis are performed on the time queue of vibration amplitude value, the time queue of current value and the time queue of voltage value to obtain a motor state detection result, and the motor state detection result is used to indicate whether there is an abnormality in the motor state; S140, in response to the motor state detection result indicating that the motor state is abnormal, an alarm notification is generated.

[0079] The specific operations of each step in the above CNC machine tool intelligent operation and maintenance management method have been referenced above. Figures 1 to 6 The invention has been introduced in detail in the description of the intelligent operation and maintenance management system of CNC machine tools, and therefore, its repeated description will be omitted.

[0080] In summary, the intelligent operation and maintenance management method of CNC machine tools based on the embodiment of the present application is explained, which uses artificial intelligence-based data analysis and processing technology to perform timing analysis on the time queue of vibration amplitude values, the time queue of current values, and the time queue of voltage values, so as to intelligently obtain the motor state detection result based on the characteristics obtained by timing association of current and voltage and the timing coordinated response characteristics between the timing characteristics of vibration amplitude, and can capture more information about the operating status of the equipment, especially those subtle changes that may indicate early failures. And it can automatically extract features from the data and identify the local timing coordination patterns between the data, so as to discover potential problems early, thereby improving the intelligence of operation and maintenance management and the ability of predictive maintenance.

[0081] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details of the above application are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to the use of the above specific details to be implemented.

Claims

1. Intelligent operation and maintenance management system for CNC machine tools, characterized in that: include: Data collection layer, data processing layer, data analysis layer and application layer; The data acquisition layer includes a data acquisition module and a data transmission module, wherein the data acquisition module is used to receive a time queue of motor state parameters collected by a sensor group of a motor deployed on a CNC machine tool, wherein the motor state parameters include vibration amplitude values, current values, and voltage values; and the data transmission module is used to transmit the time queue of the motor state parameters to the data processing layer; The data processing layer is used to group the time queue of the motor state parameters according to the parameter sample dimension to obtain a time queue of vibration amplitude values, a time queue of current values, and a time queue of voltage values; The data analysis layer is used to perform feature extraction and correlation analysis on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value to obtain a motor state detection result, and the motor state detection result is used to indicate whether the motor state is abnormal; The application layer is used to generate an alarm notification in response to the motor state detection result indicating that the motor state is abnormal; Wherein, the data analysis layer includes: A motor state parameter timing encoding module, used for respectively performing timing sequence encoding on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value to obtain a vibration amplitude timing correlation feature vector, a current timing correlation feature vector and a voltage timing correlation feature vector; A motor working power factor timing correlation representation generation module, used for inputting the current timing correlation feature vector and the voltage timing correlation feature vector into the motor working power timing analysis module to obtain a motor working power factor timing correlation representation vector; A motor power factor-vibration coordinated response analysis module is used to perform a motor power factor-vibration timing characteristic coordinated response analysis on the motor working power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector to obtain a motor power factor-vibration coordinated response feature; A motor state detection result generating module, used for obtaining the motor state detection result based on the motor power factor-vibration coordinated response characteristics; The motor dynamic factor-vibration coordinated response analysis module includes: A vibration amplitude time series correlation feature refinement unit, used for performing feature refinement on the vibration amplitude time series correlation feature vector to obtain a sequence of vibration amplitude local time series correlation feature vectors; A motor working power factor-vibration correlation unit, used for calculating the correlation matrix between the motor working power factor time series correlation representation vector and each vibration amplitude local time series correlation feature vector in the sequence of the vibration amplitude local time series correlation feature vector to obtain a sequence of motor working power factor-vibration correlation matrices; The motor power factor-vibration local and global joint response unit is used to perform local and global joint response evaluation on the sequence of the motor working power factor-vibration association matrix to obtain the motor power factor-vibration response interaction expression matrix as the motor power factor-vibration coordinated response feature.

2. The intelligent operation and maintenance management system for CNC machine tools according to claim 1 is characterized in that: The motor state parameter timing encoding module is used to: input the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value into a timing sequence encoder based on a Bi-LSTM model respectively to obtain the vibration amplitude timing association feature vector, the current timing association feature vector and the voltage timing association feature vector.

3. The intelligent operation and maintenance management system for CNC machine tools according to claim 2 is characterized in that: The motor working power factor-vibration correlation unit comprises: A nonlinear transformation subunit of the motor working power factor timing correlation representation vector, used for performing a nonlinear transformation on the motor working power factor timing correlation representation vector to obtain a nonlinearly transformed motor working power factor timing correlation representation vector; The motor working power factor-vibration correlation matrix sequence calculation subunit is used to calculate the correlation matrix between the motor working power factor time series correlation representation vector after the nonlinear transformation and each vibration amplitude local time series correlation feature vector in the sequence of the vibration amplitude local time series correlation feature vector to obtain the motor working power factor-vibration correlation matrix sequence.

4. The intelligent operation and maintenance management system for CNC machine tools according to claim 3 is characterized in that: The motor power factor-vibration local and global joint response unit comprises: A motor work power factor-vibration correlation characteristic interaction energy factor calculation subunit, used to calculate the characteristic interaction energy factor of each motor work power factor-vibration correlation matrix in the sequence of the motor work power factor-vibration correlation matrix to obtain a sequence of motor work power factor-vibration correlation characteristic interaction energy factors; A motor work power factor-vibration correlation characteristic interaction energy factor normalization subunit, used for inputting the sequence of the motor work power factor-vibration correlation characteristic interaction energy factor into a Sigmoid function to obtain a sequence of normalized motor work power factor-vibration correlation characteristic interaction energy factors; The motor power factor-vibration response interaction expression matrix generating subunit is used to calculate the position-weighted sum of the sequence of the motor work power factor-vibration association matrix using the sequence of the normalized motor work power factor-vibration association characteristic interaction energy factors as the sequence of weights to obtain the motor power factor-vibration response interaction expression matrix.

5. The intelligent operation and maintenance management system for CNC machine tools according to claim 4 is characterized in that: The motor working power factor-vibration correlation characteristic interaction energy factor calculation subunit includes: A motor work power factor-vibration associated kurtosis value calculation secondary subunit is used to calculate the kurtosis value of each motor work power factor-vibration associated matrix in the sequence of the motor work power factor-vibration associated matrix to obtain a sequence of motor work power factor-vibration associated kurtosis values; The motor working power factor-vibration interaction energy factor calculation secondary subunit is used to calculate the characteristic interaction energy factor of each motor working power factor-vibration association matrix based on each motor working power factor-vibration association kurtosis value in the sequence of the motor working power factor-vibration association kurtosis value to obtain the sequence of the motor working power factor-vibration association characteristic interaction energy factors.

6. The intelligent operation and maintenance management system for CNC machine tools according to claim 5 is characterized in that: The motor working power factor-vibration associated kurtosis value calculation secondary subunit is used for: Respectively calculating the mean and variance of the motor working power factor-vibration correlation matrix to obtain the motor working power factor-vibration correlation mean and the motor working power factor-vibration correlation variance; Calculating the position difference between the motor working power factor-vibration correlation matrix and the motor working power factor-vibration correlation mean to obtain the motor working power factor-vibration correlation difference matrix; Calculating the fourth power of each eigenvalue in the motor working power factor-vibration correlation difference matrix to obtain the motor working power factor-vibration correlation modulation difference matrix; Calculating the expected value of the motor working power factor-vibration correlation modulation difference matrix to obtain the motor working power factor-vibration correlation expected value; The motor work power factor-vibration correlation expected value is divided by the square of the motor work power factor-vibration correlation variance to obtain the motor work power factor-vibration correlation kurtosis value.

7. The intelligent operation and maintenance management system for CNC machine tools according to claim 6 is characterized in that: The motor working power factor-vibration interaction energy factor calculation secondary subunit is used for: Calculating the sum of the motor working power factor-vibration correlation variance and the hyperparameter to obtain the motor working power factor-vibration correlation first energy factor; Calculating the square of the difference between the motor work power factor-vibration correlation kurtosis value and the motor work power factor-vibration correlation mean value to obtain the motor work power factor-vibration correlation difference value; The motor work power factor-vibration correlation variance is multiplied by a constant 2, the value obtained by multiplying the hyper parameter by a constant 2, and the motor work power factor-vibration correlation difference value are added to obtain a motor work power factor-vibration correlation second energy factor; The motor work power factor-vibration associated first energy factor is divided by the motor work power factor-vibration associated second energy factor to obtain the motor work power factor-vibration associated characteristic interaction energy factor corresponding to the motor work power factor-vibration association matrix.

8. The intelligent operation and maintenance management system for CNC machine tools according to claim 7 is characterized in that: The motor state detection result generating module is used to: input the motor power factor-vibration response interaction expression matrix into the classifier-based motor state detection module to obtain the motor state detection result.

9. Intelligent operation and maintenance management method for CNC machine tools, characterized in that: include: A time queue for collecting motor state parameters by a sensor group of a motor deployed on a CNC machine tool, wherein the motor state parameters include vibration amplitude value, current value and voltage value; The time queue of the motor state parameters is grouped according to the parameter sample dimension to obtain a time queue of vibration amplitude values, a time queue of current values, and a time queue of voltage values; Performing feature extraction and correlation analysis on the time queue of the vibration amplitude value, the time queue of the current value, and the time queue of the voltage value to obtain a motor state detection result, wherein the motor state detection result is used to indicate whether the motor state is abnormal; In response to the motor state detection result indicating that the motor state is abnormal, generating an alarm notification; Among them, feature extraction and correlation analysis are performed on the time queue of the vibration amplitude value, the time queue of the current value, and the time queue of the voltage value to obtain the motor state detection result, including: Performing time series encoding on the time queue of the vibration amplitude value, the time queue of the current value and the time queue of the voltage value respectively to obtain a vibration amplitude time series correlation feature vector, a current time series correlation feature vector and a voltage time series correlation feature vector; Inputting the current time series correlation feature vector and the voltage time series correlation feature vector into a motor working power timing analysis module to obtain a motor working power factor timing correlation representation vector; Performing a motor power factor-vibration timing feature coordinated response analysis on the motor working power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector to obtain a motor power factor-vibration coordinated response feature; Based on the motor dynamic factor-vibration coordinated response characteristics, obtaining the motor state detection result; Among them, the motor working power factor timing correlation representation vector and the vibration amplitude timing correlation feature vector are subjected to the motor power factor-vibration timing feature coordinated response analysis to obtain the motor power factor-vibration coordinated response feature, including: Performing feature refinement on the vibration amplitude time series correlation feature vector to obtain a sequence of vibration amplitude local time series correlation feature vectors; Calculating the correlation matrix between the motor working power factor timing correlation representation vector and each vibration amplitude local timing correlation feature vector in the sequence of the vibration amplitude local timing correlation feature vector to obtain a sequence of motor working power factor-vibration correlation matrices; The sequence of the motor working dynamic factor-vibration correlation matrix is ​​evaluated locally and globally to obtain a motor dynamic factor-vibration response interaction expression matrix as the motor dynamic factor-vibration synergistic response feature.

Images