Industrial equipment multi-source signal processing and running state monitoring system based on artificial intelligence

Abstract

The invention provides an industrial equipment multi-source signal processing and running state monitoring system based on artificial intelligence, and belongs to the technical field of industrial equipment monitoring. Comprising a data acquisition module used for establishing a machine tool operation data set; the multi-source signal processing module is used for obtaining a multi-source processing signal; the artificial intelligence model construction module is used for constructing an artificial intelligence model by using the convolutional neural network; the operation state analysis module is used for constructing a spindle load adaptation coefficient, a torque margin coefficient and a cooling coupling influence coefficient of the ith full-automatic numerical control machine tool; and the running state judgment module is used for constructing a comprehensive working condition matching coefficient of the ith full-automatic numerical control machine tool. According to the method, intelligent prediction of the running state of the full-automatic numerical control machine tool is achieved, a traditional judgment mode based on a single threshold value or an empirical rule is avoided, and the accuracy and adaptability of running state recognition under the multi-working-condition and multi-load condition are improved.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment monitoring technology, and more specifically, to an artificial intelligence-based multi-source signal processing and operational status monitoring system for industrial equipment. Background Technology

[0002] With the continuous improvement of intelligent manufacturing and industrial automation, fully automatic CNC machine tools are widely used in production workshops such as automotive parts, mold manufacturing and precision machining. CNC machine tools operate under high speed, high load and continuous machining conditions, and the operating status of their spindle system, drive system and cooling system directly affects the machining accuracy, equipment reliability and production safety.

[0003] Existing CNC machine tool operation status monitoring systems mostly rely on single or a small number of operating parameters for judgment, such as assessing the status based solely on spindle current, spindle speed, or temperature signals. While these methods can reflect the machine tool's operating trend to some extent, they fail to comprehensively consider multi-source operating information such as spindle output torque, torque limiting margin, structural displacement, and cooling status. This makes it difficult to accurately depict the true operating status under complex machining conditions, easily leading to misjudgments or omissions. Furthermore, the lack of an effective multi-source signal processing mechanism further reduces the accuracy and stability of the status assessment. Summary of the Invention

[0004] To overcome the above deficiencies, the present invention provides an artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment that overcomes or at least partially solves the above technical problems.

[0005] This invention is implemented as follows: This invention provides an artificial intelligence-based multi-source signal processing and operational status monitoring system for industrial equipment, comprising: The data acquisition module is used to set up several fully automatic CNC machine tools in the target production workshop and to acquire multi-source operating data during the operation of the i-th fully automatic CNC machine tool, including the spindle rotation speed of the i-th fully automatic CNC machine tool. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Establish a machine tool operation dataset; The multi-source signal processing module is used to perform signal processing on multi-source operating data in the machine tool operating dataset, including the spindle rotation speed of the i-th fully automatic CNC machine tool in the multi-source operating dataset. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Time base unification and abnormal data suppression are performed to obtain multi-source processed signals. The artificial intelligence model building module is used to build an artificial intelligence model using a convolutional neural network, and inputs the processed machine tool operation dataset into the artificial intelligence model to output a prediction of the operating status of the i-th fully automatic CNC machine tool; The operation status analysis module is used to extract the spindle rotation speed of the i-th fully automatic CNC machine tool based on the processed machine tool operation dataset. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Construct the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool. Torque margin coefficient Coupling effect coefficient of cooling ; The operation status determination module is used to determine the load adaptation coefficient of the i-th fully automatic CNC machine tool spindle. Torque margin coefficient Coupling effect coefficient of cooling Correlation, construct the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool. And conduct evaluation and optimization.

[0006] In a preferred embodiment, the data acquisition module includes a region division unit, a spindle speed acquisition unit, a spindle output torque acquisition unit, a torque limiting remaining acquisition unit, an instantaneous displacement acquisition unit, and a coolant pressure acquisition unit. The area division unit is used to set up a number of fully automatic CNC machine tools in the target workshop, and to mark them as the first fully automatic CNC machine tool, the second fully automatic CNC machine tool, the third fully automatic CNC machine tool, ... the nth fully automatic CNC machine tool, where n represents the number of fully automatic CNC machine tools; The spindle speed acquisition unit is used to acquire the rotational speed of the i-th fully automatic CNC machine tool spindle in real time by installing a photoelectric sensor on the spindle of the i-th fully automatic CNC machine tool and using the pulse signal output by the photoelectric sensor. The spindle output torque acquisition unit is used to acquire the output torque of the i-th fully automatic CNC machine tool spindle in real time by using the torque feedback signal of the i-th fully automatic CNC machine tool spindle driver, converting the acquired signal into a physical torque value, and performing time stamping and synchronization processing.

[0007] In a preferred embodiment, the torque limiting remaining acquisition unit is used to acquire the actual output torque and the maximum limiting torque of the spindle driver of the i-th fully automatic CNC machine tool in real time, and calculate the difference between the two to obtain the torque limiting remaining amount of the i-th fully automatic CNC machine tool spindle. The instantaneous displacement acquisition unit is used to acquire the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle in real time through a non-contact displacement sensor installed on the i-th fully automatic CNC machine tool spindle; The coolant pressure acquisition unit is used to acquire the coolant pressure value of the i-th fully automatic CNC machine tool in real time through a pressure sensor installed on the coolant pipeline of the i-th fully automatic CNC machine tool, and to construct a machine tool operation dataset.

[0008] In a preferred embodiment, the multi-source signal processing module includes a spindle rotation speed processing unit, a spindle output torque processing unit, a torque limiting residual amount processing unit, a spindle instantaneous displacement value processing unit, and a coolant pressure value processing unit. The spindle rotation speed processing unit is used to process the rotation speed of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected rotation speed of the i-th fully automatic CNC machine tool spindle according to a unified time reference to achieve synchronization with other multi-source operating parameters, performing abnormal data suppression processing on the resampled rotation speed signal, including detecting and correcting or removing abnormal values, and using a sliding window for smoothing processing to output a smooth and reliable rotation speed signal of the i-th fully automatic CNC machine tool spindle. The spindle output torque processing unit is used to process the output torque of the i-th fully automatic CNC machine tool spindle. Specifically, it resamples the collected output torque of the i-th fully automatic CNC machine tool spindle according to a unified time base, performs abnormal data suppression processing on the resampled torque signal, including detecting and correcting or eliminating abnormal values, and smoothing the signal using a sliding window or filtering method; and outputs a smooth and reliable output torque signal of the i-th fully automatic CNC machine tool spindle.

[0009] In a preferred embodiment, the torque limiting residual amount processing unit is used to process the torque limiting residual amount of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected torque limiting residual amount of the i-th fully automatic CNC machine tool spindle according to a unified time base, performing abnormal data suppression processing on the resampled limiting residual amount signal, including detecting and correcting or removing outliers, and smoothing the signal using a sliding window or filtering method; and outputting a smooth and reliable torque limiting residual amount signal of the i-th fully automatic CNC machine tool spindle. The instantaneous displacement value processing unit of the spindle is used to process the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected instantaneous displacement value of the i-th fully automatic CNC machine tool spindle according to a unified time reference, performing abnormal data suppression processing on the resampled instantaneous displacement signal, including detecting and correcting or removing abnormal values, and smoothing the signal through a sliding window or filtering method; and outputting a smooth and reliable instantaneous displacement value signal of the i-th fully automatic CNC machine tool spindle. The coolant pressure value processing unit is used to process the coolant pressure value of the i-th fully automatic CNC machine tool. Specifically, it includes resampling the collected coolant pressure value of the i-th fully automatic CNC machine tool according to a unified time reference, performing abnormal data suppression processing on the resampled pressure signal, including detecting and correcting or removing abnormal values, and smoothing the signal through a sliding window or filtering method to output a smooth and reliable coolant pressure value signal of the i-th fully automatic CNC machine tool.

[0010] In a preferred embodiment, the artificial intelligence model building module includes a modeling unit; The modeling unit is used to construct an artificial intelligence model using a convolutional neural network, train and test the artificial intelligence model with a machine tool operation dataset, and use the trained artificial intelligence model as the operation status evaluation model of the i-th fully automatic CNC machine tool. At the same time, the machine tool operation dataset output of the equipment operation artificial intelligence model is used as a feature vector to identify feature information, and the trained artificial intelligence model is used as a data operation prediction.

[0011] In a preferred embodiment, the operating status analysis module includes a spindle load adaptation calculation unit, a load evaluation unit, a torque margin calculation unit, a torque margin evaluation unit, a cooling coupling calculation unit, and a cooling coupling evaluation unit. The spindle load adaptation calculation unit is used to calculate the spindle rotation speed of the i-th fully automatic CNC machine tool in the machine tool operation dataset. Spindle output torque and the instantaneous displacement value of the spindle The spindle load adaptation coefficient of the i-th fully automatic CNC machine tool is obtained by calculation. ; First, construct the load intensity characteristics of the i-th fully automatic CNC machine tool. ; ; Secondly, construct the structural response correction factor for the i-th fully automatic CNC machine tool. ; ; Finally, based on the load strength characteristics of the i-th fully automatic CNC machine tool Structural response correction factor of the i-th fully automatic CNC machine tool The spindle load adaptation coefficient of the i-th fully automatic CNC machine tool is obtained by the following formula. ; ; The load assessment unit is used to preset the load threshold Q and to set the load adaptation coefficient of the i-th fully automatic CNC machine tool spindle. Compare with the load threshold Q; when When the value is greater than Q, it indicates that the current spindle load of the i-th fully automatic CNC machine tool is normal, and the current parameters should be maintained to continue operation; when When ≤Q, it indicates that the current spindle load of the i-th fully automatic CNC machine tool is abnormal. The spindle speed of the i-th fully automatic CNC machine tool needs to be reduced by 8%-20% based on the current setting value, and the feed speed should be reduced by 17%-34% at the same time. Furthermore, the rapid traverse speed should be reduced by 5%-15% to restore the spindle load and structural response to the appropriate state.

[0012] In a preferred embodiment, the torque margin calculation unit is used to calculate the output torque of the i-th fully automatic CNC machine tool spindle in the machine tool operation dataset. and torque limiting remaining amount The torque margin coefficient of the i-th fully automatic CNC machine tool is obtained by calculation. ; ; The torque margin evaluation unit is used to preset the torque margin threshold F and to set the torque margin coefficient of the i-th fully automatic CNC machine tool. Compare with the torque margin threshold F; when When the value is greater than F, it indicates that the spindle torque of the i-th fully automatic CNC machine tool is normal, and the current parameters should be maintained for continued operation. when When F ≤ F, it indicates that the spindle torque of the i-th fully automatic CNC machine tool is abnormal. The spindle speed needs to be reduced by 11%-26% from the current setting value, and the feed rate should be reduced by 14%-33% and the depth of cut should be reduced by 12%-20% to reduce the spindle load and restore the torque margin.

[0013] In a preferred embodiment, the cooling coupling calculation unit is used to calculate the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle based on the machine tool operation dataset. and coolant pressure value The cooling coupling influence coefficient of the i-th fully automatic CNC machine tool is obtained by calculation. ; First, construct the cooling effectiveness characteristics of the i-th fully automatic CNC machine tool. ; ; Secondly, construct the operation stability correction factor for the i-th fully automatic CNC machine tool. ; ; Finally, the cooling effectiveness characteristics of the i-th fully automatic CNC machine tool were analyzed. And the stability correction factor for the i-th fully automatic CNC machine tool The cooling coupling influence coefficient of the i-th fully automatic CNC machine tool is calculated using the following formula. ; ; The cooling coupling evaluation unit is used to preset the cooling coupling threshold Z and to set the cooling coupling influence coefficient of the i-th fully automatic CNC machine tool. Compare with the cooling coupling threshold Z; when When the value is greater than Z, it indicates that the i-th fully automatic CNC machine tool is currently in a normal cooling state and is maintaining its current parameters. when When Z ≤ Z, it indicates that the current cooling status of the i-th fully automatic CNC machine tool is abnormal. The coolant pressure of the i-th fully automatic CNC machine tool needs to be increased by 9%-27%, and the spindle speed should be reduced by 3%-15% and the feed rate should be reduced by 8%-22% at the same time to improve the impact of cooling conditions on the spindle's operational stability.

[0014] In a preferred embodiment, the operating status determination module includes an association unit and an analysis unit; The associated unit is used to adjust the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool. Torque margin coefficient Coupling effect coefficient of cooling The correlation is established, and the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool is obtained through calculation. ; ; The analysis unit is used to preset the comprehensive working condition matching threshold AL, and to set the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool. Compare with the comprehensive operating condition matching threshold AL; when When >AL, it indicates that the current working condition of the i-th fully automatic CNC machine tool is normal, and it will continue to operate with the current machining parameters. when When ≤AL, it indicates that the current working condition of the i-th fully automatic CNC machine tool is abnormal. The spindle speed of the i-th fully automatic CNC machine tool is reduced by 5%-20%, the feed rate is reduced by 10%-30%, and in some embodiments, the coolant pressure is increased by 6%-31% to reduce the spindle load, increase the torque margin, and improve the operational stability.

[0015] This invention provides an artificial intelligence-based multi-source signal processing and operational status monitoring system for industrial equipment, the beneficial effects of which include: 1. By unifying the time base and suppressing abnormal data of multi-source operating data such as spindle rotation speed, spindle output torque, torque limit remaining amount, spindle instantaneous displacement value and coolant pressure value, the time drift and abnormal noise interference between different acquisition sources are effectively eliminated, improving the authenticity and comparability of machine tool operating data and providing a reliable data foundation for subsequent status analysis. By inputting multi-source processed signals into an artificial intelligence model based on convolutional neural network, intelligent prediction of the operating status of fully automatic CNC machine tools is realized, avoiding the traditional judgment method based on a single threshold or empirical rules, and improving the accuracy and adaptability of operating status identification under multiple working conditions and multiple loads.

[0016] 2. By constructing spindle load adaptation coefficient, torque margin coefficient, and cooling coupling influence coefficient, the machine tool operating status is quantitatively characterized from multiple dimensions such as load matching, drive margin, and cooling support capability. This gives the operating status assessment results clear physical meaning and engineering interpretability, avoiding the problem of "uninterpretable" results from artificial intelligence models. Through comprehensive analysis of spindle load, torque margin, and cooling coupling relationship, and further construction of comprehensive working condition matching coefficient, the overall assessment of the machine tool operating status is achieved. This effectively suppresses false alarms and missed alarms caused by changes in machining conditions, load fluctuations, or cooling conditions. The assessment of machine tool operating status based on comprehensive working condition matching coefficient can provide a basis for optimizing and controlling operating parameters, improving the stability and continuity of the machine tool operation process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, referring to Figure 1 This invention provides a technical solution: an artificial intelligence-based multi-source signal processing and operational status monitoring system for industrial equipment, comprising: The data acquisition module is used to set up several fully automatic CNC machine tools in the target production workshop and to acquire multi-source operating data during the operation of the i-th fully automatic CNC machine tool, including the spindle rotation speed of the i-th fully automatic CNC machine tool. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Establish a machine tool operation dataset; The multi-source signal processing module is used to perform signal processing on multi-source operating data in the machine tool operating dataset, including the spindle rotation speed of the i-th fully automatic CNC machine tool in the multi-source operating dataset. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Time base unification and abnormal data suppression are performed to obtain multi-source processed signals. The artificial intelligence model building module is used to build an artificial intelligence model using a convolutional neural network, and inputs the processed machine tool operation dataset into the artificial intelligence model to output a prediction of the operating status of the i-th fully automatic CNC machine tool; The operation status analysis module is used to extract the spindle rotation speed of the i-th fully automatic CNC machine tool based on the processed machine tool operation dataset. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Construct the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool. Torque margin coefficient Coupling effect coefficient of cooling ; The operation status determination module is used to determine the load adaptation coefficient of the i-th fully automatic CNC machine tool spindle. Torque margin coefficient Coupling effect coefficient of cooling Correlation, construct the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool. And conduct evaluation and optimization.

[0021] In this embodiment, by unifying the time base and suppressing abnormal data of multi-source operating data such as spindle rotation speed, spindle output torque, torque limiting remaining amount, spindle instantaneous displacement value, and coolant pressure value, the time drift and abnormal noise interference between different acquisition sources are effectively eliminated, improving the authenticity and comparability of machine tool operating data and providing a reliable data foundation for subsequent state analysis. By inputting the multi-source processed signals into an artificial intelligence model based on a convolutional neural network, intelligent prediction of the operating state of a fully automatic CNC machine tool is achieved, avoiding the traditional judgment method based on a single threshold or empirical rules, and improving the accuracy and adaptability of operating state identification under multiple working conditions and multiple loads.

[0022] By constructing spindle load adaptation coefficient, torque margin coefficient, and cooling coupling influence coefficient, the machine tool operating status is quantitatively characterized from multiple dimensions such as load matching, drive margin, and cooling support capability. This gives the operating status evaluation results clear physical meaning and engineering interpretability, avoiding the problem of "uninterpretable" results from artificial intelligence models. Through comprehensive analysis of spindle load, torque margin, and cooling coupling relationship, and further construction of comprehensive working condition matching coefficient, the overall evaluation of the machine tool operating status is achieved. This effectively suppresses false alarms and missed alarms caused by changes in machining conditions, load fluctuations, or cooling conditions. Based on the comprehensive working condition matching coefficient, the machine tool operating status is evaluated, enabling the system not only to identify the current operating status but also to provide a basis for optimizing and controlling operating parameters, thereby improving the stability and continuity of the machine tool operation process.

[0023] Example 2 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the data acquisition module includes a region division unit, a spindle speed acquisition unit, a spindle output torque acquisition unit, a torque limiting remaining acquisition unit, an instantaneous displacement acquisition unit, and a coolant pressure acquisition unit; The area division unit is used to set up a number of fully automatic CNC machine tools in the target workshop, and to mark them as the first fully automatic CNC machine tool, the second fully automatic CNC machine tool, the third fully automatic CNC machine tool, ... the nth fully automatic CNC machine tool, where n represents the number of fully automatic CNC machine tools; The spindle speed acquisition unit is used to acquire the rotational speed of the i-th fully automatic CNC machine tool spindle in real time by installing a photoelectric sensor on the spindle of the i-th fully automatic CNC machine tool and using the pulse signal output by the photoelectric sensor. The spindle output torque acquisition unit is used to acquire the output torque of the i-th fully automatic CNC machine tool spindle in real time by using the torque feedback signal of the i-th fully automatic CNC machine tool spindle driver, converting the acquired signal into a physical torque value, and performing time stamping and synchronization processing.

[0024] In this embodiment, several fully automatic CNC machine tools in the target workshop are uniformly marked and numbered by a regional division unit, so that the collected data can be accurately matched with specific machine tools, avoiding the problem of data confusion in a multi-machine tool operating environment, and improving the accuracy and traceability of machine tool operation data management. By installing a photoelectric sensor on the spindle, the spindle rotation speed is collected by the pulse signal output by the photoelectric sensor, realizing a non-contact, high-response speed acquisition method, effectively reducing the impact of mechanical wear and environmental interference on the speed measurement accuracy, and improving the stability and reliability of speed data.

[0025] By directly utilizing the torque feedback signal of the spindle drive and converting the acquired signal into a physical torque value, real-time acquisition of the actual output torque of the spindle is achieved. Compared with methods based on current or empirical estimation, this method can more realistically reflect the changes in spindle load and improve the accuracy of load assessment. By time-stamping and synchronizing the acquired spindle output torque signal, the torque data can be correlated and analyzed with other operating parameters such as spindle speed, displacement, and coolant pressure on the same time reference. This provides a reliable data foundation for multi-source signal fusion and operating status analysis. Through high-precision, real-time acquisition of key operating parameters such as spindle speed and output torque, the integrity and consistency of the data input to the multi-source signal processing module and artificial intelligence model are ensured, improving the accuracy and stability of subsequent operating status prediction, load assessment, and working condition matching analysis.

[0026] Example 3 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the torque limiting remaining acquisition unit is used to acquire the actual output torque and the maximum limiting torque of the spindle driver of the i-th fully automatic CNC machine tool in real time, and calculate the difference between the two to obtain the torque limiting remaining amount of the i-th fully automatic CNC machine tool spindle. The instantaneous displacement acquisition unit is used to acquire the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle in real time through a non-contact displacement sensor installed on the i-th fully automatic CNC machine tool spindle; The coolant pressure acquisition unit is used to acquire the coolant pressure value of the i-th fully automatic CNC machine tool in real time through a pressure sensor installed on the coolant pipeline of the i-th fully automatic CNC machine tool, and to construct a machine tool operation dataset.

[0027] In this embodiment, by acquiring the actual output torque and maximum limiting torque of the spindle driver in real time and calculating the difference between the two, the remaining space of the spindle torque from the limiting boundary can be intuitively reflected. This allows the system to identify potential risks of insufficient torque margin in advance and avoid the driver from frequently entering the limiting or protection state. Compared with only acquiring the spindle output torque, the remaining torque limiting amount can comprehensively reflect the relationship between load changes and driving capability, making the system more responsive to sudden cutting loads, changes in machining conditions, etc. By installing a non-contact displacement sensor on the spindle and acquiring the instantaneous displacement value of the spindle in real time, the system can capture the small structural responses generated by the spindle under high-speed rotation and cutting load, which helps to evaluate the spindle's operational stability and avoid a decrease in machining accuracy due to abnormal structural responses.

[0028] By employing non-contact displacement sensors for instantaneous displacement acquisition, mechanical contact wear and environmental vibration interference are avoided, improving the stability and long-term reliability of displacement measurement under high-speed, high-temperature, and complex machining environments. By installing pressure sensors on the coolant pipeline to collect coolant pressure values ​​in real time, the system can accurately reflect the working status of the cooling system, providing a reliable basis for determining whether the cooling conditions meet the thermal management requirements of the spindle and machining process. Combining coolant pressure data with operating parameters such as instantaneous spindle displacement helps to analyze the impact of changes in cooling conditions on the spindle structural response and operational stability, providing key data support for constructing the cooling coupling influence coefficient.

[0029] Example 4 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the multi-source signal processing module includes a spindle rotation speed processing unit, a spindle output torque processing unit, a torque limiting residual amount processing unit, a spindle instantaneous displacement value processing unit, and a coolant pressure value processing unit. The spindle rotation speed processing unit is used to process the rotation speed of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected rotation speed of the i-th fully automatic CNC machine tool spindle according to a unified time reference to achieve synchronization with other multi-source operating parameters, performing abnormal data suppression processing on the resampled rotation speed signal, including detecting and correcting or removing abnormal values, and using a sliding window for smoothing processing to output a smooth and reliable rotation speed signal of the i-th fully automatic CNC machine tool spindle. The spindle output torque processing unit is used to process the output torque of the i-th fully automatic CNC machine tool spindle. Specifically, it resamples the collected output torque of the i-th fully automatic CNC machine tool spindle according to a unified time base, performs abnormal data suppression processing on the resampled torque signal, including detecting and correcting or eliminating abnormal values, and smoothing the signal using a sliding window or filtering method; and outputs a smooth and reliable output torque signal of the i-th fully automatic CNC machine tool spindle.

[0030] In this embodiment, by resampling multi-source operating parameters such as spindle rotation speed and spindle output torque according to a unified time reference, data from different sampling frequencies and sampling times are aligned on the same time axis. This avoids time deviations in the fusion analysis of multi-source signals, improving the accuracy of subsequent multi-source signal correlation analysis and state assessment. By detecting, correcting, or eliminating abnormal data in the resampled rotation speed and torque signals, abnormal data points introduced by sensor jitter, communication interruptions, and environmental interference can be effectively eliminated, reducing the impact of noise on the operating state analysis results. By introducing a sliding window smoothing process, the spindle rotation speed and spindle output torque signals are smoothed, making the processed signals more continuous and stable, avoiding misjudgments of the operating state due to instantaneous fluctuations, and improving the reliability of the state monitoring results.

[0031] The processed spindle rotation speed and spindle output torque signals have higher signal-to-noise ratio and consistency, providing accurate and reliable input data for subsequent calculations of indicators such as spindle speed following error, spindle load adaptation coefficient and torque margin coefficient. By resampling, anomaly suppression and smoothing of multi-source operating data, the quality of data input to the artificial intelligence model is ensured, the interference of noise data on model training and prediction results is reduced, and the stability and generalization ability of operating status prediction are improved.

[0032] Example 5 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the torque limiting residual amount processing unit is used to process the torque limiting residual amount of the i-th fully automatic CNC machine tool spindle. This includes resampling the collected torque limiting residual amount of the i-th fully automatic CNC machine tool spindle according to a unified time reference; performing abnormal data suppression processing on the resampled limiting residual amount signal, including detecting and correcting or removing outliers; and smoothing the signal using a sliding window or filtering method; and outputting a smooth and reliable torque limiting residual amount signal of the i-th fully automatic CNC machine tool spindle. The instantaneous displacement value processing unit of the spindle is used to process the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected instantaneous displacement value of the i-th fully automatic CNC machine tool spindle according to a unified time reference, performing abnormal data suppression processing on the resampled instantaneous displacement signal, including detecting and correcting or removing abnormal values, and smoothing the signal through a sliding window or filtering method; and outputting a smooth and reliable instantaneous displacement value signal of the i-th fully automatic CNC machine tool spindle. The coolant pressure value processing unit is used to process the coolant pressure value of the i-th fully automatic CNC machine tool. Specifically, it includes resampling the collected coolant pressure value of the i-th fully automatic CNC machine tool according to a unified time reference, performing abnormal data suppression processing on the resampled pressure signal, including detecting and correcting or removing abnormal values, and smoothing the signal through a sliding window or filtering method to output a smooth and reliable coolant pressure value signal of the i-th fully automatic CNC machine tool.

[0033] In this embodiment, the spindle torque limiting residual quantity is resampled using a unified time reference, allowing it to be correlated with parameters such as spindle output torque and spindle rotation speed on the same time scale. This avoids torque margin assessment deviations caused by asynchronous sampling. Simultaneously, abnormal data suppression and smoothing effectively eliminate abrupt interference in the limiting residual quantity signal, improving the stability of torque margin coefficient calculation. Resampling, suppressing, and smoothing the spindle instantaneous displacement value accurately reflects the minute structural response changes of the spindle under different operating conditions, avoiding the amplification effect of instantaneous impacts or measurement noise on the displacement signal. This provides reliable data support for spindle load adaptation analysis and operational stability assessment. Furthermore, unified time reference processing and abnormal data suppression of the coolant pressure value prevent misjudgments of the cooling status due to pressure fluctuations, sensor jitter, or instantaneous interference, making the coolant pressure signal more stable and reliable, providing a stable input for cooling coupling effect analysis.

[0034] By uniformly resampling the remaining torque limit, instantaneous spindle displacement, and coolant pressure, the multi-source operating data is highly consistent in the time dimension, improving the accuracy and repeatability of multi-source signal fusion analysis. By detecting, correcting, or removing abnormal data in each processing unit, and combining this with smoothing methods such as sliding windows or filtering, the interference of outliers on operating status determination and threshold evaluation is effectively suppressed, reducing the occurrence of false alarms and missed alarms.

[0035] Example 6 is an explanation of Example 1; please refer to the provided text. Figure 1 Specifically, the artificial intelligence model building module includes a modeling unit; The modeling unit is used to construct an artificial intelligence model using a convolutional neural network, train and test the artificial intelligence model with a machine tool operation dataset, and use the trained artificial intelligence model as the operation status evaluation model of the i-th fully automatic CNC machine tool. At the same time, the machine tool operation dataset output of the equipment operation artificial intelligence model is used as a feature vector to identify feature information, and the trained artificial intelligence model is used as a data operation prediction.

[0036] In this embodiment, by using a convolutional neural network to train and test the machine tool operation dataset, the artificial intelligence model can automatically learn the potential feature relationships in multi-source operation data. Compared with judgment methods based on human experience or a single threshold, it can more accurately identify the operating status of fully automatic CNC machine tools. By inputting the machine tool operation dataset into the trained artificial intelligence model and outputting feature vectors, the key feature information in multi-source operation data can be automatically extracted, avoiding the limitations of manually designed features and improving the comprehensiveness and effectiveness of feature expression. The convolutional neural network can model the local correlation of multi-source operation data in time and feature dimensions, enabling the constructed operation status evaluation model to adapt to different processing conditions, load changes and operating modes, and improving the stability and robustness of the model under complex working conditions.

[0037] Example 7 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the operating status analysis module includes a spindle load adaptation calculation unit, a load evaluation unit, a torque margin calculation unit, a torque margin evaluation unit, a cooling coupling calculation unit, and a cooling coupling evaluation unit. The spindle load adaptation calculation unit is used to calculate the spindle rotation speed of the i-th fully automatic CNC machine tool in the machine tool operation dataset. Spindle output torque and the instantaneous displacement value of the spindle After normalization, the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool is obtained in the following way. ; First, construct the load intensity characteristics of the i-th fully automatic CNC machine tool. ; ; Secondly, construct the structural response correction factor for the i-th fully automatic CNC machine tool. ; ; Finally, based on the load strength characteristics of the i-th fully automatic CNC machine tool Structural response correction factor of the i-th fully automatic CNC machine tool The spindle load adaptation coefficient of the i-th fully automatic CNC machine tool is obtained by the following formula. ; ; The load assessment unit is used to preset the load threshold Q, which is determined based on the historical operating data of the i-th fully automatic CNC machine tool under normal processing conditions. The load adaptation coefficient of the spindle is calculated by statistical analysis of the spindle rotation speed, spindle output torque and spindle instantaneous displacement value, and the safe critical value under stable operating conditions is extracted as the load threshold Q. The spindle load adaptation coefficient of the i-th fully automatic CNC machine tool Compare with the load threshold Q; when When the value is greater than Q, it indicates that the current spindle load of the i-th fully automatic CNC machine tool is normal, and the current parameters should be maintained to continue operation; when When ≤Q, it indicates that the current spindle load of the i-th fully automatic CNC machine tool is abnormal. The spindle speed of the i-th fully automatic CNC machine tool needs to be reduced by 8%-20% based on the current setting value, and the feed speed should be reduced by 17%-34% at the same time. Furthermore, the rapid traverse speed should be reduced by 5%-15% to restore the spindle load and structural response to the appropriate state.

[0038] In this embodiment, by constructing load intensity characteristics and structural response correction factors, and further calculating the spindle load adaptation coefficient, multi-source operating parameters such as spindle rotation speed, output torque, and instantaneous displacement are integrated into evaluation indicators with clear physical meaning, thereby achieving a quantitative characterization of the spindle load state. By comparing the spindle load adaptation coefficient with a preset load threshold, the normal and abnormal states of the spindle load can be effectively distinguished, avoiding misjudgments caused by relying on a single parameter, and improving the accuracy and stability of spindle load anomaly identification. When the spindle load is abnormal, the spindle speed, feed rate, and rapid traverse speed are reduced proportionally to achieve flexible adjustment of the machine tool operating parameters, avoiding the impact of drastic intervention on the machining process, and at the same time helping to gradually restore the adaptation state between the spindle load and structural response.

[0039] By triggering parameter adjustment strategies in a timely manner when the spindle load approaches an abnormal level, the risks of spindle overload and amplified structural vibration are effectively reduced, thereby improving the operational safety of machine tools under complex machining conditions.

[0040] Example 8 is an explanation of Example 1; please refer to it. Figure 1 Specifically, the torque margin calculation unit is used to calculate the output torque of the i-th fully automatic CNC machine tool spindle in the machine tool operation dataset. and torque limiting remaining amount After normalization, the torque margin coefficient of the i-th fully automatic CNC machine tool is obtained using the following formula. ; ; The torque margin assessment unit is used to preset the torque margin threshold F, which is determined based on the historical operating data of the i-th fully automatic CNC machine tool under normal processing conditions. By collecting the spindle output torque and the torque limit remaining amount, the corresponding torque margin coefficient is calculated, and the torque margin coefficient in the stable operating range is statistically analyzed, and its safety critical value or lower limit value is set as the torque margin threshold F. The torque margin coefficient of the i-th fully automatic CNC machine tool Compare with the torque margin threshold F; when When the value is greater than F, it indicates that the spindle torque of the i-th fully automatic CNC machine tool is normal, and the current parameters should be maintained for continued operation. when When F ≤ F, it indicates that the spindle torque of the i-th fully automatic CNC machine tool is abnormal. The spindle speed needs to be reduced by 11%-26% from the current setting value, and the feed rate should be reduced by 14%-33% and the depth of cut should be reduced by 12%-20% to reduce the spindle load and restore the torque margin.

[0041] In this embodiment, by introducing a torque margin coefficient, the spindle output torque is correlated with the remaining torque limit of the driver, thereby achieving a quantitative description of the spindle's current available torque space. This allows for a direct reflection of whether the spindle torque is close to the limit state. By comparing the torque margin coefficient with a preset torque margin threshold, abnormal operating conditions with insufficient spindle torque margin can be effectively identified, avoiding overload, stall, or protection shutdown problems caused by torque approaching saturation. When insufficient torque margin is detected, the system reduces the spindle speed, feed rate, and depth of cut proportionally to achieve adaptive adjustment of the machining load, gradually moving the spindle torque away from the limit region and restoring safe operating margin.

[0042] By taking adjustment strategies in advance when the torque margin is close to the threshold, the probability of the driver triggering protection due to overcurrent or overload can be effectively reduced, thereby improving the reliability and safety of machine tool operation. In machining processes with high load or large fluctuations in operating conditions, by dynamically maintaining a reasonable torque margin, the drastic torque fluctuations during machining can be effectively suppressed, thereby improving cutting stability and machining quality.

[0043] Example 9, this example is an explanation of Example 1, please refer to it. Figure 1 Specifically, the cooling coupling calculation unit is used to calculate the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle based on the machine tool operation dataset. and coolant pressure value After normalization, the cooling coupling influence coefficient of the i-th fully automatic CNC machine tool is obtained in the following way. ; First, construct the cooling effectiveness characteristics of the i-th fully automatic CNC machine tool. ; ; Secondly, construct the operation stability correction factor for the i-th fully automatic CNC machine tool. ; ; Finally, the cooling effectiveness characteristics of the i-th fully automatic CNC machine tool were analyzed. And the stability correction factor for the i-th fully automatic CNC machine tool The cooling coupling influence coefficient of the i-th fully automatic CNC machine tool is calculated using the following formula. ; ; The cooling coupling evaluation unit is used to preset the cooling coupling threshold Z, which is determined based on the historical operating data of the i-th fully automatic CNC machine tool under normal cooling conditions, and to obtain the cooling coupling threshold Z. The cooling coupling influence coefficient of the i-th fully automatic CNC machine tool Compare with the cooling coupling threshold Z; when When the value is greater than Z, it indicates that the i-th fully automatic CNC machine tool is currently in a normal cooling state and is maintaining its current parameters. when When Z ≤ Z, it indicates that the current cooling status of the i-th fully automatic CNC machine tool is abnormal. The coolant pressure of the i-th fully automatic CNC machine tool needs to be increased by 9%-27%, and the spindle speed should be reduced by 3%-15% and the feed rate should be reduced by 8%-22% at the same time to improve the impact of cooling conditions on the spindle's operational stability.

[0044] In this embodiment, by introducing a cooling coupling influence coefficient, the coolant pressure and the instantaneous displacement of the spindle are correlated and modeled to achieve a quantitative characterization of the cooling effectiveness and its impact on the spindle's operational stability. This avoids the limitations of judging based on a single cooling parameter. By comparing the cooling coupling influence coefficient with a preset cooling coupling threshold, abnormal cooling coupling conditions caused by insufficient cooling or increased structural vibration can be accurately identified, and potential risks of thermal deformation or lubrication failure can be detected in advance. When an abnormal cooling condition is detected, the system increases the coolant pressure and simultaneously reduces the spindle speed and feed rate to achieve coordinated adjustment of cooling conditions and machining load, thereby effectively improving the coverage and heat dissipation capacity of the cooling medium on the spindle and cutting area.

[0045] Example 10: This example is an explanation of Example 1. Please refer to the provided text. Figure 1 Specifically, the operating status determination module includes an association unit and an analysis unit; The associated unit is used to adjust the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool. Torque margin coefficient Coupling effect coefficient of cooling The overall working condition matching coefficient of the i-th fully automatic CNC machine tool is obtained by correlation and calculation in the following manner. ; ; The analysis unit is used to preset the comprehensive working condition matching threshold AL, which is determined based on the historical operating data of the i-th fully automatic CNC machine tool under stable processing conditions. By performing correlation calculations on the spindle load adaptation coefficient, torque margin coefficient, and cooling coupling influence coefficient, the corresponding comprehensive working condition matching coefficient KG,i is obtained. The comprehensive working condition matching coefficient within the normal operating range is statistically analyzed, and the critical value or lower limit of its safe operating range is set as the comprehensive working condition matching threshold AL. The overall working condition matching coefficient of the i-th fully automatic CNC machine tool Compare with the comprehensive operating condition matching threshold AL; when When >AL, it indicates that the current working condition of the i-th fully automatic CNC machine tool is normal, and it will continue to operate with the current machining parameters. when When ≤AL, it indicates that the current working condition of the i-th fully automatic CNC machine tool is abnormal. The spindle speed of the i-th fully automatic CNC machine tool is reduced by 5%-20%, the feed rate is reduced by 10%-30%, and in some embodiments, the coolant pressure is increased by 6%-31% to reduce the spindle load, increase the torque margin, and improve the operational stability.

[0046] In this embodiment, by multiplying and correlating the multidimensional operating characteristics of spindle load, torque margin, and cooling status, a comprehensive operating condition matching coefficient is formed. This avoids the one-sidedness caused by judging a single parameter and achieves a comprehensive and objective evaluation of the overall operating condition of the machine tool. The comprehensive operating condition matching coefficient can amplify the coupling effect between various sub-states. When any operating link is abnormal, the comprehensive index will drop significantly, thereby improving the sensitivity and response speed for identifying complex and compound abnormal operating conditions.

[0047] Example: Several fully automatic CNC machine tools are placed in a workshop, and the i-th fully automatic CNC machine tool is analyzed; By collecting the spindle rotation speed of the i-th fully automatic CNC machine tool Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value The values ​​are obtained respectively; The normalized value is 0.47; The normalized value is 0.45; The normalized value is 0.30; The normalized value is 0.24; The normalized value is 0.36; After normalization, the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool is now... Torque margin coefficient Coupling effect coefficient of cooling ; The spindle load adaptation coefficient of the i-th fully automatic CNC machine tool ; ; Torque margin coefficient of the i-th fully automatic CNC machine tool , ; Cooling coupling influence coefficient of the i-th fully automatic CNC machine tool ; ; Finally, the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool was calculated. ; ; The preset comprehensive working condition matching threshold AL is set to 0.018; If the value is greater than 0.018, it means that the i-th fully automatic CNC machine tool is currently operating normally and will continue to operate with the current machining parameters.

[0048] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0049] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A multi-source signal processing and operational status monitoring system for industrial equipment based on artificial intelligence, characterized in that, include: The data acquisition module is used to set up several fully automatic CNC machine tools in the target production workshop and to acquire multi-source operating data during the operation of the i-th fully automatic CNC machine tool, including the spindle rotation speed of the i-th fully automatic CNC machine tool. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Establish a machine tool operation dataset; The multi-source signal processing module is used to perform signal processing on multi-source operating data in the machine tool operating dataset, including the spindle rotation speed of the i-th fully automatic CNC machine tool in the multi-source operating dataset. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Time base unification and abnormal data suppression are performed to obtain multi-source processed signals. The artificial intelligence model building module is used to build an artificial intelligence model using a convolutional neural network, and inputs the processed machine tool operation dataset into the artificial intelligence model to output a prediction of the operating status of the i-th fully automatic CNC machine tool; The operation status analysis module is used to extract the spindle rotation speed of the i-th fully automatic CNC machine tool based on the processed machine tool operation dataset. Spindle output torque Torque limiting remaining amount Instantaneous displacement value of the spindle and coolant pressure value Construct the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool. Torque margin coefficient Coupling effect coefficient of cooling ; The operation status determination module is used to determine the load adaptation coefficient of the i-th fully automatic CNC machine tool spindle. Torque margin coefficient Coupling effect coefficient of cooling Correlation, construct the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool. And conduct evaluation and optimization.

2. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 1, characterized in that, The data acquisition module includes a region division unit, a spindle speed acquisition unit, a spindle output torque acquisition unit, a torque limiting remaining acquisition unit, an instantaneous displacement acquisition unit, and a coolant pressure acquisition unit. The area division unit is used to set up a number of fully automatic CNC machine tools in the target workshop, and to mark them as the first fully automatic CNC machine tool, the second fully automatic CNC machine tool, the third fully automatic CNC machine tool, ... the nth fully automatic CNC machine tool, where n represents the number of fully automatic CNC machine tools; The spindle speed acquisition unit is used to acquire the rotational speed of the i-th fully automatic CNC machine tool spindle in real time by installing a photoelectric sensor on the spindle of the i-th fully automatic CNC machine tool and using the pulse signal output by the photoelectric sensor. The spindle output torque acquisition unit is used to acquire the output torque of the i-th fully automatic CNC machine tool spindle in real time by using the torque feedback signal of the i-th fully automatic CNC machine tool spindle driver, converting the acquired signal into a physical torque value, and performing time stamping and synchronization processing.

3. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 2, characterized in that, The torque limiting remaining acquisition unit is used to acquire the actual output torque and the maximum limiting torque of the spindle driver of the i-th fully automatic CNC machine tool in real time, and calculate the difference between the two to obtain the torque limiting remaining amount of the i-th fully automatic CNC machine tool spindle. The instantaneous displacement acquisition unit is used to acquire the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle in real time through a non-contact displacement sensor installed on the spindle of the i-th fully automatic CNC machine tool. The coolant pressure acquisition unit is used to acquire the coolant pressure value of the i-th fully automatic CNC machine tool in real time through a pressure sensor installed on the coolant pipeline of the i-th fully automatic CNC machine tool, and to construct a machine tool operation dataset.

4. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 3, characterized in that, The multi-source signal processing module includes a spindle rotation speed processing unit, a spindle output torque processing unit, a torque limiting residual amount processing unit, a spindle instantaneous displacement value processing unit, and a coolant pressure value processing unit. The spindle rotation speed processing unit is used to process the rotation speed of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected rotation speed of the i-th fully automatic CNC machine tool spindle according to a unified time reference to achieve synchronization with other multi-source operating parameters, performing abnormal data suppression processing on the resampled rotation speed signal, including detecting and correcting or removing abnormal values, and using a sliding window for smoothing processing to output a smooth and reliable rotation speed signal of the i-th fully automatic CNC machine tool spindle. The spindle output torque processing unit is used to process the output torque of the i-th fully automatic CNC machine tool spindle. Specifically, it resamples the collected output torque of the i-th fully automatic CNC machine tool spindle according to a unified time base, performs abnormal data suppression processing on the resampled torque signal, including detecting and correcting or eliminating abnormal values, and smoothing the signal using a sliding window or filtering method; and outputs a smooth and reliable output torque signal of the i-th fully automatic CNC machine tool spindle.

5. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 4, characterized in that, The torque limiting residual quantity processing unit is used to process the torque limiting residual quantity of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected torque limiting residual quantity of the i-th fully automatic CNC machine tool spindle according to a unified time base, performing abnormal data suppression processing on the resampled limiting residual quantity signal, including detecting and correcting or removing outliers, and smoothing the signal using a sliding window or filtering method; and outputting a smooth and reliable torque limiting residual quantity signal of the i-th fully automatic CNC machine tool spindle. The instantaneous displacement value processing unit of the spindle is used to process the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle. Specifically, it includes resampling the collected instantaneous displacement value of the i-th fully automatic CNC machine tool spindle according to a unified time reference, performing abnormal data suppression processing on the resampled instantaneous displacement signal, including detecting and correcting or removing abnormal values, and smoothing the signal through a sliding window or filtering method; and outputting a smooth and reliable instantaneous displacement value signal of the i-th fully automatic CNC machine tool spindle. The coolant pressure value processing unit is used to process the coolant pressure value of the i-th fully automatic CNC machine tool. Specifically, it includes resampling the collected coolant pressure value of the i-th fully automatic CNC machine tool according to a unified time reference, performing abnormal data suppression processing on the resampled pressure signal, including detecting and correcting or removing abnormal values, and smoothing the signal through a sliding window or filtering method to output a smooth and reliable coolant pressure value signal of the i-th fully automatic CNC machine tool.

6. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 5, characterized in that, The artificial intelligence model building module includes a modeling unit; The modeling unit is used to construct an artificial intelligence model using a convolutional neural network, train and test the artificial intelligence model with a machine tool operation dataset, and use the trained artificial intelligence model as the operation status evaluation model of the i-th fully automatic CNC machine tool. At the same time, the machine tool operation dataset output of the equipment operation artificial intelligence model is used as a feature vector to identify feature information, and the trained artificial intelligence model is used as a data operation prediction.

7. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 6, characterized in that, The operating status analysis module includes a spindle load adaptation calculation unit, a load evaluation unit, a torque margin calculation unit, a torque margin evaluation unit, a cooling coupling calculation unit, and a cooling coupling evaluation unit. The spindle load adaptation calculation unit is used to calculate the spindle rotation speed of the i-th fully automatic CNC machine tool in the machine tool operation dataset. Spindle output torque and the instantaneous displacement value of the spindle The spindle load adaptation coefficient of the i-th fully automatic CNC machine tool is obtained by calculation. ; The load assessment unit is used to preset the load threshold Q and to set the load adaptation coefficient of the i-th fully automatic CNC machine tool spindle. Compare with the load threshold Q; when When the value is greater than Q, it indicates that the current spindle load of the i-th fully automatic CNC machine tool is normal, and the current parameters should be maintained to continue operation; when When ≤Q, it indicates that the current spindle load of the i-th fully automatic CNC machine tool is abnormal. The spindle speed of the i-th fully automatic CNC machine tool needs to be reduced by 8%-20% based on the current setting value, and the feed rate should be reduced by 17%-34% at the same time, and the rapid traverse speed should be further reduced by 5%-15%.

8. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 7, characterized in that, The torque margin calculation unit is used to calculate the output torque of the i-th fully automatic CNC machine tool spindle in the machine tool operation data set. and torque limiting remaining amount The torque margin coefficient of the i-th fully automatic CNC machine tool is obtained by calculation. ; ； The torque margin evaluation unit is used to preset the torque margin threshold F and to set the torque margin coefficient of the i-th fully automatic CNC machine tool. Compare with the torque margin threshold F; when When the value is greater than F, it indicates that the spindle torque of the i-th fully automatic CNC machine tool is normal, and the current parameters should be maintained for continued operation. when When ≤F, it indicates that the spindle torque of the i-th fully automatic CNC machine tool is abnormal. The spindle speed needs to be reduced by 11%-26% from the current setting value, and the feed rate should be reduced by 14%-33% and the depth of cut should be reduced by 12%-20% simultaneously.

9. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 8, characterized in that, The cooling coupling calculation unit is used to calculate the instantaneous displacement value of the i-th fully automatic CNC machine tool spindle in the machine tool operation dataset. and coolant pressure value The cooling coupling influence coefficient of the i-th fully automatic CNC machine tool is obtained by calculation. ; The cooling coupling evaluation unit is used to preset the cooling coupling threshold Z and to set the cooling coupling influence coefficient of the i-th fully automatic CNC machine tool. Compare with the cooling coupling threshold Z; when When the value is greater than Z, it indicates that the i-th fully automatic CNC machine tool is currently in a normal cooling state and is maintaining its current parameters. when When Z ≤ Z, it indicates that the current cooling status of the i-th fully automatic CNC machine tool is abnormal. The coolant pressure of the i-th fully automatic CNC machine tool needs to be increased by 9%-27%, and the spindle speed should be reduced by 3%-15% and the feed rate should be reduced by 8%-22% simultaneously.

10. The artificial intelligence-based multi-source signal processing and operation status monitoring system for industrial equipment according to claim 9, characterized in that, The operation status determination module includes an association unit and an analysis unit; The associated unit is used to adjust the spindle load adaptation coefficient of the i-th fully automatic CNC machine tool. Torque margin coefficient Coupling effect coefficient of cooling The correlation is established, and the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool is obtained through calculation. ; ； The analysis unit is used to preset the comprehensive working condition matching threshold AL, and to set the comprehensive working condition matching coefficient of the i-th fully automatic CNC machine tool. Compare with the comprehensive operating condition matching threshold AL; when When >AL, it indicates that the current working condition of the i-th fully automatic CNC machine tool is normal, and it will continue to operate with the current machining parameters. when When ≤AL, it indicates that the current working condition of the i-th fully automatic CNC machine tool is abnormal. The spindle speed of the i-th fully automatic CNC machine tool is reduced by 5%-20%, the feed rate is reduced by 10%-30%, and in some embodiments, the coolant pressure is increased by 6%-31%.

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