An intelligent remote control and monitoring system for AC servo motors

Abstract

The present invention discloses an intelligent remote control and monitoring system for an AC servo motor, which relates to the technical field of motor control and monitoring. The system collects operation data in real time through an integrated sensor installed on the servo motor and transmits it to a remote monitoring database through a wireless network. By preprocessing and storing the data, the magnetic field intensity change coefficient Cbh, the torque fluctuation amplitude coefficient Nbd, and the current harmonic component influence coefficient Dlx are calculated, and the comprehensive fault index DGZ of the AC servo motor is calculated through a fault evaluation module and compared with a preset AC servo motor fault threshold A for preliminary evaluation. The fault probability is predicted through logical regression of historical data and real-time data and is subjected to a secondary evaluation with a preset fault prediction threshold Z. According to the evaluation results, a regulation instruction is generated and transmitted to the servo motor through a wireless network to realize intelligent adjustment and remote control of the motor operation state.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control and monitoring, and specifically to an intelligent remote control and monitoring system for an AC servo motor. Background Art

[0002] In the field of modern industrial automation, AC servo motors have become one of the key components of industrial drive equipment. With the rapid development of industrial technology and the popularization of intelligent manufacturing, traditional manual operation and regular maintenance methods can no longer meet the needs of contemporary high-efficiency production. In order to improve the stability of production equipment, reduce labor maintenance costs, and prevent sudden failures from interfering with the production process, intelligent remote control and monitoring systems have gradually become an indispensable technical means.

[0003] Fault monitoring and regulation are important issues that need to be urgently solved in the current operation of AC servo motors. Traditional motor fault monitoring mainly relies on manual regular maintenance and simple alarm systems. This method not only has a slow response speed but also is difficult to detect potential hidden dangers in a timely manner, which may lead to abnormal equipment operation or even shutdown. In addition, traditional methods lack intelligent analysis means in fault cause diagnosis and are not easy to comprehensively evaluate based on multi-dimensional data. They can only judge problems through single-parameter alarms, which not only increases maintenance costs but also may shorten the service life of equipment, affecting production efficiency and equipment stability. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent remote control and monitoring system for an AC servo motor, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent remote control and monitoring system for an AC servo motor, including an operating data acquisition module, a database processing module, a data integration module, a fault assessment module, a comprehensive fault prediction module, and a remote monitoring and control module;

[0006] The operating data acquisition module collects the operating data of the AC servo motor during operation in real time through an integrated sensor group installed at various positions of the AC servo motor, and transmits it to the remote monitoring database through a wireless network;

[0007] The database processing module is used to preprocess the operating data in the database, obtain the working data group of the AC servo motor, store the working data group of the AC servo motor in the remote monitoring database in real time, and extract the real-time working data group of the AC servo motor;

[0008] The data integration module is used to summarize and calculate the extracted real-time working data group of the AC servo motor to obtain the magnetic field strength change coefficient Cbh, the torque fluctuation amplitude coefficient Nbd, and the current harmonic component influence coefficient Dlx;

[0009] The fault evaluation module is used to perform summary calculations based on the obtained magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx, obtain the comprehensive fault index DGZ of the AC servo motor, and conduct a preliminary comparison and evaluation with the preset AC servo motor fault threshold A. According to the evaluation results, the second evaluation mechanism process is started;

[0010] The comprehensive fault prediction module is used to perform logical regression through the operation data of historical AC servo motors when the initial evaluation result is that the AC servo motor is operating normally, combine it with the comprehensive fault index DGZ of the AC servo motor to predict the fault probability, construct the AC servo motor fault probability prediction index DJL, and conduct a secondary in-depth prediction evaluation with the preset fault prediction threshold Z. According to the evaluation results, relevant regulation and warning instructions are generated;

[0011] The remote monitoring and control module is used to compare and correct the unqualified data of the two evaluations based on the historical normal data in the remote monitoring database, and transmit the corrected parameters to the AC servo motor through a wireless network by generating a regulation instruction.

[0012] Preferably, the operation data acquisition module includes a data acquisition unit and a data transmission unit;

[0013] The data acquisition unit is used to collect the operation data of the AC servo motor during operation in real time through an integrated sensor group installed at various positions of the AC servo motor; the integrated sensor group includes a Hall effect sensor, a current sensor, a rotary encoder, a torque sensor, a voltage sensor, and an acceleration sensor;

[0014] The data transmission unit transmits the operation data of the AC servo motor to the remote monitoring database in real time through a wireless network.

[0015] Preferably, the database processing module includes a data preprocessing unit and a data storage unit;

[0016] The data preprocessing unit is used to receive the operation data of the AC servo motor transmitted by the data transmission unit in real time, and perform data cleaning, data fusion, real-time monitoring, noise filtering, and normalization processing on the operation data of the AC servo motor to obtain the AC servo motor working data group;

[0017] The AC servo motor working data group includes a magnetic field strength change influence data group, an AC servo motor torque data group, and a current harmonic data group;

[0018] The magnetic field strength change influence data group includes magnetic field strength cp, current density dm, and rotor angular velocity zj;

[0019] The AC servo motor torque data set includes the output torque nj, the moment of inertia gl, and the rotor angular acceleration zj;

[0020] The current harmonic data set includes the current harmonic amplitude dl, the fundamental frequency current jd, the voltage harmonic amplitude dy, and the effective voltage value yd;

[0021] The data storage unit stores the AC servo motor working data set in the real-time data repository in real time through a remote monitoring database divided into a real-time data repository and a historical data repository. When new data is stored in the real-time data repository, the database will automatically transfer the previous set of data to the historical data repository for storage.

[0022] Preferably, the data integration module includes a magnetic field strength calculation unit, a torque fluctuation amplitude calculation unit, and a current harmonic component influence calculation unit;

[0023] The magnetic field strength calculation unit is used to perform a summary calculation based on the obtained magnetic field strength change influence data set to obtain the magnetic field strength change coefficient Cbh;

[0024] The magnetic field strength change coefficient Cbh is obtained through the following formula;

[0025]

[0026] In the formula, T represents the total duration of the monitoring period, and dt represents the differential symbol in calculus;

[0027] The torque fluctuation amplitude calculation unit is used to perform a summary calculation based on the obtained AC servo motor torque data set to obtain the torque fluctuation amplitude coefficient Nbd;

[0028] The torque fluctuation amplitude coefficient Nbd is obtained through the following formula;

[0029]

[0030] In the formula, max(nj) and min(nj) respectively represent the upper limit value and the lower limit value of the output torque nj, and max(nj) - min(nj) represents the maximum fluctuation amplitude of the output torque nj;

[0031] Preferably, the current harmonic component influence calculation unit is used to perform a summary calculation based on the obtained current harmonic data set to obtain the current harmonic component influence coefficient Dlx;

[0032] The current harmonic component influence coefficient Dlx is obtained through the following formula;

[0033]

[0034] In the formula, k represents the weight coefficient.

[0035] Preferably, the fault evaluation module includes an AC servo motor fault calculation unit and an AC servo motor fault evaluation unit;

[0036] The AC servo motor fault calculation unit is used to perform a summary calculation based on the obtained magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx to obtain the comprehensive fault index DGZ of the AC servo motor;

[0037] The comprehensive fault index DGZ of the AC servo motor is obtained through the following formula;

[0038]

[0039] In the formula, w1, w2, and w3 respectively represent the weight coefficients of the magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx, and w1 + w2 + w3 = 1. Their specific values are set by the user according to the actual situation. exp(-Cbh) represents the negative exponential function of the magnetic field strength change coefficient Cbh.

[0040] Preferably, the AC servo motor fault evaluation unit preliminarily compares and evaluates the preset AC servo motor fault threshold A with the obtained comprehensive fault index DGZ of the AC servo motor, and generates an alarm message according to the evaluation result. The specific evaluation scheme is as follows;

[0041] When the comprehensive fault index DGZ of the AC servo motor ≤ the preset AC servo motor fault threshold A, it indicates that the AC servo motor is operating normally. At this time, no adjustment is required, and a secondary evaluation mechanism is triggered;

[0042] When the comprehensive fault index DGZ of the AC servo motor > the preset AC servo motor fault threshold A, it indicates that the AC servo motor is operating abnormally. At this time, a first warning message is triggered, and the first warning message is transmitted to the remote monitoring and control module for regulation.

[0043] Preferably, the comprehensive fault prediction module includes an AC servo motor fault prediction calculation unit and an AC servo motor hidden danger prediction and evaluation unit;

[0044] The AC servo motor fault prediction calculation unit is used to perform a logical regression on the historical AC servo motor operation data and the time change rate of the real-time monitoring data when the initial evaluation result is that the AC servo motor is operating normally, and combine it with the comprehensive fault index DGZ of the AC servo motor to predict the fault probability, and construct an AC servo motor fault probability prediction index DJL;

[0045]

[0046] In the formula, β0 represents the intercept term, which represents the predicted probability when all eigenvalues are zero, and β1 represents the regression coefficient of the comprehensive fault index DGZ of the AC servo motor.

[0047] Preferably, the hidden danger prediction and evaluation unit of the AC servo motor is used to conduct a secondary comparison and evaluation between the preset fault prediction threshold Z and the obtained fault probability prediction index DJL of the AC servo motor, and further analyze and predict the hidden dangers of the AC servo motor. The specific evaluation scheme is as follows;

[0048] When the fault probability prediction index DJL of the AC servo motor ≤ the preset fault prediction threshold Z, it indicates that there are no hidden dangers in the AC servo motor, and continuous monitoring is maintained;

[0049] When the fault probability prediction index DJL of the AC servo motor > the preset fault prediction threshold Z, it indicates that there are hidden dangers in the AC servo motor. At this time, a second warning message is triggered, and the second warning message is transmitted to the remote monitoring and control module for regulation.

[0050] Preferably, the remote monitoring and control module includes an instruction generation unit and a remote control execution unit;

[0051] The instruction generation unit transmits the abnormal operation results of the AC servo motor generated by the initial evaluation and the secondary evaluation to the data storage unit. The data storage unit marks the current collected working data group of this set of AC servo motors as fault data, and then compares and corrects it with the average value of the normal data that has not been marked in the historical data. Further, the control input value is adjusted through a PID controller, and the corrected parameters are used to generate a regulation instruction, which is transmitted to the control unit of the AC servo motor through a wireless network;

[0052] The remote control execution unit receives the regulation instruction in real time through the network communication interface of the AC servo motor, extracts the target parameters and execution conditions of the regulation instruction, and converts the regulation instruction into an execution signal through a programmable logic controller PLC. The execution mechanism of the AC servo motor adjusts the operation state according to the received execution signal and monitors the operation state in real time through an integrated sensor group to generate feedback data.

[0053] The present invention provides an intelligent remote control and monitoring system for an AC servo motor. It has the following beneficial effects:

[0054] (1) The system collects the operation data of the motor in real time through an integrated sensor group and transmits it to a remote monitoring database via a wireless network. The data is preprocessed in the database to ensure accuracy and consistency, forming an effective data set. These data can not only reflect the current working state of the motor, but also calculate the magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx through the data integration module, which provides a solid foundation for further fault assessment and prediction.

[0055] (2) The system conducts a preliminary assessment of the motor's operation status through a fault assessment module, calculates the comprehensive fault index DGZ of the AC servo motor by integrating various coefficients, and makes a preliminary comparison with the preset AC servo motor fault threshold A. If DGZ exceeds the preset AC servo motor fault threshold A, the system will trigger an early warning, indicating that the motor is operating abnormally; if the index is within the normal range, the system will continue to execute the secondary assessment mechanism. By comprehensively evaluating the change trend of historical data and current data, the system also predicts the motor's fault probability through a logistic regression model, generates a fault probability prediction index DJL, so as to make a more in-depth prediction of the motor's future operation status.

[0056] (3) When the system identifies that there may be potential fault hazards in the motor, the remote monitoring and control module will automatically generate control instructions according to the fault assessment results and transmit these instructions to the motor's control unit via a wireless network. The instruction generation module will combine historical normal operation data to correct abnormal parameters and optimize the motor's input control signal through a PID controller. The motor's actuator will then adjust the operation state according to these corrected parameters and feedback real-time data through the sensor group to ensure effective intervention before a fault occurs. Through this intelligent control and closed-loop feedback mechanism, the system realizes preventive maintenance and self-optimization of the motor, greatly improving the reliability and stability of the motor operation, while reducing downtime and maintenance costs. Description of the Drawings

[0057] Figure 1 It is a schematic flow diagram of an intelligent remote control and monitoring system for an AC servo motor of the present invention. Detailed Embodiments

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1

[0060] Please refer toFigure 1 The present invention provides an intelligent remote control and monitoring system for an AC servo motor. To achieve the above objectives, the present invention is implemented through the following technical solutions: It includes an operating data acquisition module, a database processing module, a data integration module, a fault assessment module, a comprehensive fault prediction module, and a remote monitoring and control module;

[0061] The operating data acquisition module uses an integrated sensor group installed at various positions of the AC servo motor to collect the operating data of the AC servo motor during operation in real time, and transmits it to the remote monitoring database through a wireless network;

[0062] The database processing module is used to preprocess the operating data in the database, obtain the operating data group of the AC servo motor, store the operating data group of the AC servo motor in the remote monitoring database in real time, and extract the real-time operating data group of the AC servo motor;

[0063] The data integration module is used to summarize and calculate the extracted real-time operating data group of the AC servo motor to obtain the magnetic field intensity change coefficient Cbh, the torque fluctuation amplitude coefficient Nbd, and the current harmonic component influence coefficient Dlx;

[0064] The fault assessment module is used to summarize and calculate based on the obtained magnetic field intensity change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx to obtain the comprehensive fault index DGZ of the AC servo motor, and conduct a preliminary comparison and assessment with the preset AC servo motor fault threshold A. According to the assessment result, start the second assessment mechanism process;

[0065] When the initial assessment result is that the AC servo motor is operating normally, the comprehensive fault prediction module conducts logical regression through the operating data of the historical AC servo motor, combines it with the comprehensive fault index DGZ of the AC servo motor to predict the fault probability, constructs the AC servo motor fault probability prediction index DJL, and conducts a secondary in-depth prediction assessment with the preset fault prediction threshold Z. According to the assessment result, generate relevant regulation and warning instructions;

[0066] The remote monitoring and control module is used to compare and correct the unqualified data of the two assessments based on the historical normal data in the remote monitoring database, and generate a regulation instruction for the corrected parameters and transmit it to the AC servo motor through a wireless network.

[0067] In this embodiment, the operating data acquisition module relies on an integrated sensor group to collect key data of various parts of the motor in real time, ensuring the immediacy and accuracy of the data during operation, and transmitting it to a remote database through a wireless network. The database processing module cleans and fuses this data to form a standardized motor operating data set, improving the efficiency and reliability of data processing and providing a solid data foundation for subsequent evaluation and prediction. The data integration module summarizes the real-time motor data and extracts the magnetic field intensity change coefficient Cbh, the torque fluctuation amplitude coefficient Nbd, and the current harmonic component influence coefficient Dlx, which can accurately evaluate the stability and health status of the motor operation. The fault evaluation module calculates the comprehensive fault index DGZ of the AC servo motor based on these key coefficients and compares it with the preset AC servo motor fault threshold A to form a preliminary evaluation result. When the motor is operating normally, the system will trigger the comprehensive fault prediction module for in-depth evaluation, combining historical operation data and a logistic regression algorithm to predict the probability of a fault occurring. This dual evaluation mechanism not only improves the fault detection accuracy of the system but also enables the system to have the ability to predict faults in advance, so as to give an early warning before the fault occurs and avoid major losses. The remote monitoring and control module corrects abnormal parameters by comparing them with historical normal data and generates control instructions to achieve automatic adjustment and control. The control instructions are transmitted to the motor control unit through a wireless network, and the motor makes adaptive adjustments according to the feedback to ensure the continuous and stable operation of the system. Overall, this integrated and intelligent fault monitoring and control system significantly improves the stability of motor operation, the accuracy of fault warning, and the convenience of maintenance compared with traditional methods, not only reducing the downtime but also extending the service life of the motor and greatly improving the efficiency and safety of industrial production.

[0068] Embodiment 2

[0069] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The operating data acquisition module includes a data acquisition unit and a data transmission unit;

[0070] The data acquisition unit is used to collect the AC servo motor operation data during the operation of the AC servo motor in real time through an integrated sensor group installed at various positions of the AC servo motor; the integrated sensor group includes a Hall effect sensor, a current sensor, a rotary encoder, a torque sensor, a voltage sensor, and an acceleration sensor;

[0071] The data transmission unit transmits the AC servo motor operation data to the remote monitoring database in real time through a wireless network.

[0072] The database processing module includes a data preprocessing unit and a data storage unit;

[0073] The data preprocessing unit is used to receive the operation data of the AC servo motor transmitted by the data transmission unit in real time, and perform data cleaning, data fusion, real-time monitoring, noise filtering, and normalization processing on the operation data of the AC servo motor to obtain the working data set of the AC servo motor;

[0074] The working data set of the AC servo motor includes the data set of the influence of magnetic field intensity change, the torque data set of the AC servo motor, and the current harmonic data set;

[0075] The data set of the influence of magnetic field intensity change includes magnetic field intensity cp, current density dm, and rotor angular velocity zj;

[0076] The torque data set of the AC servo motor includes output torque nj, moment of inertia gl, and rotor angular acceleration zj;

[0077] The current harmonic data set includes current harmonic amplitude dl, fundamental frequency current jd, voltage harmonic amplitude dy, and effective voltage value yd;

[0078] The data storage unit stores the working data set of the AC servo motor in the real-time data repository in real time through a remote monitoring database divided into a real-time data repository and a historical data repository. When new data is stored in the real-time data repository, the database will automatically transfer the previous set of data to the historical data repository for storage.

[0079] In this embodiment, through the precise design of the operation data acquisition module and the database processing module, the all-round real-time monitoring of the operation state of the AC servo motor is realized. The data acquisition unit relies on a variety of high-precision sensors and can accurately collect the operation parameters during the operation of the motor. The acquisition of such multi-dimensional data ensures a comprehensive understanding of the complex working conditions of the motor. The data transmission unit realizes the instant transmission of data through the wireless network, ensuring that the system can be monitored in real time remotely. The database processing module, through advanced data preprocessing technologies such as data cleaning, normalization, and noise filtering, not only improves the accuracy and reliability of the data, but also can automatically archive and manage the real-time data and historical data, providing a rich reference basis for subsequent analysis and prediction. Generally speaking, this integrated design of data acquisition, transmission, and processing significantly improves the data quality, real-time performance, and monitoring depth of the system, reduces the blind area of equipment failures, and lays a solid foundation for fault prediction and early warning.

[0080] Embodiment 3

[0081] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 Specifically: The data integration module includes a magnetic field intensity calculation unit, a torque fluctuation amplitude calculation unit, and a current harmonic component influence calculation unit;

[0082] The magnetic field strength calculation unit is used to perform a summary calculation based on the obtained magnetic field strength change influence data group to obtain a magnetic field strength change coefficient Cbh;

[0083] The magnetic field strength change coefficient Cbh is obtained through the following formula;

[0084]

[0085] In the formula, T represents the total duration of the monitoring period, and dt represents the differential symbol in calculus;

[0086] The torque fluctuation amplitude calculation unit is used to perform a summary calculation based on the obtained AC servo motor torque data group to obtain a torque fluctuation amplitude coefficient Nbd;

[0087] The torque fluctuation amplitude coefficient Nbd is obtained through the following formula;

[0088]

[0089] In the formula, max(nj) and min(nj) respectively represent the upper limit value and the lower limit value of the output torque nj, and max(nj)-min(nj) represents the maximum fluctuation amplitude of the output torque nj;

[0090] The current harmonic component influence calculation unit is used to perform a summary calculation based on the obtained current harmonic data group to obtain a current harmonic component influence coefficient Dlx;

[0091] The current harmonic component influence coefficient Dlx is obtained through the following formula;

[0092]

[0093] In the formula, k represents a weight coefficient.

[0094] In this embodiment, the data integration module calculates the magnetic field strength change coefficient Cbh, the torque fluctuation amplitude coefficient Nbd, and the current harmonic component influence coefficient Dlx through the magnetic field strength calculation unit, the torque fluctuation amplitude calculation unit, and the current harmonic component influence calculation unit respectively. This refined sub-module calculation method has high precision and can more comprehensively reflect the changes of each core parameter of the AC servo motor during operation. By separately calculating the magnetic field strength, torque fluctuation, and current harmonics, the system can quickly identify any abnormal fluctuations or potential problems, and based on this, perform accurate fault assessment and early warning. This modular design not only improves the sensitivity of the system to different types of motor faults, but also enhances the overall operation monitoring intelligence by summarizing and analyzing these key data, enabling the operating state of the motor to be monitored and regulated in real time and accurately, thereby greatly reducing unexpected shutdowns and maintenance costs.

[0095] Example 4

[0096] This example is an explanatory note based on Example 3. Please refer to Figure 1 , specifically: The fault evaluation module includes an AC servo motor fault calculation unit and an AC servo motor fault evaluation unit;

[0097] The AC servo motor fault calculation unit is used to perform a summary calculation based on the obtained magnetic field intensity change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx to obtain the comprehensive fault index DGZ of the AC servo motor;

[0098] The comprehensive fault index DGZ of the AC servo motor is obtained through the following formula;

[0099]

[0100] In the formula, w1, w2, and w3 respectively represent the weight coefficients of the magnetic field intensity change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx, and w1 + w2 + w3 = 1. Their specific values are set by the user according to the actual situation. exp(-Cbh) represents the negative exponential function of the magnetic field intensity change coefficient Cbh.

[0101] The AC servo motor fault evaluation unit preliminarily compares and evaluates the preset AC servo motor fault threshold A with the obtained comprehensive fault index DGZ of the AC servo motor, and generates an alarm message according to the evaluation result. The specific evaluation scheme is as follows;

[0102] When the comprehensive fault index DGZ of the AC servo motor ≤ the preset AC servo motor fault threshold A, it indicates that the AC servo motor is operating normally. At this time, no adjustment is required, and a secondary evaluation mechanism is triggered;

[0103] When the comprehensive fault index DGZ of the AC servo motor > the preset AC servo motor fault threshold A, it indicates that the AC servo motor is operating abnormally. At this time, a first warning message is triggered and transmitted to the remote monitoring and control module for regulation.

[0104] In this embodiment, the operating state of the AC servo motor can be accurately monitored and evaluated in real time. The fault calculation unit aggregates the magnetic field strength change coefficient Cbh, the torque fluctuation amplitude coefficient Nbd, and the current harmonic component influence coefficient Dlx to obtain the comprehensive fault index DGZ. Combining with the weight coefficient set by the user, this index can flexibly adjust the evaluation model to adapt to different operating conditions. By comparing the comprehensive fault index DGZ with the preset AC servo motor fault threshold A, the system can timely detect whether there are potential faults in the motor. When the comprehensive fault index DGZ exceeds the preset threshold, the system automatically triggers an alarm, responds quickly and transmits it to the remote monitoring module for regulation. This mechanism not only realizes efficient fault detection and early warning, but also ensures that the system can intervene in time before potential problems develop into serious faults, reducing downtime and maintenance costs, and significantly improving the operating safety and stability of the motor system.

[0105] Embodiment 5

[0106] This embodiment is an explanatory description based on Embodiment 4. Please refer to Figure 1 , specifically: The comprehensive fault prediction module includes an AC servo motor fault prediction calculation unit and an AC servo motor hidden danger prediction and evaluation unit;

[0107] The AC servo motor fault prediction calculation unit is used to perform logistic regression on the historical AC servo motor operation data and the time change rate of real-time monitoring data when the initial evaluation result is that the AC servo motor is operating normally, and combine it with the comprehensive fault index DGZ of the AC servo motor to predict the fault probability, and construct the AC servo motor fault probability prediction index DJL;

[0108]

[0109] In the formula, β0 represents the intercept term, indicating the prediction probability when all feature values are zero, and β1 represents the regression coefficient of the comprehensive fault index DGZ of the AC servo motor.

[0110] The AC servo motor hidden danger prediction and evaluation unit is used to perform a secondary comparison and evaluation of the preset fault prediction threshold Z and the obtained AC servo motor fault probability prediction index DJL, and further analyze and predict the fault hidden danger of the AC servo motor. The specific evaluation scheme is as follows;

[0111] When the AC servo motor fault probability prediction index DJL ≤ the preset fault prediction threshold Z, it means that there are no fault hidden dangers in the AC servo motor, and continuous monitoring is maintained;

[0112] When the fault probability prediction index DJL of the AC servo motor is greater than the preset fault prediction threshold Z, it indicates that there are potential fault hazards in the AC servo motor. At this time, the second warning information is triggered and transmitted to the remote monitoring and control module for regulation.

[0113] In this embodiment, the comprehensive fault prediction module constructs the fault probability prediction index DJL of the AC servo motor by combining historical data and real-time monitoring data and using a logistic regression model. The core advantage of this module lies in its dual evaluation mechanism: First, by analyzing the relationship between the comprehensive fault index DGZ of the motor and historical operation data through logistic regression, the potential fault probability can be accurately predicted. Second, by comparing the prediction index DJL with the preset fault prediction threshold Z for a second time, the system can deeply evaluate the potential fault hazards of the motor. When the fault probability prediction index DJL of the AC servo motor exceeds the preset fault prediction threshold Z, the system can trigger the second-level warning in a timely manner and generate corresponding regulation instructions. This method not only improves the accuracy of fault prediction but also ensures early warning of motor potential hazards, thus significantly reducing the risk of faults, improving the operating reliability and maintenance efficiency of the motor, and providing a strong guarantee for the stable operation of industrial equipment.

[0114] Embodiment 6

[0115] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The remote monitoring and control module includes an instruction generation unit and a remote control execution unit;

[0116] The instruction generation unit transmits the abnormal operation results of the AC servo motor generated by the initial evaluation and the secondary evaluation to the data storage unit. The data storage unit labels this set of AC servo motor working data collected currently as fault data, and then compares and corrects it with the average value of the normal data that has not been labeled in the historical data. Further, the control input value is adjusted through a PID controller, and the corrected parameters are used to generate a regulation instruction, which is transmitted to the control unit of the AC servo motor through a wireless network.

[0117] The remote control execution unit receives the regulation instruction in real time through the AC servo motor network communication interface, extracts the target parameters and execution conditions of the regulation instruction, and converts the regulation instruction into an execution signal through a programmable logic controller (PLC). The execution mechanism of the AC servo motor adjusts the operating state according to the received execution signal and real-time monitors the operating state through an integrated sensor group to generate feedback data.

[0118] In this embodiment, through the instruction generation unit and the remote control execution unit of the remote monitoring and control module, the intelligent monitoring system of the AC servo motor realizes precise fault correction and adaptive control. Based on the primary and secondary evaluations, the instruction generation unit optimizes the control input value by using a PID controller through the annotation of fault data and the comparison and correction with normal data, thereby generating precise regulation instructions. These instructions are transmitted to the motor control unit through a wireless network to ensure the real-time and effectiveness of the adjustment. The remote control execution unit then converts the regulation instructions into execution signals through a PLC, adjusts the operating state of the motor in real time, and monitors the feedback data through an integrated sensor group. This refined control not only improves the system's ability to adjust the operating state of the motor, but also significantly enhances the rapidity of fault response and the accuracy of repair, ensuring the stable operation of the motor and the maximization of production efficiency.

[0119] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent remote control and monitoring system for an AC servo motor, characterized in that: It includes an operating data acquisition module, a database processing module, a data integration module, a fault assessment module, a comprehensive fault prediction module, and a remote monitoring and control module; The operating data acquisition module collects the operating data of the AC servo motor during operation in real time through an integrated sensor group installed at various positions of the AC servo motor, and transmits it to the remote monitoring database through a wireless network; The database processing module is used to preprocess the operating data in the database, obtain the working data group of the AC servo motor, store the working data group of the AC servo motor in the remote monitoring database in real time, and extract the real-time working data group of the AC servo motor; The working data group of the AC servo motor includes a magnetic field strength change influence data group, an AC servo motor torque data group, and a current harmonic data group; The magnetic field strength change influence data group includes magnetic field strength cp, current density dm, and rotor angular velocity zj; The AC servo motor torque data group includes output torque nj, moment of inertia gl, and rotor angular acceleration zj; The current harmonic data group includes current harmonic amplitude dl, fundamental frequency current jd, voltage harmonic amplitude dy, and effective voltage value yd; The data integration module is used to perform summary calculations on the extracted real-time working data group of the AC servo motor to obtain a magnetic field strength change coefficient Cbh, a torque fluctuation amplitude coefficient Nbd, and a current harmonic component influence coefficient Dlx; In the formula, T represents the total duration of the monitoring period, dt represents the differential symbol in calculus, max(nj) and min(nj) respectively represent the upper limit value and the lower limit value of the output torque nj, max(nj)-min(nj) represents the maximum fluctuation amplitude of the output torque nj, and k represents the weight coefficient; The fault assessment module is used to perform summary calculations based on the obtained magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx to obtain the comprehensive fault index DGZ of the AC servo motor, and conduct a preliminary comparison and assessment with the preset AC servo motor fault threshold A; When the initial assessment result is that the AC servo motor is operating normally, the comprehensive fault prediction module performs logistic regression through the operating data of the historical AC servo motor, combines it with the comprehensive fault index DGZ of the AC servo motor to predict the fault probability, constructs a fault probability prediction index DJL of the AC servo motor, and conducts a secondary in-depth prediction assessment with the preset fault prediction threshold Z; In the formula, β0 represents the intercept term, representing the prediction probability when all feature values are zero, and β1 represents the regression coefficient of the comprehensive fault index DGZ of the AC servo motor; The remote monitoring and control module is used to compare and correct the unqualified data of the two evaluations based on the historical normal data in the remote monitoring database, and generate a control instruction for the corrected parameters and transmit it to the AC servo motor through a wireless network.

2. The intelligent remote control and monitoring system for an AC servo motor according to claim 1, wherein: The operating data acquisition module includes a data collection unit and a data transmission unit; The data acquisition unit is used to collect the operation data of the AC servo motor in real time through an integrated sensor group installed at various positions of the AC servo motor; the integrated sensor group includes a Hall effect sensor, a current sensor, a rotary encoder, a torque sensor, a voltage sensor, and an acceleration sensor; The data transmission unit transmits the operation data of the AC servo motor to the remote monitoring database in real time through a wireless network.

3. An intelligent remote control and monitoring system for an AC servo motor according to claim 2, characterized in that: The database processing module includes a data preprocessing unit and a data storage unit; The data preprocessing unit is used to receive the operation data of the AC servo motor transmitted by the data transmission unit in real time, and perform data cleaning, data fusion, real-time monitoring, noise filtering, and normalization processing on the operation data of the AC servo motor to obtain the working data group of the AC servo motor; The data storage unit stores the working data group of the AC servo motor in the real-time data repository of the remote monitoring database, which is divided into a real-time data repository and a historical data repository. When new data is stored in the real-time data repository, the database will automatically transfer the previous group of data to the historical data repository for storage.

4. An intelligent remote control and monitoring system for an AC servo motor according to claim 3, characterized in that: The data integration module includes a magnetic field strength calculation unit, a torque fluctuation amplitude calculation unit, and a current harmonic component influence calculation unit; The magnetic field strength calculation unit is used to perform a summary calculation based on the obtained magnetic field strength change influence data group to obtain the magnetic field strength change coefficient Cbh; The torque fluctuation amplitude calculation unit is used to perform a summary calculation based on the obtained torque data group of the AC servo motor to obtain the torque fluctuation amplitude coefficient Nbd.

5. An intelligent remote control and monitoring system for an AC servo motor according to claim 4, characterized in that: The current harmonic component influence calculation unit is used to perform a summary calculation based on the obtained current harmonic data group to obtain the current harmonic component influence coefficient Dlx.

6. An intelligent remote control and monitoring system for an AC servo motor according to claim 5, characterized in that: The fault evaluation module includes an AC servo motor fault calculation unit and an AC servo motor fault evaluation unit; The AC servo motor fault calculation unit is used to perform a summary calculation based on the obtained magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx to obtain the comprehensive fault index DGZ of the AC servo motor; The comprehensive fault index DGZ of the AC servo motor is obtained through the following formula; In the formula, w1, w2, and w3 respectively represent the weight coefficients of the magnetic field strength change coefficient Cbh, torque fluctuation amplitude coefficient Nbd, and current harmonic component influence coefficient Dlx, and w1 + w2 + w3 = 1. Their specific values are set by the user according to the actual situation. exp(-Cbh) represents the negative exponential function of the magnetic field strength change coefficient Cbh.

7. An intelligent remote control and monitoring system for an AC servo motor according to claim 6, characterized in that: The AC servo motor fault evaluation unit makes a preliminary comparison and evaluation between the preset AC servo motor fault threshold A and the obtained comprehensive fault index DGZ of the AC servo motor, and generates an alarm message according to the evaluation result. The specific evaluation scheme is as follows; When the comprehensive fault index DGZ of the AC servo motor ≤ the preset AC servo motor fault threshold A, it indicates that the AC servo motor is operating normally. At this time, no adjustment is required, and a secondary evaluation mechanism is triggered; When the comprehensive fault index DGZ of the AC servo motor is greater than the preset AC servo motor fault threshold A, it indicates that the AC servo motor is operating abnormally. At this time, the first warning message is triggered and transmitted to the remote monitoring and control module for regulation.

8. An intelligent remote control and monitoring system for an AC servo motor according to claim 7, characterized in that: The comprehensive fault prediction module includes an AC servo motor fault prediction calculation unit and an AC servo motor hidden danger prediction and evaluation unit; When the initial evaluation result is that the AC servo motor is operating normally, the AC servo motor fault prediction calculation unit performs logical regression based on the historical AC servo motor operation data and the change rate of real-time monitoring data over time, combines it with the comprehensive fault index DGZ of the AC servo motor to predict the fault probability, and constructs an AC servo motor fault probability prediction index DJL.

9. An intelligent remote control and monitoring system for an AC servo motor according to claim 8, characterized in that: The AC servo motor hidden danger prediction and evaluation unit is used to conduct a secondary comparison and evaluation between the preset fault prediction threshold Z and the obtained AC servo motor fault probability prediction index DJL, and further analyze and predict the fault hidden danger of the AC servo motor. The specific evaluation scheme is as follows; When the AC servo motor fault probability prediction index DJL ≤ the preset fault prediction threshold Z, it indicates that there is no fault hidden danger in the AC servo motor, and continuous monitoring is maintained; When the AC servo motor fault probability prediction index DJL > the preset fault prediction threshold Z, it indicates that there is a fault hidden danger in the AC servo motor. At this time, the second warning message is triggered and transmitted to the remote monitoring and control module for regulation.

10. An intelligent remote control and monitoring system for an AC servo motor according to claim 9, characterized in that: The remote monitoring and control module includes an instruction generation unit and a remote control execution unit; The instruction generation unit transmits the abnormal operation results of the AC servo motor generated by the initial evaluation and the secondary evaluation to the data storage unit. The data storage unit marks the current collected set of AC servo motor working data as fault data, compares and corrects it with the average value of the normal data that has not been marked in the historical data, and further adjusts the control input value through a PID controller. The corrected parameters are used to generate a regulation instruction, which is transmitted to the control unit of the AC servo motor through a wireless network; The remote control execution unit receives the regulation instruction in real time through the AC servo motor network communication interface, extracts the target parameters and execution conditions of the regulation instruction, and converts the regulation instruction into an execution signal through a programmable logic controller PLC. The execution mechanism of the AC servo motor adjusts the operating state according to the received execution signal and real-time monitors the operating state through an integrated sensor group to generate feedback data.

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