Main shaft servo motor performance evaluation method and device adopting data traceability

Abstract

The invention provides a main shaft servo motor performance evaluation method and device adopting data traceability, and relates to the technical field of servo motors, and the method comprises the steps: collecting and obtaining the real-time state data of a motor; based on the motor state registration constraint, state expectation mining is carried out according to a motor control scheme, and motor expectation state data is determined; performing deviation analysis based on the real-time state data of the motor and the expected state data of the motor; data tracing is carried out, and a motor performance evaluation space is established; and if the motor performance evaluation index is smaller than a preset motor performance evaluation index, generating a motor performance early warning signal. Through the motor performance evaluation method and device, the technical problem that the evaluation result is not accurate enough due to the fact that existing motor performance evaluation is difficult to comprehensively identify factors of restrictive performance in a complex scene in the prior art can be solved, and the motor performance evaluation method and device can be used for evaluating the motor performance by collecting real-time data and building a motor performance evaluation space based on motor state registration and deviation analysis. Real-time monitoring and early warning are carried out, and the accuracy of performance evaluation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of servo motors, and in particular to a method and device for evaluating the performance of a spindle servo motor using data traceability. Background Art

[0002] The spindle servo motor is a key component used in machine tools, industrial robots and other equipment. Its main function is to control the speed and position of the spindle. It combines servo control systems and motor technology to provide high-precision speed control, positioning control and dynamic response. Therefore, it is widely used in occasions that require precise processing and high dynamic performance. In a complex working environment, the operating state of the motor is affected by multi-dimensional factors, such as load fluctuations, temperature changes, vibrations, humidity, external interference, etc. The motor performance often changes dynamically under complex load conditions, especially when the load changes suddenly, the high-speed switching, and the temperature fluctuates violently. The transient performance of the motor is difficult to capture accurately. Traditional evaluation methods may perform well under static or stable conditions, but they are not responsive enough to dynamic changes, or they cannot fully consider the impact of these changes on the motor performance, resulting in inaccurate evaluation results and unable to reflect the true performance of the motor in actual operation.

[0003] In summary, there are technical problems in the prior art that the existing motor performance evaluation is difficult to fully capture all state changes of the motor in complex environments in complex scenarios, which affects the dynamic adaptation to different loads and working conditions, resulting in inaccurate evaluation results. Summary of the invention

[0004] The purpose of this application is to provide a spindle servo motor performance evaluation method and device using data traceability, so as to solve the technical problem in the prior art that the existing motor performance evaluation is difficult to fully capture all state changes of the motor in complex environments in complex scenarios, affecting the dynamic adaptation to different loads and working conditions, resulting in inaccurate evaluation results.

[0005] In view of the above problems, the present application provides a spindle servo motor performance evaluation method and device using data traceability.

[0006] In a first aspect, the present application provides a spindle servo motor performance evaluation method using data traceability, wherein the spindle servo motor performance evaluation method using data traceability is implemented by a spindle servo motor performance evaluation device using data traceability, wherein the spindle servo motor performance evaluation method using data traceability includes: collecting real-time operating parameters and real-time environmental parameters of the spindle servo motor to obtain real-time state data of the motor; based on the motor state alignment constraint, performing state expectation mining according to the motor control scheme of the spindle servo motor to determine the motor expected state data; performing deviation analysis based on the motor real-time state data and the motor expected state data to obtain a motor state expected deviation vector; performing data traceability according to the spindle servo motor to build a motor performance evaluation space; based on the motor state expected deviation vector, according to the motor performance evaluation space, calculating a motor performance evaluation index; judging whether the motor performance evaluation index is less than a preset motor performance evaluation index, and generating a motor performance warning signal if the motor performance evaluation index is less than the preset motor performance evaluation index.

[0007] In a second aspect, the present application further provides a spindle servo motor performance evaluation device using data traceability, which is used to execute the spindle servo motor performance evaluation method using data traceability as described in the first aspect, wherein the spindle servo motor performance evaluation device using data traceability includes: a data acquisition module, the data acquisition module is used to collect real-time operating parameters and real-time environmental parameters of the spindle servo motor to obtain real-time state data of the motor; an expectation mining module, the expectation mining module is used to perform state expectation mining based on the motor state alignment constraint and the motor control scheme of the spindle servo motor to determine the motor expected state data; a deviation analysis module, the deviation analysis module is used to A deviation analysis is performed based on the real-time state data of the motor and the expected state data of the motor to obtain an expected deviation vector of the motor state; a data tracing module is used to perform data tracing according to the spindle servo motor and build a motor performance evaluation space; a performance evaluation module is used to calculate a motor performance evaluation index based on the expected deviation vector of the motor state and according to the motor performance evaluation space; a signal generating module is used to determine whether the motor performance evaluation index is less than a preset motor performance evaluation index, and if the motor performance evaluation index is less than the preset motor performance evaluation index, a motor performance warning signal is generated.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting the real-time operating parameters and real-time environmental parameters of the spindle servo motor, the real-time state data of the motor is obtained; based on the motor state registration constraint, the state expectation mining is performed according to the motor control scheme of the spindle servo motor to determine the motor expected state data; based on the real-time state data of the motor and the expected state data of the motor, the deviation analysis is performed to obtain the expected deviation vector of the motor state; data tracing is performed according to the spindle servo motor to build a motor performance evaluation space; based on the expected deviation vector of the motor state, the motor performance evaluation index is calculated according to the motor performance evaluation space; it is determined whether the motor performance evaluation index is less than the preset motor performance evaluation index, and if the motor performance evaluation index is less than the preset motor performance evaluation index, a motor performance warning signal is generated. In other words, by collecting the real-time operating parameters and environmental parameters of the spindle servo motor, and based on the motor state registration and deviation analysis, the motor performance evaluation space is built, so as to perform real-time monitoring and early warning, thereby improving the accuracy of motor performance evaluation.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 A flow chart of a spindle servo motor performance evaluation method using data traceability for this application; Figure 2 This is a schematic diagram of the structure of the spindle servo motor performance evaluation device using data traceability in this application.

[0012] Explanation of the accompanying drawings: data collection module 11, expectation mining module 12, deviation analysis module 13, data tracing module 14, performance evaluation module 15, signal generation module 16. DETAILED DESCRIPTION

[0013] This application solves the technical problem in the prior art that the existing motor performance evaluation is difficult to fully capture all state changes of the motor in complex environments in complex scenarios, which affects the dynamic adaptation to different loads and working conditions, resulting in inaccurate evaluation results by providing a spindle servo motor performance evaluation method and device using data traceability. By collecting the real-time operating parameters and environmental parameters of the spindle servo motor, and building a motor performance evaluation space based on motor state alignment and deviation analysis, real-time monitoring and early warning can be carried out, thereby improving the accuracy of motor performance evaluation.

[0014] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] For example, please refer to the attached Figure 1 The present application provides a spindle servo motor performance evaluation method using data traceability, wherein the spindle servo motor performance evaluation method using data traceability is applied to a spindle servo motor performance evaluation device using data traceability, and the spindle servo motor performance evaluation method using data traceability specifically includes the following steps: Step 1: Collect the real-time operating parameters and real-time environmental parameters of the spindle servo motor to obtain the real-time status data of the motor.

[0016] Specifically, various sensors and monitoring devices are used to obtain key data of the motor during operation and the parameters of the environment in which the motor is located in real time. The spindle servo motor is a motor used for precision control, usually used for the spindle drive of machine tools, and can provide high-precision and high-speed motion control. Real-time operating parameters refer to a series of performance indicators of the motor during operation, reflecting the immediate working state of the motor, including the motor's motion characteristics, load conditions and electrical parameters, such as current, voltage, power, speed, torque, etc. Real-time environmental parameters refer to external factors that affect the operating state of the motor, including ambient temperature, humidity, vibration, electromagnetic interference, etc. The real-time status data of the motor is a combination of operating parameters and environmental parameters, and is a comprehensive description of the current operating status of the motor. By integrating the above two types of data, the actual working state of the motor can be understood in real time, including the performance of the motor under the current load, working environment, input voltage and other conditions. By monitoring the operating parameters of the motor in real time, abnormal conditions such as current fluctuations and temperature increases can be discovered in time, so that maintenance can be carried out in advance to avoid sudden failures.

[0017] Step 2: Based on the motor state registration constraint, perform state expectation mining according to the motor control scheme of the spindle servo motor to determine the motor expected state data.

[0018] Specifically, the normal state data of multiple spindle servo motors of the same type are obtained by accessing the global state record database or historical data set. From the retrieved normal state records, the normal state data samples that meet the motor state alignment constraints are screened out according to the motor control scheme, and added to the motor state expectation mining sample domain to identify the typical working mode or ideal state under the motor control scheme. By analyzing the mined sample domain, its statistical center value (such as the mean or median) is calculated to determine the performance indicators of the motor in the ideal state, that is, the motor expected state data. The motor state alignment constraint refers to the rules or standards that ensure that when comparing different motor states, these states are measured under the same or comparable conditions. The motor control scheme refers to a series of instructions and parameter settings used to guide the operation of the motor. By performing state expectation mining based on the motor state alignment constraints and the motor control scheme, the motor expected state data is determined, which helps to establish a clear performance benchmark.

[0019] Step three: Perform deviation analysis based on the real-time state data of the motor and the expected state data of the motor to obtain an expected deviation vector of the motor state.

[0020] Specifically, the real-time state data of the motor and the expected state data of the motor are vectorized respectively, that is, the multi-dimensional parameters are expressed in vector form, and the real-time state data of the motor and the expected state data of the motor are compared to find the difference between the two, and obtain the expected deviation vector of the motor state. Deviation analysis is to identify the deviation between the current operating state of the motor and the ideal state by calculating the difference between the real-time state vector of the motor and the expected state vector, and includes deviation data in multiple dimensions, such as speed deviation, torque deviation, temperature deviation, etc. By performing deviation analysis based on the real-time state data of the motor and the expected state data of the motor, and obtaining the expected deviation vector of the motor state, the difference between the actual operating state of the motor and the ideal state can be quantitatively understood, which helps to accurately evaluate the performance of the motor, timely discover performance deviations, and take corresponding maintenance or optimization measures.

[0021] Step 4: Conduct data tracing based on the spindle servo motor and build a motor performance evaluation space.

[0022] Specifically, all expected deviation vector records related to the motor state are retrieved from the historical records according to the spindle servo motor, including multiple sample motor state expected deviation vectors. Through confidence optimization, the evaluation records with high confidence are screened out. With the sample motor state expected deviation vector as the horizontal axis and the sample motor performance evaluation index as the vertical axis, the deviation vector and the performance index are mapped into a two-dimensional coordinate system, and a coordinate system for visualizing and analyzing motor performance is established. The state deviation vector record set and the performance evaluation record set are input into the performance evaluation coordinate system, and a three-dimensional evaluation space is generated according to the performance evaluation index corresponding to different deviation vectors, which reflects the performance of the motor under various working conditions. Through the traceability and multi-dimensional analysis of motor data, a motor performance evaluation space is built to achieve all-round monitoring, performance optimization and abnormality detection of the motor operating status.

[0023] Step 5: Based on the motor state expected deviation vector and the motor performance evaluation space, calculate the motor performance evaluation index.

[0024] Specifically, the motor state expected deviation vector is input into the motor performance evaluation space, and the distance between the motor state expected deviation vector and the motor state expected deviation vector of each sample in the motor performance evaluation space is calculated to determine which sample is closest to the current state. Commonly used methods include Euclidean distance, cosine similarity, etc. The purpose of calculating the distance is to find the historical states that are most similar to the current state, so as to obtain the corresponding performance evaluation index. If the distance is small, it means that the current state of the motor is very similar to the state of the historical sample, and the performance evaluation index of the historical sample can be used as a reference. Once the historical sample vector closest to the current motor state is found, the performance evaluation index of the sample is calculated based on the centralized value to obtain the motor performance evaluation index, which identifies the overall performance of the current motor in the running state. By inputting the motor state expected deviation vector into the motor performance evaluation space and calculating the distance between similar vectors, a motor performance evaluation index based on historical data is generated to reflect the current performance of the motor, which helps to understand the overall performance of the motor, timely discover performance deviations, and take corresponding maintenance or optimization measures.

[0025] Step six: determine whether the motor performance evaluation index is less than a preset motor performance evaluation index; if the motor performance evaluation index is less than the preset motor performance evaluation index, generate a motor performance warning signal.

[0026] Specifically, a motor performance evaluation index is pre-set, which represents the minimum acceptable standard for motor performance. If the motor performance index is lower than this preset value, it indicates that the motor may be in an abnormal state or insufficient performance, and there may be potential risks or failures. The motor performance evaluation index is a calculated comprehensive indicator of the current performance of the motor, reflecting the operating status and health of the motor. It is determined whether the motor performance evaluation index is less than the preset motor performance evaluation index. If the performance evaluation index is lower than the threshold, it means that the performance of the motor has deteriorated, the operating state may be unstable or abnormal, and preventive measures need to be taken. At this time, a motor performance warning signal needs to be generated to inform the system or operator that the performance of the motor has dropped to an unsafe state and requires attention or intervention. After the warning signal is generated, the operator is reminded through an alarm or notification that there may be a problem with the motor, requiring the motor status to be checked or maintenance measures to be taken. By monitoring the motor performance evaluation index and generating a warning signal, timely measures can be taken when the motor performance deteriorates to reduce the risk of failure.

[0027] Further, step 2 of this application includes: Based on the spindle servo motor, a global state record retrieval is performed to obtain a plurality of motor normal state records; based on the motor state alignment constraint, the plurality of motor normal state records are mined according to the motor control scheme to establish a motor state expected mining sample domain; based on the motor state expected mining sample domain, a centralized value calculation is performed to generate the motor expected state data.

[0028] Specifically, by accessing the global status record database or historical data set, the operation history data of the spindle servo motor is retrieved and analyzed, and the status records of multiple spindle servo motors of the same model under normal working conditions are retrieved and extracted to obtain the normal status records of multiple spindle servo motors of the same type. Global status records refer to status data that is not limited to a certain motor, but can also retrieve the operation records of the same model of equipment across multiple motors, thereby enhancing the comprehensiveness and representativeness of the data. The normal status record includes the working parameters of the motor under fault-free conditions (such as speed, torque, temperature, current, voltage, etc.), which are the performance of the motor under different working conditions, and are all in normal working condition.

[0029] Use specific algorithms and constraints to analyze the state records of multiple motors under normal working conditions and identify the typical working mode or ideal state under the motor control scheme. Motor state registration constraints are rules or standards that ensure that the state data of different motors can be effectively compared and matched, including time synchronization, dimension unification, data standardization, etc., to ensure that the state data of different motors can be compared and analyzed with each other. The motor control scheme is a series of instructions and parameter settings used to guide the operation of the motor. According to the motor control scheme, the normal state records are mined to establish the motor state expectation mining sample domain, which includes the working numbers under multiple normal states and reflects the performance of multiple motors under various working conditions.

[0030] The motor state expectation mining sample domain is analyzed to calculate a representative centralized value. The representative state value is calculated from multiple normal state records in the sample domain through statistical methods (such as mean, median, mode, etc.), reflecting the expected state of the motor under normal working conditions. The calculation result of the centralized value is the expected state data of the motor, reflecting the "ideal state" of the motor under normal working conditions. For example, suppose a factory uses 10 spindle servo motors of the same model, and retrieves the data under normal working conditions from the operation records of these motors, including parameters such as speed, torque and temperature. According to the motor control scheme, the normal state records of these 10 motors are mined to establish a sample domain containing 1000 data points. Ensure that the timestamps of all records are aligned and the units are consistent, and normalize the data. In this sample domain, the average value of each parameter is calculated to obtain: average speed: 2900rpm, average torque: 48N·m, average temperature: 75℃, and these average values ​​are used as the motor expected state data. By retrieving the global motor state record, the motor state expected sample domain is established, and the motor expected state data is generated through centralized value calculation to accurately predict the future state of the motor.

[0031] Furthermore, the present application also includes the following steps: Traverse the multiple motor normal state records and extract the first motor normal state record, wherein the first motor normal state record includes a first sample motor control scheme and a first motor normal state data sample; perform association analysis on the first sample motor control scheme based on the motor control scheme to obtain a first sample state alignment coefficient; determine whether the first sample state alignment coefficient satisfies the motor state alignment constraint; if the first sample state alignment coefficient satisfies the motor state alignment constraint, add the first motor normal state data sample to the motor state expected mining sample domain.

[0032] Specifically, multiple motor normal state records are traversed, checked one by one, and a specific motor record is selected as the first motor normal state record, including the control scheme of the motor and the corresponding normal state data sample. The normal state record refers to the state data of the motor when it is in a fault-free state and operates according to the predetermined control scheme, including parameters such as speed, torque, temperature, current, and voltage. The control scheme of the first motor is associated with the standard control scheme to obtain the first sample state matching coefficient, which indicates the matching degree between the current motor control scheme and the standard motor control scheme. If this matching coefficient is close to 1, it means that the state of the motor is close to the standard control state and the matching degree is high. Association analysis refers to the use of a specific algorithm to analyze data, the purpose of which is to evaluate the matching degree between the actual control scheme of the motor and the ideal control scheme.

[0033] Determine whether the first sample state registration coefficient satisfies the motor state registration constraint. If the first sample state registration coefficient satisfies the motor state registration constraint, it means that the similarity between the first sample motor control scheme and other motor control schemes has reached an acceptable level. The sample can be used as part of the normal state of the motor. The first motor normal state data sample is added to the motor state expected mining sample domain. The motor state expected mining sample domain is a data set containing multiple motor normal state data samples, which is used to analyze and determine the expected state of the motor. By judging the registration coefficient, it is ensured that only high-quality, standard-compliant data is used to establish the expected state model, thereby improving the representativeness and accuracy of the motor state expected mining sample domain.

[0034] Further, step three of this application includes: Vectorization processing is performed on the real-time state data of the motor to generate a real-time state vector of the motor; vectorization processing is performed on the expected state data of the motor to generate an expected state vector of the motor; and deviation identification is performed on the real-time state vector of the motor based on the expected state vector of the motor to generate an expected deviation vector of the motor state.

[0035] Specifically, the collected real-time state data of the motor is converted into a vector form. Vectorization is a method of converting data into a vector form for mathematical calculation and analysis. Various operating parameters of the motor (such as current, voltage, temperature, speed, etc.) are converted into components in the vector. The obtained real-time state vector of the motor contains all the key performance indicators of the motor at a certain moment. Similarly, the expected state data of the motor is also vectorized to obtain the expected state vector of the motor, which represents the performance indicators of the motor under ideal or standard operating conditions. The difference between the real-time state vector of the motor and the expected state vector is calculated to identify the deviation between the current operating state of the motor and the ideal state. The process of deviation identification can be achieved through vector operations, which is specifically expressed as: motor state expected deviation vector = motor real-time state vector - motor expected state vector. Deviation identification refers to comparing the real-time state vector with the expected state vector, finding the difference between the two, and revealing the performance deviation of the motor. By calculating the deviation of each parameter, it is determined in which aspects of the motor's operating state and the expected state are different.

[0036] In a specific example, assume that the real-time state data of a motor is: speed 1500rpm, torque 10N·m, temperature 60℃, which is converted into vector form: [1500,10,60]. At the same time, according to historical data and motor control scheme, the expected state of the motor is determined to be: speed 1500rpm, torque 12N·m, temperature 55℃, and the expected state vector form is: [1500,12,55]. Calculate the deviation between the real-time state vector and the expected state vector, and the deviation vector is calculated as: [1500-1500,10-12,60-55]=[0,-2,5], indicating that the torque of the motor is 2Nm lower than the expected value and the temperature is 5℃ higher than the expected value in actual operation. The deviation vector reflects the difference between the actual state and the ideal state of the motor. In this way, the working state of the motor is monitored in real time, and adjustments or warnings are triggered when the deviation is too large to ensure that the motor can operate in the best state. Through vectorization processing and deviation identification, the actual deviation of the motor performance is quantified, and the difference between the actual operating state and the ideal state of the motor is effectively identified.

[0037] Further, step 4 of this application includes: A global search of expected deviation records is performed on the spindle servo motor to obtain a motor state expected deviation vector record set; evaluation record retrieval confidence optimization is performed based on the motor state expected deviation vector record set to obtain a motor performance evaluation record set; a motor performance evaluation coordinate system is constructed with the sample motor state expected deviation vector as the horizontal axis and the sample motor performance evaluation index as the vertical axis; the motor state expected deviation vector record set and the motor performance evaluation record set are input into the motor performance evaluation coordinate system to establish the motor performance evaluation space.

[0038] Specifically, the expected deviation records of the spindle servo motor are comprehensively retrieved, including the state deviation data of the motor at different time points. The expected deviation record refers to the record of the difference between the actual operating state and the expected state of the motor in the past period of time. Global retrieval means searching all expected deviation records to analyze the overall trend of motor performance. Through global retrieval, a set of multiple motor state expected deviation vectors, namely the motor state expected deviation vector record set, is obtained, which contains the expected deviation data of the motor at different time points or under different working conditions. Through confidence optimization, the evaluation index with high confidence is screened from each sample motor state expected deviation vector to obtain the motor performance evaluation record set. Confidence optimization ensures that the data used for performance evaluation is high-quality and reliable by evaluating the accuracy and reliability of each record. The motor performance evaluation record set is a set of data obtained after confidence optimization, which reflects the performance evaluation results of the motor at different time points or under different working conditions, and usually includes the performance evaluation index of the motor, such as efficiency, stability, failure rate, etc.

[0039] A two-dimensional coordinate system is established, in which the horizontal axis represents the motor state expected deviation vector and the vertical axis represents the motor performance evaluation index. The motor state expected deviation vector refers to the degree of difference between the actual state of the motor and the expected state, reflecting the state changes of the motor in various dimensions, such as deviations in temperature, speed, torque, etc. The performance evaluation index refers to the performance evaluation of the motor under a specific state, including the overall efficiency, health status, stability, etc. of the motor. The deviation vector and performance index are mapped into a two-dimensional coordinate system, and a coordinate system for visualizing and analyzing motor performance is established, which intuitively displays the relationship between the motor state deviation and performance.

[0040] In a specific example, for example, the expected deviation vectors of a spindle servo motor at different time points are recorded. The expected deviation vector at the first time point is [0.2, 0, 2, -10], the expected deviation vector at the second time point is [0.3, 0, 1, -8], and the expected deviation vector at the third time point is [0.25, 0, 3, -12]. The record set of these expected deviation vectors is obtained through global retrieval, namely [0.2, 0, 2, -10], [0.3, 0, 1, -8], [0.25, 0, 3, -12]. The motor performance evaluation record set is obtained through evaluation record retrieval confidence optimization, time point 1: 90%, time point 2: 95%, time point 3: 85%, indicating that the performance evaluation result at the first time point is 90%, the performance evaluation result at the second time point is 95%, and the performance evaluation result at the third time point is 85%. In the motor performance evaluation coordinate system, these data will be represented as three points, with horizontal coordinates of [0.2, 0, 2, -10], [0.3, 0, 1, -8] and [0.25, 0, 3, -12], and vertical coordinates of 90, 95 and 85. Entering these data points into the motor performance evaluation coordinate system, I see that these three points are located in different positions in the coordinate system, reflecting the performance status of the motor at different time points. Through the coordinate system, you can intuitively see the relationship between motor performance and deviation, and accurately predict motor performance trends based on the data in the evaluation space, thereby optimizing maintenance plans and strategies.

[0041] Furthermore, the present application also includes the following steps: Traverse the motor state expected deviation vector record set and extract the first sample motor state expected deviation vector; perform performance evaluation record retrieval based on the first sample motor state expected deviation vector to obtain a first historical motor performance evaluation index set; perform confidence calculation based on the first historical motor performance evaluation index set to obtain the confidence of each evaluation index; based on the predetermined evaluation index confidence, filter the first historical motor performance evaluation index set according to the confidence of each evaluation index to obtain a first confidence motor performance evaluation index set that meets the predetermined evaluation index confidence; perform centralized value calculation based on the first confidence motor performance evaluation index set to obtain a first sample motor performance evaluation index, and add the first sample motor performance evaluation index to the motor performance evaluation record set.

[0042] Specifically, the motor state expected deviation vector record set is traversed, and a sample motor state expected deviation vector is randomly selected and extracted as the first sample motor state expected deviation vector, which represents the difference between the actual state and the expected state of the sample motor at a certain moment or under a certain working condition. The corresponding performance evaluation index is retrieved in the historical database to obtain the first historical motor performance evaluation index set, including multiple historical motor performance evaluation indexes corresponding to the first sample motor state expected deviation vector, reflecting the performance of the motor at different time points or under different working conditions. The confidence calculation is performed on the first historical motor performance evaluation index set to evaluate the reliability of each historical motor performance evaluation index, which is obtained by comprehensive analysis of multiple factors such as the stability of historical data, error range, and data source reliability. Confidence is a value between 0 and 1, which indicates the degree of confidence in the reliability or accuracy of a data point or result. The higher the confidence, the higher the credibility of the evaluation index in describing the motor performance, and it can be used as a reference more reliably. Through confidence calculation, a confidence value is assigned to each evaluation index in the first historical motor performance evaluation index set to obtain the confidence of each evaluation index.

[0043] A confidence threshold is set in advance, and only historical evaluation indices with confidence higher than the threshold are considered credible. Among the confidences of each evaluation index, the evaluation index higher than or equal to the predetermined evaluation index confidence is screened out to form a first confidence motor performance evaluation index set, which only contains motor performance evaluation indices with confidences that meet the requirements. All evaluation indices in the first confidence motor performance evaluation index set are statistically analyzed to calculate a representative value, i.e., a central value. The central value can be an average, a median, or other statistical indicators, which are used to reflect the central trend of this group of evaluation indices. The first sample motor performance evaluation index obtained by the central value calculation is added to the motor performance evaluation record set as a new data point for motor performance evaluation. The same operation is performed on other sample motor state expected deviation vectors in the motor state expected deviation vector record set, and the obtained sample motor performance evaluation index is added to the motor performance evaluation record set to form a complete motor performance evaluation record set. Through confidence calculation and screening, the quality and reliability of the data used for performance evaluation are improved, ensuring that only high-confidence data is used for performance evaluation, and avoiding the impact of low-quality data on the evaluation results.

[0044] Further, step five of this application includes: The motor state expected deviation vector is input into the motor performance evaluation space, and the distance between the motor state expected deviation vector and each sample motor state expected deviation vector in the motor performance evaluation space is calculated to obtain a plurality of vector target distances; it is determined whether the plurality of vector target distances are less than a vector target distance threshold; if any one of the plurality of vector target distances is less than the vector target distance threshold, an identification vector target distance is generated; and a concentrated value calculation is performed based on the sample motor performance evaluation index corresponding to the identification vector target distance to generate the motor performance evaluation index.

[0045] Specifically, the motor state expected deviation vector is input into the motor performance evaluation space for evaluation, and the distance between the motor state expected deviation vector and each sample motor state expected deviation vector in the motor performance evaluation space is calculated. The distance between the input motor state expected deviation vector and each sample motor state expected deviation vector in the motor performance evaluation space is calculated using a mathematical distance measurement method (such as Euclidean distance, Manhattan distance, etc.), and multiple vector target distances are obtained, which reflect the degree of difference between the input vector and each sample vector in the space. Multiple vector target distances represent the similarity between the current motor state and the historical sample state. The smaller the distance, the closer the current state is to the state of a historical sample. The motor state expected deviation vector is a multidimensional vector that describes the difference between the current running state of the motor and the expected state. The motor performance evaluation space is a multidimensional space established previously, which contains a large number of sample motor state expected deviation vectors and corresponding performance evaluation indexes.

[0046] A critical value, namely the vector target distance threshold, is set in advance to measure whether the current motor state is close enough to the historical sample motor state. It is determined whether each vector target distance is less than the threshold to determine whether the current motor state matches the state of certain samples. If any of the multiple vector target distances is less than the vector target distance threshold, it is considered that the input motor state expected deviation vector is similar to the corresponding sample motor state expected deviation vector, that is, the positions of the two in the motor performance evaluation space are close. In this case, a representation is generated to indicate that the input motor state expected deviation vector is similar to a sample vector in the motor performance evaluation space. It can be a simple logical value (such as True or False) or a more complex data structure for recording specific similarity information. By identifying the qualified vectors, the most valuable historical sample motor states are screened out to avoid using irrelevant data to affect the evaluation results.

[0047] The sample motor performance evaluation indexes corresponding to the identified vector target distances are collected together, and a representative value, namely the concentrated value, is calculated using a concentrated value calculation method (such as mean, median, etc.). The motor performance evaluation index obtained by concentrated value calculation represents the overall level of the sample motor performance evaluation indexes corresponding to the identified vector target distances, reflecting the overall performance of the current motor under the operating state. The goal of concentrated value calculation is to generate the performance evaluation index of the current motor by summarizing multiple historical evaluation indexes to ensure that the evaluation results are representative and reliable. Through distance calculation, it is ensured that only the historical data closest to the current motor state is used to generate the evaluation results, reducing the error of the evaluation results. The vector target distance threshold ensures that only those sample data with sufficiently high similarity are used for performance evaluation to avoid interference from irrelevant data.

[0048] In summary, the spindle servo motor performance evaluation method using data traceability provided in this application has the following technical effects: By collecting the real-time operating parameters and real-time environmental parameters of the spindle servo motor, the real-time state data of the motor is obtained; based on the motor state registration constraint, the state expectation mining is performed according to the motor control scheme of the spindle servo motor to determine the motor expected state data; based on the real-time state data of the motor and the expected state data of the motor, the deviation analysis is performed to obtain the expected deviation vector of the motor state; data tracing is performed according to the spindle servo motor to build a motor performance evaluation space; based on the expected deviation vector of the motor state, the motor performance evaluation index is calculated according to the motor performance evaluation space; it is determined whether the motor performance evaluation index is less than the preset motor performance evaluation index, and if the motor performance evaluation index is less than the preset motor performance evaluation index, a motor performance warning signal is generated. In other words, by collecting the real-time operating parameters and environmental parameters of the spindle servo motor, and based on the motor state registration and deviation analysis, the motor performance evaluation space is built, so as to perform real-time monitoring and early warning, thereby improving the accuracy of motor performance evaluation.

[0049] Embodiment 2, based on the same inventive concept as the spindle servo motor performance evaluation method using data traceability in the aforementioned embodiment 1, the present application also provides a spindle servo motor performance evaluation device using data traceability, please refer to the attached Figure 2 , the spindle servo motor performance evaluation device using data traceability includes: The data acquisition module 11 is used to collect real-time operating parameters and real-time environmental parameters of the spindle servo motor to obtain real-time status data of the motor.

[0050] The expectation mining module 12 is used to perform state expectation mining based on the motor state registration constraint and the motor control scheme of the spindle servo motor to determine the motor expected state data.

[0051] The deviation analysis module 13 is used to perform deviation analysis based on the real-time state data of the motor and the expected state data of the motor to obtain an expected deviation vector of the motor state.

[0052] The data tracing module 14 is used to perform data tracing according to the spindle servo motor and build a motor performance evaluation space.

[0053] The performance evaluation module 15 is used to calculate the motor performance evaluation index based on the motor state expected deviation vector and the motor performance evaluation space.

[0054] The signal generating module 16 is used to determine whether the motor performance evaluation index is less than a preset motor performance evaluation index, and if the motor performance evaluation index is less than the preset motor performance evaluation index, generate a motor performance warning signal.

[0055] Furthermore, the expectation mining module 12 in the spindle servo motor performance evaluation device using data tracing is also used for: Based on the spindle servo motor, a global state record retrieval is performed to obtain a plurality of motor normal state records; based on the motor state alignment constraint, the plurality of motor normal state records are mined according to the motor control scheme to establish a motor state expected mining sample domain; based on the motor state expected mining sample domain, a centralized value calculation is performed to generate the motor expected state data.

[0056] Furthermore, the expectation mining module 12 in the spindle servo motor performance evaluation device using data tracing is also used for: Traverse the multiple motor normal state records and extract the first motor normal state record, wherein the first motor normal state record includes a first sample motor control scheme and a first motor normal state data sample; perform association analysis on the first sample motor control scheme based on the motor control scheme to obtain a first sample state alignment coefficient; determine whether the first sample state alignment coefficient satisfies the motor state alignment constraint; if the first sample state alignment coefficient satisfies the motor state alignment constraint, add the first motor normal state data sample to the motor state expected mining sample domain.

[0057] Furthermore, the deviation analysis module 13 in the spindle servo motor performance evaluation device using data traceability is also used for: Vectorization processing is performed on the real-time state data of the motor to generate a real-time state vector of the motor; vectorization processing is performed on the expected state data of the motor to generate an expected state vector of the motor; and deviation identification is performed on the real-time state vector of the motor based on the expected state vector of the motor to generate an expected deviation vector of the motor state.

[0058] Furthermore, the data tracing module 14 in the spindle servo motor performance evaluation device using data tracing is also used for: A global search of expected deviation records is performed on the spindle servo motor to obtain a motor state expected deviation vector record set; evaluation record retrieval confidence optimization is performed based on the motor state expected deviation vector record set to obtain a motor performance evaluation record set; a motor performance evaluation coordinate system is constructed with the sample motor state expected deviation vector as the horizontal axis and the sample motor performance evaluation index as the vertical axis; the motor state expected deviation vector record set and the motor performance evaluation record set are input into the motor performance evaluation coordinate system to establish the motor performance evaluation space.

[0059] Furthermore, the data tracing module 14 in the spindle servo motor performance evaluation device using data tracing is also used for: Traverse the motor state expected deviation vector record set and extract the first sample motor state expected deviation vector; perform performance evaluation record retrieval based on the first sample motor state expected deviation vector to obtain a first historical motor performance evaluation index set; perform confidence calculation based on the first historical motor performance evaluation index set to obtain the confidence of each evaluation index; based on the predetermined evaluation index confidence, filter the first historical motor performance evaluation index set according to the confidence of each evaluation index to obtain a first confidence motor performance evaluation index set that meets the predetermined evaluation index confidence; perform centralized value calculation based on the first confidence motor performance evaluation index set to obtain a first sample motor performance evaluation index, and add the first sample motor performance evaluation index to the motor performance evaluation record set.

[0060] Furthermore, the performance evaluation module 15 in the spindle servo motor performance evaluation device using data traceability is also used for: The motor state expected deviation vector is input into the motor performance evaluation space, and the distance between the motor state expected deviation vector and each sample motor state expected deviation vector in the motor performance evaluation space is calculated to obtain a plurality of vector target distances; it is determined whether the plurality of vector target distances are less than a vector target distance threshold; if any one of the plurality of vector target distances is less than the vector target distance threshold, an identification vector target distance is generated; and a concentrated value calculation is performed based on the sample motor performance evaluation index corresponding to the identification vector target distance to generate the motor performance evaluation index.

[0061] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The spindle servo motor performance evaluation method using data traceability and the specific examples in the first embodiment are also applicable to the spindle servo motor performance evaluation device using data traceability in this embodiment. Through the above detailed description of the spindle servo motor performance evaluation method using data traceability, those skilled in the art can clearly know the spindle servo motor performance evaluation device using data traceability in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0062] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A spindle servo motor performance evaluation method using data traceability, characterized in that: include: Collect the real-time operating parameters and real-time environmental parameters of the spindle servo motor to obtain the real-time status data of the motor; Based on the motor state registration constraint, performing state expectation mining according to the motor control scheme of the spindle servo motor to determine the motor expected state data; Perform deviation analysis based on the real-time state data of the motor and the expected state data of the motor to obtain an expected deviation vector of the motor state; According to the spindle servo motor, data tracing is performed to build a motor performance evaluation space; Based on the motor state expected deviation vector and according to the motor performance evaluation space, calculating a motor performance evaluation index; It is determined whether the motor performance evaluation index is less than a preset motor performance evaluation index, and if the motor performance evaluation index is less than the preset motor performance evaluation index, a motor performance warning signal is generated.

2. The spindle servo motor performance evaluation method using data traceability according to claim 1, characterized in that: Based on the motor state registration constraint, state expectation mining is performed according to the motor control scheme of the spindle servo motor to determine the motor expected state data, including: Performing a global status record search based on the spindle servo motor to obtain multiple motor normal status records; Based on the motor state registration constraint, mining the plurality of motor normal state records according to the motor control scheme to establish a motor state expected mining sample domain; The motor expected state data is generated by performing concentrated value calculation based on the motor expected state mining sample domain.

3. The spindle servo motor performance evaluation method using data traceability as claimed in claim 2, characterized in that: Based on the motor state registration constraint, mining the plurality of motor normal state records according to the motor control scheme to establish a motor state expected mining sample domain, including: Traversing the plurality of motor normal state records, extracting a first motor normal state record, wherein the first motor normal state record includes a first sample motor control scheme and a first motor normal state data sample; Performing correlation analysis on the first sample motor control scheme based on the motor control scheme to obtain a first sample state registration coefficient; determining whether the first sample state registration coefficient satisfies the motor state registration constraint; If the first sample state registration coefficient satisfies the motor state registration constraint, the first motor normal state data sample is added to the motor state expected mining sample domain.

4. The spindle servo motor performance evaluation method using data traceability according to claim 1, characterized in that: Performing deviation analysis based on the real-time state data of the motor and the expected state data of the motor to obtain an expected deviation vector of the motor state includes: Performing vectorization processing according to the real-time state data of the motor to generate a real-time state vector of the motor; Performing vectorization processing according to the motor desired state data to generate a motor desired state vector; Based on the motor expected state vector, deviation identification is performed on the motor real-time state vector to generate the motor state expected deviation vector.

5. The spindle servo motor performance evaluation method using data traceability according to claim 1, characterized in that: According to the spindle servo motor, data traceability is performed to build a motor performance evaluation space, including: Perform a global search of the expected deviation record according to the spindle servo motor to obtain a motor state expected deviation vector record set; Performing evaluation record retrieval confidence optimization based on the motor state expected deviation vector record set to obtain a motor performance evaluation record set; A motor performance evaluation coordinate system is constructed with the sample motor state expected deviation vector as the horizontal axis and the sample motor performance evaluation index as the vertical axis; The motor state expected deviation vector record set and the motor performance evaluation record set are input into the motor performance evaluation coordinate system to establish the motor performance evaluation space.

6. The spindle servo motor performance evaluation method using data traceability according to claim 5, characterized in that: Based on the motor state expected deviation vector record set, evaluation record retrieval confidence optimization is performed to obtain a motor performance evaluation record set, including: Traversing the motor state expected deviation vector record set, extracting a first sample motor state expected deviation vector; Retrieving performance evaluation records based on the first sample motor state expected deviation vector to obtain a first historical motor performance evaluation index set; Performing confidence calculation based on the first historical motor performance evaluation index set to obtain confidence of each evaluation index; Based on a predetermined evaluation index confidence, the first historical motor performance evaluation index set is screened according to the confidence of each evaluation index to obtain a first confidence motor performance evaluation index set that meets the predetermined evaluation index confidence; A centralized value calculation is performed according to the first confidence motor performance evaluation index set to obtain a first sample motor performance evaluation index, and the first sample motor performance evaluation index is added to the motor performance evaluation record set.

7. The spindle servo motor performance evaluation method using data traceability according to claim 1, characterized in that: Based on the motor state expected deviation vector and according to the motor performance evaluation space, a motor performance evaluation index is calculated, including: Inputting the motor state expected deviation vector into the motor performance evaluation space, calculating the distance between the motor state expected deviation vector and each sample motor state expected deviation vector in the motor performance evaluation space, and obtaining a plurality of vector target distances; Determining whether the multiple vector target distances are less than a vector target distance threshold; If any one of the plurality of vector target distances is less than the vector target distance threshold, generating an identification vector target distance; The motor performance evaluation index is generated by performing a concentrated value calculation based on the sample motor performance evaluation index corresponding to the identification vector target distance.

8. A spindle servo motor performance evaluation device using data traceability, characterized in that: The method for evaluating the performance of a spindle servo motor using data traceability according to any one of claims 1 to 7 is used, wherein the device for evaluating the performance of a spindle servo motor using data traceability comprises: A data acquisition module, wherein the data acquisition module is used to collect real-time operating parameters and real-time environmental parameters of the spindle servo motor to obtain real-time status data of the motor; An expectation mining module, the expectation mining module is used to perform state expectation mining according to the motor control scheme of the spindle servo motor based on the motor state registration constraint to determine the motor expected state data; A deviation analysis module, the deviation analysis module is used to perform deviation analysis based on the real-time state data of the motor and the expected state data of the motor to obtain an expected deviation vector of the motor state; A data tracing module, wherein the data tracing module is used to perform data tracing according to the spindle servo motor and build a motor performance evaluation space; A performance evaluation module, the performance evaluation module is used to calculate a motor performance evaluation index based on the motor state expected deviation vector and the motor performance evaluation space; A signal generating module is used to determine whether the motor performance evaluation index is less than a preset motor performance evaluation index, and if the motor performance evaluation index is less than the preset motor performance evaluation index, generate a motor performance warning signal.

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