Control method and system for direct-current brushless gear motor

Abstract

The invention relates to the technical field of direct-current brushless gear motor auxiliary control, and discloses a control method and system for a direct-current brushless gear motor, and the method comprises the steps: carrying out the control of the direct-current brushless gear motor based on the real-time data during the starting and stopping of the motor, including detection data (such as temperature, vibration, noise and the like), operation data (such as current, voltage, torque and the like), and load data; and comparing and analyzing with preset historical detection data to judge whether the motor has a fault: when a large data difference is detected, evaluating the severity of the motor fault through a fault scoring mechanism. If the fault score exceeds a preset threshold value, early warning is triggered, and a corresponding control method is selected according to the fault type, so that fault processing or control strategy adjustment is carried out, and stable operation of the motor is ensured. According to the invention, the maintenance and fault diagnosis process of the motor is optimized by combining the fault score and the dynamic adjustment of the control strategy and through the real-time monitoring and analysis of the operation data and the load data, and the efficiency and accuracy of fault response are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of auxiliary control of a brushless DC reduction motor, and in particular to a control method and system for a brushless DC reduction motor. Background Art

[0002] With the rapid development of modern industrial automation, brushless DC motors are widely used in various mechanical equipment, such as automated production lines, robots, aerospace, etc. These motors have the advantages of high efficiency, reliability and low maintenance cost, and have become an important power source, especially in applications that require precise control.

[0003] However, the motor will be affected by many factors during operation, such as current, voltage fluctuations, load changes, ambient temperature, etc. These factors may cause the motor's performance to deteriorate or malfunction. To ensure the stable operation of the motor, timely detection of potential faults and early warning are the key to improving equipment reliability and extending service life. At present, traditional motor fault diagnosis methods usually rely on manual inspection and regular maintenance. It is impossible to monitor the operating status of the motor in real time, and it is difficult to detect potential fault hazards in time. This method not only increases labor costs, but may also miss the best time for maintenance, resulting in increased equipment downtime and affecting production efficiency.

[0004] Therefore, there is an urgent need to invent a control technology for brushless DC reduction motors to solve the problem that traditional motor fault diagnosis methods rely on manual inspection and regular maintenance, are unable to monitor the motor status in real time and detect potential faults in a timely manner, resulting in increased equipment downtime and reduced production efficiency. Summary of the invention

[0005] In view of this, the present invention proposes a control method and system for a brushless DC reduction motor, aiming to solve the problem that traditional motor fault diagnosis methods rely on manual inspection and regular maintenance, are unable to monitor the motor status in real time and detect potential faults in time, resulting in increased equipment downtime and reduced production efficiency.

[0006] The present invention proposes a control method for a brushless DC reduction motor, comprising: The detection data, operation data and load data of the motor when it is started and stopped are obtained, and whether the motor operation fails is determined based on the relationship between the detection data and the preset detection data, where: When it is determined that a fault occurs during the operation of the motor, the fault score of the motor is determined based on the relationship between the detection data and the preset detection data; Determine the fault type of the motor based on the operating data and load data; According to the relationship between the fault score and the configured preset fault score, it is determined whether to issue an early warning for the motor. When the fault score is lower than the preset fault score, the early warning method is determined according to the score difference between the fault score and the preset fault score; when the fault score is higher than or equal to the preset fault score, the control method is determined according to the fault type.

[0007] Furthermore, when setting the preset detection data, it includes: Acquire various historical detection data of the motor, wherein the detection data includes temperature detection data, vibration detection data and noise detection data of the motor; Obtaining the distance measurement between each historical detection data, and establishing a distance matrix for each historical detection data according to the distance measurement; Iteratively cluster the historical detection data according to the distance matrix, and obtain the feature vector of the normal detection data and the feature vector of the abnormal detection data according to the clustering results; Determine preset detection data according to the feature vector of normal detection data; The distance value between the feature vector of the normal detection data and the feature vector of the abnormal detection data is obtained and determined as a preset difference value.

[0008] Further, when determining whether a motor operation fails based on the relationship between the detection data and the preset detection data, the method includes: Obtain the data difference between the detection data and the preset detection data, and determine whether the motor operation fails based on the relationship between each data difference and the preset difference: When the data difference is lower than the preset difference, it is determined that the motor is running without any fault; When the data difference is equal to or higher than the preset difference amount, it is determined that the motor operation fails, and the fault score of the motor is determined based on the relationship between the data difference and the preset difference amount.

[0009] Furthermore, when determining the fault score of the motor according to the relationship between the data difference and the preset difference amount, it includes: ; Among them, S is the fault score of the motor, wi is the weight coefficient of the i-th data item, α i is the nonlinear coefficient of the ith data item, t is the temperature data item, v is the vibration data item, c is the noise data item, e -βi is the smoothing coefficient, △di is the data difference, and △Di is the preset difference amount.

[0010] Furthermore, when obtaining the running data and load data of the motor when starting and stopping, it includes: When obtaining the operating data, it includes obtaining the motor's starting current, starting voltage, starting torque, start-stop time interval, and start-stop speed; When acquiring load data, it includes acquiring the inertial load, friction load, torque load and current load carried by the motor.

[0011] Furthermore, when determining the fault type of the motor according to the operation data and the load data, it includes: Acquire each historical operation data and each historical load data of the motor, and establish a motor fault diagnosis model based on a decision tree, a neural network, and the relationship between each historical operation data and each historical load data; The motor's operating data and load data are substituted into the motor fault diagnosis model to determine the motor's fault type, and the execution operation during control is determined based on the fault type.

[0012] Furthermore, based on the relationship between the decision tree, the neural network, the historical operation data and the historical load data, a motor fault diagnosis model is established, including: Establishing a motor state correlation equation based on historical operation data and corresponding motor states and historical load data and corresponding motor states; Based on the decision tree, the motor state associations are screened to determine the abnormal data between the motor state associations: ; Where A is the contribution value of the motor correlation, H(F) is the entropy of the motor state correlation in the data set F; Fj is the jth subset after the motor state correlation is divided, and K is the total number of data subsets; Get the median contribution value between the contribution values ​​of each motor correlation equation, and determine the abnormal data based on the relationship between the contribution value and the median contribution value: When the contribution value of the motor correlation equation is less than the median value of the contribution value, the historical operation data and the historical load data in the motor correlation equation are determined to be abnormal data; When the contribution value of the motor correlation formula is greater than or equal to the median value of the contribution value, it is determined that the historical operation data and the historical load data in the motor correlation formula are not abnormal data; According to the determined abnormal data, the motor fault type and characteristic vector are determined based on the neural network, and a motor fault diagnosis model is established according to the motor fault type and characteristic vector.

[0013] Further, according to the determined abnormal data, when determining the motor fault type and characteristic vector based on the neural network, it includes: ; Among them, f(X) is abnormal data, is the probability of the Kth fault type, and is the weight coefficient, and and are all non-zero, σ is the activation function, xi is the i-th feature of the feature data, wij is the weight of the input feature xi to the j-th neuron in the hidden layer, bj is the bias of the j-th neuron, h is the number of motor fault types, n is the number of input features, and j is the neuron index of the hidden layer.

[0014] Further, according to the fault type, the execution operation of the control is determined, including: Determine a first execution operation and a second execution operation according to the fault type, control the motor according to the first execution operation, and obtain real-time operation data and real-time load data of the motor during control; Obtaining the running data difference between the real-time running data and the historical running data of the adjacent time periods, and obtaining the load data difference between the real-time load data and the historical load data of the adjacent time periods; According to the relationship between the running data difference and the load data difference, determine whether to use the second execution operation to control the motor: When the operation data difference is lower than or equal to the configured preset operation data difference, and / or the load data difference is lower than or equal to the configured preset load data difference, it is determined not to select the second execution operation to control the motor; When the operation data difference is higher than the preset operation data difference, and / or the load data difference is higher than the preset load data difference, it is determined to select the second execution operation to control the motor.

[0015] Compared with the prior art, the beneficial effect of the present invention is that by obtaining the detection data, operation data and load data of the motor, and comparing these real-time data with the preset detection data, it is possible to efficiently identify whether the motor is abnormal. This method not only avoids the traditional method of relying on manual inspection, but also can perform real-time monitoring at each stage of the motor operation, ensuring that potential faults can be discovered and handled in time at the early stage, reducing the downtime and maintenance costs caused by the fault. In addition, by introducing a fault scoring mechanism, the fault state of the motor can be quantified. When a fault occurs during the operation of the motor, a fault score is calculated based on the relationship between the detection data and the preset data. This score not only helps to determine whether the motor has a fault, but also can accurately assess the severity of the fault. After comparing with the preset fault score, it can be determined whether the early warning mechanism needs to be activated based on the score difference. This early warning method based on the score difference makes the fault diagnosis of the motor more refined and intelligent, avoiding the previous extensive judgment that simply relies on a fixed threshold. Finally, when the fault score is higher than or equal to the preset value, the corresponding control method can be automatically selected and executed according to the fault type. This control method can not only take the most appropriate response measures for different types of faults, but also make dynamic adjustments based on the real-time operating status of the motor to ensure the stability and safety of the motor operation. When dealing with more complex faults, it will automatically select an alternative control scheme and further optimize the motor's control parameters based on historical load data or other relevant data to achieve the best operating state.

[0016] On the other hand, the present application also provides a control system for a brushless DC reduction motor, comprising: An acquisition module is configured to acquire detection data, operation data and load data when the motor is started and stopped; An analysis module is electrically connected to the acquisition module, and the analysis module is configured to determine whether a fault occurs in the operation of the motor according to a relationship between the detection data and the preset detection data, wherein: when it is determined that a fault occurs in the operation of the motor, a fault score of the motor is determined according to the relationship between the detection data and the preset detection data; the analysis module is further configured to determine a fault type of the motor according to the operation data and the load data; The central control module is electrically connected to the analysis module. The central control module is configured to determine whether to issue an early warning for the motor based on the relationship between the fault score and a configured preset fault score. When the fault score is lower than the preset fault score, the score difference between the fault score and the preset fault score is determined to determine the early warning method; when the fault score is higher than or equal to the preset fault score, the control method is determined according to the fault type.

[0017] It can be understood that the control method and system for a brushless DC reduction motor in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of a control method for a brushless DC reduction motor provided by an embodiment of the present invention; Figure 2 A functional block diagram of a control system for a brushless DC reduction motor provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a control method for a brushless DC reduction motor, including: Step S100, obtaining detection data, operation data and load data when the motor is started and stopped, and determining whether the motor operation fails based on the relationship between the detection data and the set preset detection data, wherein: when it is determined that the motor operation fails, the relationship between the detection data and the set preset detection data determines the fault score of the motor.

[0021] Specifically, when setting the preset detection data, it includes: obtaining various historical detection data of the motor, wherein the detection data includes the temperature detection data, vibration detection data and noise detection data of the motor. Obtaining the distance metric between each historical detection data, and establishing a distance matrix for each historical detection data according to the distance metric. Iteratively clustering each historical detection data according to the distance matrix, and according to the clustering results, obtaining the feature vector of the normal detection data and the feature vector of the abnormal detection data. Determine the preset detection data according to the feature vector of the normal detection data. Obtain the distance value between the feature vector of the normal detection data and the feature vector of the abnormal detection data, and determine it as the preset difference value.

[0022] Specifically, when determining whether a motor operation fails according to the relationship between the detection data and the preset detection data, the method includes: obtaining a data difference between the detection data and the preset detection data, and determining whether the motor operation fails according to the relationship between each data difference and the preset difference amount: when the data difference is lower than the preset difference amount, it is determined that the motor operation does not fail. When the data difference is equal to or higher than the preset difference amount, it is determined that the motor operation fails, and the fault score of the motor is determined according to the relationship between the data difference and the preset difference amount.

[0023] Specifically, according to the relationship between the data difference and the preset difference amount, the fault score of the motor is determined, including: ; Among them, S is the fault score of the motor, wi is the weight coefficient of the i-th data item, α i is the nonlinear coefficient of the ith data item, t is the temperature data item, v is the vibration data item, c is the noise data item, e -βi is the smoothing coefficient, △di is the data difference, and △Di is the preset difference amount.

[0024] It is understandable that preset detection data is constructed by historical detection data such as temperature, vibration and noise. After these historical data are processed by distance metric, a distance matrix is ​​formed, and then the data is iteratively clustered to identify normal and abnormal detection data. Through this analysis process, the feature vectors of normal data and abnormal data can be extracted, and these feature vectors are used to determine the preset detection data, thereby providing a basis for subsequent fault judgment and scoring. In the fault diagnosis process, the acquired real-time detection data will be compared with the preset detection data to calculate the data difference. This difference is used to determine whether the operation of the motor is normal. When the difference between the detection data and the preset data is lower than the preset difference amount, it can be determined that there is no fault in the operation of the motor; when the data difference is equal to or higher than the preset difference amount, it is considered that the motor has a fault, and the fault score of the motor is determined by the difference. This method can not only monitor the state of the motor in real time, but also evaluate the health of the motor by dynamically calculating the fault score. Further, in order to more accurately evaluate the motor fault, the calculation of the fault score involves weighted and nonlinear processing of multiple data items. Specifically, for temperature, vibration and noise data, weight coefficients Wi are assigned to these data items respectively, and combined with the nonlinear coefficient α i These data items have different importance in the fault score calculation process. The effects of temperature, vibration and noise are considered separately, so as to reflect the specific situation of the motor fault in a refined manner. This method makes the contribution of different data items to the fault score reasonably reflected. In addition, in order to improve the stability and accuracy of the score, the calculation of the fault score also considers the smoothing coefficient e -βi , which helps to reduce the impact of data fluctuations on the results, making the scoring more stable and reliable. The introduction of this smoothing coefficient effectively suppresses the errors that may be caused by noise data, and further improves the accuracy and stability of fault diagnosis. Through the combination of these mathematical models and coefficients, the fault state of the motor can be more accurately reflected, and a basis for subsequent fault handling can be provided. Finally, through a comprehensive analysis of the relationship between the motor detection data and the preset data, with the help of clustering, weighting, nonlinear adjustment and smoothing processing, the fault degree of the motor can be accurately evaluated in real time and a fault score can be generated.

[0025] It can be seen that by real-time monitoring of the motor's various test data, operation data and load data, combined with the preset standard data, it is possible to effectively determine whether the motor has a fault and score it. This method can help the motor management system to promptly discover potential fault risks, avoid further development of faults, reduce downtime caused by faults, and improve the reliability of the motor. By real-time monitoring and comparison with preset data, the system can issue an early warning before the fault occurs, thereby providing maintenance personnel with an opportunity for early intervention to prevent serious consequences of the fault. In addition, cluster analysis is performed using historical data, combined with distance metrics and clustering results, and feature vectors of normal and abnormal data can be extracted from them, providing a more accurate basis for subsequent fault diagnosis. This method can ensure that the performance of the motor under different working conditions can be accurately identified, and reasonable preset data can be generated based on different types of abnormal data. This data-driven approach reduces the error of human judgment and improves the diagnostic accuracy. The fault scoring mechanism is implemented by calculating the relationship between the data difference and the preset difference amount. This can not only help determine whether the motor has a fault, but also assess the severity of the fault. When the fault score is higher than the preset value, the corresponding control measures can be initiated to prevent the fault from further aggravating, thereby ensuring the stable operation of the motor. This scoring mechanism makes the motor fault diagnosis more intelligent and accurate, avoiding the blindness of traditional methods. In addition, the weighting and nonlinear adjustment methods are adopted to refine the weights of different data items in the fault scoring. Data items such as temperature, vibration and noise have different effects on the health of the motor. The introduction of nonlinear coefficients and weight coefficients can adjust the contribution of each data according to the actual situation, further improving the accuracy of the fault scoring. This enables the entire motor monitoring system to better adapt to various complex working conditions and make accurate judgments. Finally, the smoothing of the fault scoring effectively reduces the noise in the data and avoids misjudgments caused by data fluctuations. Through smoothing, it can better cope with the instantaneous changes in sensor data and ensure the stability and reliability of the scoring.

[0026] Step S200: Determine the fault type of the motor according to the operation data and the load data.

[0027] Step S300: Determine whether to issue an early warning to the motor according to the relationship between the fault score and the preset fault score, wherein when the fault score is lower than the preset fault score, determine the early warning method according to the score difference between the fault score and the preset fault score. When the fault score is higher than or equal to the preset fault score, determine the control method according to the fault type.

[0028] Specifically, when obtaining the operation data and load data of the motor when starting and stopping, it includes: when obtaining the operation data, it includes obtaining the starting current, starting voltage, starting torque, start-stop time interval, and start-stop speed of the motor. When obtaining the load data, it includes obtaining the inertial load, friction load, torque load, and current load carried by the motor.

[0029] Specifically, when determining the fault type of the motor according to the operation data and the load data, it includes: obtaining each historical operation data and each historical load data of the motor, and establishing a motor fault diagnosis model based on the relationship between the decision tree, the neural network, and each historical operation data and each historical load data. Substituting the operation data and the load data of the motor into the motor fault diagnosis model, determining the fault type of the motor, and determining the execution operation during control according to the fault type.

[0030] Specifically, when establishing a motor fault diagnosis model based on the relationship between the decision tree, neural network, historical operation data and historical load data, it includes: establishing a motor state association based on historical operation data and corresponding motor states and historical load data and corresponding motor states. Screening each motor state association based on the decision tree to determine abnormal data between each motor state association: .

[0031] Among them, A is the contribution value of the motor association, H (F) is the entropy of the motor state association in the data set F; Fj is the jth subset after the motor state association is divided, and K is the total number of data subsets; the median contribution value between the contribution values ​​of each motor association is obtained, and the abnormal data is determined according to the relationship between the contribution value and the median contribution value: when the contribution value of the motor association is less than the median contribution value, the historical operation data and the historical load data in the motor association are determined to be abnormal data; when the contribution value of the motor association is greater than or equal to the median contribution value, the historical operation data and the historical load data in the motor association are determined not to be abnormal data; according to the determined abnormal data, the motor fault type and the characteristic vector are determined based on the neural network, and the motor fault diagnosis model is established according to the motor fault type and the characteristic vector.

[0032] Specifically, according to the determined abnormal data, when determining the motor fault type and characteristic vector based on the neural network, it includes: .

[0033] Among them, f(X) is abnormal data, is the probability of the Kth fault type, and is the weight coefficient, and and are all non-zero, σ is the activation function, xi is the i-th feature of the feature data, wij is the weight of the input feature xi to the j-th neuron in the hidden layer, bj is the bias of the j-th neuron, h is the number of motor fault types, n is the number of input features, and j is the neuron index of the hidden layer.

[0034] Specifically, when determining the execution operation during control according to the fault type, it includes: determining the first execution operation and the second execution operation according to the fault type, controlling the motor according to the first execution operation, and obtaining the real-time operation data and real-time load data of the motor during control. Obtain the operation data difference between the real-time operation data and the historical operation data of the adjacent time period, and obtain the load data difference between the real-time load data and the historical load data of the adjacent time period. According to the relationship between the operation data difference and the load data difference, determine whether to use the second execution operation to control the motor: when the operation data difference is lower than or equal to the configured preset operation data difference, and / or the load data difference is lower than or equal to the configured preset load data difference, it is determined not to use the second execution operation to control the motor. When the operation data difference is higher than the preset operation data difference, and / or the load data difference is higher than the preset load data difference, it is determined to use the second execution operation to control the motor.

[0035] It can be understood that by obtaining the running data (such as starting current, starting voltage, starting torque, start-stop time interval, etc.) and load data (such as inertia load, friction load, torque load, current load, etc.) when the motor starts and stops, and combining these data with historical running data and load data, the motor state is modeled by association through the decision tree algorithm. The core of this process is to detect abnormal data by building state associations, and to provide support for subsequent fault diagnosis by screening out data points that deviate greatly from the normal operating state. The motor state association established based on the decision tree helps to determine the abnormal data in the data set, which reflects the abnormal performance of the motor under specific conditions. By establishing the motor state association, the decision tree is used to screen the motor state, calculate the contribution value of each state association, and divide the data by entropy value, so as to determine which data is abnormal data. The basis for determining abnormal data is the comparison between the contribution value of the motor association and the median of its contribution value. If it is less than the median, it is considered to be abnormal data. Then, based on the determined abnormal data, the neural network is used to further analyze the fault type and characteristic vector of the motor, and finally a motor fault diagnosis model is constructed to achieve accurate motor fault identification and prediction. Specifically, the historical operation data and load data are analyzed through the decision tree to establish the motor state association. The motor state association divides the motor data in different time periods and clusters the historical data under each state. Based on these data, the contribution value A of each motor association is calculated, and the complexity of the data set is evaluated by calculating the entropy H (F) of each association. In this process, the data set F is divided into multiple subsets Fj, each subset Fj contains different historical operation data and load data. Then, by calculating the median of the contribution value of each motor association, the abnormal data is further screened out. If the contribution value A of a motor association is less than the median of the contribution value, the historical operation data and historical load data in the motor association are considered to be abnormal data. On the contrary, if the contribution value of the motor association is greater than or equal to the median of the contribution value, it is considered that these data are normal and not abnormal data. In this way, normal data and abnormal data can be effectively distinguished, and more accurate input can be provided for subsequent fault diagnosis. After the abnormal data is selected, these data are input into the neural network model to further classify the fault type of the motor, thereby providing a basis for control decision-making. The application of neural networks in fault type judgment plays a vital role. By training abnormal data, the neural network can output the probability of the fault type and perform nonlinear mapping on the data through the activation function σ, thereby determining the fault type and feature vector of the motor. The key to this process is that through continuous learning and optimization of the model, the specific fault type of the motor can be identified based on historical data and feature vectors, thereby improving the accuracy and real-time performance of fault diagnosis. According to the motor fault type, the motor's execution operation will be determined based on the preset control strategy.The execution operation is divided into a first execution operation and a second execution operation. The first execution operation is a priority operation and is used to perform preliminary control according to the fault type of the motor. During the control process, the motor operation data and load data are obtained in real time and compared with the historical data to determine whether the second execution operation is needed. Specifically, the difference between the real-time data and the historical data is calculated to determine whether the conditions for executing the second operation are met. If the difference is greater than the set threshold, the second operation is executed for further adjustment. Through this comprehensive control method, the control strategy can be dynamically adjusted during the operation of the motor based on real-time data and fault scores. When a motor fails, the most appropriate execution operation is selected through multi-level data analysis and judgment, which can not only improve the operating efficiency of the motor, but also detect and solve the fault in a shorter time, ensuring the stable operation of the motor under different working conditions. This method can improve the accuracy and efficiency of motor fault diagnosis and processing by combining decision trees, neural networks and sophisticated control strategies, minimize the downtime of equipment, and increase the service life of the motor.

[0036] It can be seen that by combining the operation data and load data of the motor, the diagnostic model based on decision trees and neural networks provides a comprehensive and accurate method for motor fault detection and control. One of the main benefits of this method is that by real-time monitoring of the operation status and load status of the motor, it can accurately identify whether the motor has a fault, and take corresponding control measures according to different fault scores. Compared with the traditional maintenance method, it can warn potential faults in real time, reduce the dependence on manual inspection, and greatly improve the efficiency and timeliness of fault diagnosis. Secondly, by setting a preset fault score and combining the difference judgment of the actual fault score, it can clearly divide when the motor needs to be warned. When the fault score is higher than or equal to the preset fault score, the fault type is used to decide whether to adopt a more in-depth control strategy. This difference judgment mechanism ensures the stability of the motor operation, avoids premature warnings or ignoring potential problems, and thus reduces the risk of motor operation under a more efficient control strategy. By analyzing and modeling the motor historical data based on decision trees and neural network models, the characteristics of motor faults can be extracted from a large amount of historical data, and the fault types can be accurately classified. This data-driven approach improves the accuracy of diagnosis, avoids errors in human judgment, and makes the identification of motor fault types more scientific and systematic. In addition, the state-related screening method can effectively identify abnormal data that does not conform to the normal working mode, further improving the sensitivity of fault prediction. Combined with the feature vector processing of the neural network, more distinguishing features can be extracted from complex abnormal data, making the identification of fault types more accurate. This multi-level and multi-dimensional data analysis method improves the reliability of fault diagnosis, especially when the motor is running under various working conditions, it can maintain a high diagnostic accuracy and ensure that the motor is always in the best operating state. Finally, based on the comparison of the difference between real-time data and historical data, it can be determined whether a second control operation is required based on the changes in the difference between the operating data and the difference between the load data. This mechanism of dynamically adjusting the control operation can first make lightweight adjustments when a minor fault occurs in the motor, and then adopt a more complex control strategy if the problem is not solved. This step-by-step optimization control method not only reduces the risk of motor failure, but also improves the long-term operating stability of the motor, minimizing maintenance costs and downtime.

[0037] In the above embodiment, by obtaining the detection data, operation data and load data of the motor, and comparing these real-time data with the preset detection data, it is possible to efficiently identify whether the motor is abnormal. This method not only avoids the traditional method of relying on manual inspection, but also can perform real-time monitoring at each stage of the motor operation, ensuring that potential faults can be discovered and handled in time at the early stage, reducing the downtime and maintenance costs caused by the fault. In addition, by introducing a fault scoring mechanism, the fault state of the motor can be quantified. When the motor fails, a fault score is calculated based on the relationship between the detection data and the preset data. This score not only helps to determine whether the motor has failed, but also can accurately assess the severity of the fault. After comparing with the preset fault score, it can be determined whether the early warning mechanism needs to be started based on the score difference. This early warning method based on the score difference makes the fault diagnosis of the motor more refined and intelligent, avoiding the previous extensive judgment that simply relies on a fixed threshold. Finally, when the fault score is higher than or equal to the preset value, the corresponding control method can be automatically selected and executed according to the fault type. This control method can not only take the most appropriate response measures for different types of faults, but also make dynamic adjustments based on the real-time operating status of the motor to ensure the stability and safety of the motor operation. When dealing with more complex faults, it will automatically select an alternative control scheme and further optimize the motor's control parameters based on historical load data or other relevant data to achieve the best operating state.

[0038] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a control system for a brushless DC reduction motor, including: an acquisition module, an analysis module and a central control module.

[0039] Specifically, the acquisition module is configured to acquire detection data, operation data and load data when the motor is started and stopped. The analysis module is electrically connected to the acquisition module, and the analysis module is configured to determine whether the motor operation fails based on the relationship between the detection data and the preset detection data, wherein: when it is determined that the motor operation fails, the relationship between the detection data and the preset detection data determines the fault score of the motor. The analysis module is also configured to determine the fault type of the motor based on the operation data and the load data. The central control module is electrically connected to the analysis module, and the central control module is configured to determine whether to issue an early warning to the motor based on the relationship between the fault score and the preset fault score, wherein when the fault score is lower than the preset fault score, the score difference between the fault score and the preset fault score is determined to determine the early warning method. When the fault score is higher than or equal to the preset fault score, the control method is determined according to the fault type.

[0040] It can be understood that the control method and system for a brushless DC reduction motor in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0042] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control method for a brushless DC reduction motor, characterized in that: include: The detection data, operation data and load data of the motor when it is started and stopped are obtained, and whether the motor operation fails is determined based on the relationship between the detection data and the preset detection data, where: When it is determined that a fault occurs during the operation of the motor, the fault score of the motor is determined based on the relationship between the detection data and the preset detection data; Determine the fault type of the motor based on the operating data and load data; According to the relationship between the fault score and the configured preset fault score, it is determined whether to issue an early warning for the motor. When the fault score is lower than the preset fault score, the early warning method is determined according to the score difference between the fault score and the preset fault score; when the fault score is higher than or equal to the preset fault score, the control method is determined according to the fault type.

2. The control method for a brushless DC reduction motor according to claim 1, characterized in that: When setting the preset test data, include: Acquire various historical detection data of the motor, wherein the detection data includes temperature detection data, vibration detection data and noise detection data of the motor; Obtaining the distance measurement between each historical detection data, and establishing a distance matrix for each historical detection data according to the distance measurement; Iteratively cluster the historical detection data according to the distance matrix, and obtain the feature vector of the normal detection data and the feature vector of the abnormal detection data according to the clustering results; Determine preset detection data according to the feature vector of normal detection data; The distance value between the feature vector of the normal detection data and the feature vector of the abnormal detection data is obtained and determined as a preset difference value.

3. The control method for a brushless DC reduction motor according to claim 2, characterized in that: When determining whether a motor operation failure occurs based on the relationship between the detection data and the preset detection data, it includes: Obtain the data difference between the detection data and the preset detection data, and determine whether the motor operation fails based on the relationship between each data difference and the preset difference: When the data difference is lower than the preset difference, it is determined that the motor is running without any fault; When the data difference is equal to or higher than the preset difference amount, it is determined that the motor operation fails, and the fault score of the motor is determined based on the relationship between the data difference and the preset difference amount.

4. The control method for a brushless DC reduction motor according to claim 3, characterized in that: When determining the fault score of the motor based on the relationship between the data difference and the preset difference amount, it includes: ; Among them, S is the fault score of the motor, wi is the weight coefficient of the i-th data item, is the nonlinear coefficient of the ith data item, t is the temperature data item, is the vibration data item, is the noise data item, is the smoothing coefficient, is the data difference, The preset difference value.

5. The control method for a brushless DC reduction motor according to claim 1, characterized in that: When obtaining the running data and load data of the motor when it starts and stops, it includes: When obtaining the operating data, it includes obtaining the motor's starting current, starting voltage, starting torque, start-stop time interval, and start-stop speed; When acquiring load data, it includes acquiring the inertial load, friction load, torque load and current load carried by the motor.

6. The control method for a brushless DC reduction motor according to claim 5, characterized in that: Determine the motor fault type based on the operating data and load data, including: Acquire each historical operation data and each historical load data of the motor, and establish a motor fault diagnosis model based on a decision tree, a neural network, and the relationship between each historical operation data and each historical load data; The motor's operating data and load data are substituted into the motor fault diagnosis model to determine the motor's fault type, and the execution operation during control is determined based on the fault type.

7. The control method for a brushless DC reduction motor according to claim 6, characterized in that: When establishing a motor fault diagnosis model based on the relationship between the decision tree, neural network, historical operation data and historical load data, it includes: Establishing a motor state correlation equation based on historical operation data and corresponding motor states and historical load data and corresponding motor states; Based on the decision tree, the motor state associations are screened to determine the abnormal data between the motor state associations: ； Where A is the contribution value of the motor correlation, H(F) is the entropy of the motor state correlation in the data set F; Fj is the jth subset after the motor state correlation is divided, and K is the total number of data subsets; Get the median contribution value between the contribution values ​​of each motor correlation equation, and determine the abnormal data based on the relationship between the contribution value and the median contribution value: When the contribution value of the motor correlation equation is less than the median value of the contribution value, the historical operation data and the historical load data in the motor correlation equation are determined to be abnormal data; When the contribution value of the motor correlation formula is greater than or equal to the median value of the contribution value, it is determined that the historical operation data and the historical load data in the motor correlation formula are not abnormal data; According to the determined abnormal data, the motor fault type and characteristic vector are determined based on the neural network, and a motor fault diagnosis model is established according to the motor fault type and characteristic vector.

8. The control method for a brushless DC reduction motor according to claim 7, characterized in that: According to the determined abnormal data, the motor fault type and characteristic vector are determined based on the neural network, including: ； Among them, f(X) is abnormal data, is the probability of the Kth fault type, and is the weight coefficient, and and are not zero, is the activation function, xi is the i-th feature of the feature data, wij is the weight of the input feature xi to the j-th neuron in the hidden layer, bj is the bias of the j-th neuron, h is the number of motor fault types, n is the number of input features, and j is the neuron index of the hidden layer.

9. The control method for a brushless DC reduction motor according to claim 7, characterized in that: Depending on the fault type, the control operation to be performed is determined, including: Determine a first execution operation and a second execution operation according to the fault type, control the motor according to the first execution operation, and obtain real-time operation data and real-time load data of the motor during control; Obtaining the running data difference between the real-time running data and the historical running data of the adjacent time periods, and obtaining the load data difference between the real-time load data and the historical load data of the adjacent time periods; According to the relationship between the running data difference and the load data difference, determine whether to use the second execution operation to control the motor: When the operation data difference is lower than or equal to the configured preset operation data difference, and / or the load data difference is lower than or equal to the configured preset load data difference, it is determined not to select the second execution operation to control the motor; When the operation data difference is higher than the preset operation data difference, and / or the load data difference is higher than the preset load data difference, it is determined to select the second execution operation to control the motor.

10. A control system for a brushless DC reduction motor, which is adopted in a control method for a brushless DC reduction motor as claimed in any one of claims 1 to 9, characterized in that: include: An acquisition module is configured to acquire detection data, operation data and load data when the motor is started and stopped; An analysis module is electrically connected to the acquisition module, and the analysis module is configured to determine whether a fault occurs in the operation of the motor according to a relationship between the detection data and the preset detection data, wherein: when it is determined that a fault occurs in the operation of the motor, a fault score of the motor is determined according to the relationship between the detection data and the preset detection data; the analysis module is further configured to determine a fault type of the motor according to the operation data and the load data; The central control module is electrically connected to the analysis module. The central control module is configured to determine whether to issue an early warning for the motor based on the relationship between the fault score and a configured preset fault score. When the fault score is lower than the preset fault score, the score difference between the fault score and the preset fault score is determined to determine the early warning method; when the fault score is higher than or equal to the preset fault score, the control method is determined according to the fault type.

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