Electric actuating mechanism intelligent maintenance system and method based on fault prediction

Abstract

The invention discloses an intelligent maintenance system and method for an electric actuating mechanism based on fault prediction, and particularly relates to the technical field of equipment maintenance. The method comprises the following steps: acquiring historical operation data in a preset operation period, generating a historical health data set, extracting characteristic parameters to obtain a historical characteristic parameter set, and constructing a trend change characteristic matrix according to the change trend of the historical characteristic parameters; constructing a multi-dimensional state space by using the trend change feature matrix, calculating the state aggregation degree of the fault event in the space, and determining an early warning index set; meanwhile, collecting real-time operation data to extract real-time feature parameters, forming real-time feature state vectors, and performing correlation calculation on the real-time feature state vectors and early warning indexes in a space to obtain real-time risk correlation factors; and finally, generating a risk probability prediction model according to the real-time risk association factor, the historical characteristic parameter set and the trend change characteristic matrix, predicting a fault probability and outputting a maintenance scheduling suggestion, thereby realizing intelligent maintenance of the electric actuating mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment maintenance, and more specifically, to an intelligent maintenance system and method for an electric actuator based on fault prediction. Background Art

[0002] The current operating status of electric actuators is directly related to equipment safety and production efficiency. However, traditional maintenance methods mainly rely on fixed-cycle maintenance, which makes it difficult to capture potential equipment fault signals in a timely manner, often resulting in delayed maintenance response after a fault occurs, increasing downtime risks and maintenance costs. With the rapid development of sensor and data acquisition technology, the operating data of electric actuators is becoming increasingly abundant, providing reliable data support for equipment status assessment, but there are still major technical challenges in how to extract effective features from massive data and build accurate fault prediction models. Existing technologies are relatively scattered in their research on intelligent maintenance of electric actuators, lacking a unified and systematic analysis method.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent maintenance system and method for electric actuators based on fault prediction to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The intelligent maintenance method of electric actuator based on fault prediction includes the following steps: Acquire historical operation data of the electric actuator within a preset operation cycle, and generate a historical health data set of the electric actuator based on the historical operation data; Extract characteristic parameters from the historical health data set to obtain the historical characteristic parameter set of the electric actuator, and establish a trend change characteristic matrix based on the trend of historical characteristic parameters changing over time; A multidimensional state space is constructed based on the trend change feature matrix, the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space is calculated, and the characteristic warning indicator set is determined according to the state aggregation degree; Extract real-time characteristic parameters based on the real-time operation data of the electric actuator, establish a real-time characteristic state vector, and calculate the spatial position correlation with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor; Based on the real-time risk correlation factors, historical characteristic parameter sets and trend change characteristic matrices, a risk probability prediction model is generated to predict the failure probability of the electric actuator and generate maintenance scheduling recommendations.

[0006] In a preferred embodiment, historical operation data of the electric actuator within a preset operation cycle is obtained, and a historical health data set of the electric actuator is generated according to the historical operation data, specifically: Collect historical operation data of the electric actuator within a preset operation cycle; According to the change range, change trend and fluctuation range of each parameter data in the historical operation data, data validity screening is carried out to remove parameter data that exceeds the preset reasonable range; The historical operating data that has been screened for data validity are classified and integrated according to the parameter correspondence to generate a historical health data set of the electric actuator that includes motor operating parameters, actuator motion parameters and environmental vibration parameters.

[0007] In a preferred embodiment, feature parameters are extracted from the historical health data set to obtain a historical feature parameter set of the electric actuator, and a trend change feature matrix is ​​established according to the trend of historical feature parameters changing over time, specifically: Extract historical characteristic parameters from historical health data sets; Establish a historical characteristic parameter set according to different dimensions of historical characteristic parameters; Calculate the change trend values ​​of different historical characteristic parameters in the historical characteristic parameter set within a specified time period; The change trend values ​​of the historical characteristic parameters are arranged in chronological order to form a trend change characteristic matrix; the rows of the trend change characteristic matrix represent different historical characteristic parameters, and the columns represent corresponding time series data.

[0008] In a preferred embodiment, a multidimensional state space is constructed based on the trend change feature matrix, the state aggregation of the historical fault events of the electric actuator in the multidimensional state space is calculated, and a characteristic warning indicator set is determined based on the state aggregation, specifically: The dimension of the multidimensional state space is determined according to the number of historical characteristic parameters in the trend change characteristic matrix; the coordinate axes of the multidimensional state space are different historical characteristic parameters of the trend change characteristic matrix, and the characteristic position coordinates in the multidimensional state space are the change trend values ​​of the historical characteristic parameters; Based on the coordinates of each characteristic position in the multidimensional state space, the aggregation degree of the state points when the electric actuator has a historical fault is calculated, and the state aggregation degree value of the historical fault event of the electric actuator in the multidimensional state space is obtained; The warning threshold is set according to the state concentration value, and the historical feature parameters corresponding to the feature position coordinates whose state concentration value exceeds the warning threshold are selected as the feature warning indicator set.

[0009] In a preferred embodiment, the real-time characteristic parameters are extracted according to the real-time operation data of the electric actuator, a real-time characteristic state vector is established, and the spatial position correlation calculation is performed with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk association factor, which is specifically: Real-time collection of operating data of the electric actuator in its current operating state; Extracting real-time characteristic parameters based on real-time operation data, and forming a real-time characteristic state vector according to the parameter sequence of the historical characteristic parameter set; The real-time feature state vector is mapped to the multidimensional state space, the spatial distance between the real-time feature state vector and the feature position coordinates in the feature warning indicator set is calculated, the spatial position correlation is calculated according to the spatial distance, and the real-time risk association factor of the current operating state of the electric actuator is determined based on the spatial position correlation.

[0010] In a preferred embodiment, a risk probability prediction model is generated based on the real-time risk association factor, the historical characteristic parameter set and the trend change characteristic matrix to predict the failure probability of the electric actuator and generate maintenance scheduling suggestions, specifically: Use the real-time risk correlation factors and historical characteristic parameter sets as input data for the risk probability prediction model; Using the risk probability prediction model, the change trend values ​​of the historical characteristic parameters in the trend change characteristic matrix are calculated to predict the probability value of the electric actuator failing in the current operating state; Based on the predicted failure probability value and the preset maintenance threshold, maintenance scheduling recommendations for the electric actuator are generated.

[0011] On the other hand, the present invention provides an intelligent maintenance system for electric actuators based on fault prediction, including a data acquisition module, a characteristic parameter extraction module, a state space construction module, a correlation calculation module, and a risk probability prediction module; The data acquisition module obtains the historical operation data of the electric actuator within a preset operation cycle, and generates a historical health data set of the electric actuator based on the historical operation data; The characteristic parameter extraction module extracts characteristic parameters from the historical health data set to obtain the historical characteristic parameter set of the electric actuator, and establishes a trend change characteristic matrix according to the trend of historical characteristic parameters changing over time; The state space construction module constructs a multidimensional state space based on the trend change feature matrix, calculates the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space, and determines the characteristic warning indicator set according to the state aggregation degree; The correlation calculation module extracts real-time characteristic parameters according to the real-time operation data of the electric actuator, establishes a real-time characteristic state vector, and performs spatial position correlation calculation with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor; The risk probability prediction module generates a risk probability prediction model based on real-time risk association factors, historical characteristic parameter sets and trend change characteristic matrices, predicts the failure probability of the electric actuator, and generates maintenance scheduling recommendations.

[0012] The technical effects and advantages of the electric actuator intelligent maintenance system and method based on fault prediction of the present invention are as follows: By analyzing the historical and real-time operation data of the electric actuator, the key characteristic parameters that affect the fault of the electric actuator can be accurately identified, and the trend change characteristic matrix can be constructed based on the parameter change trend, realizing the full-cycle monitoring of the operation status of the electric actuator. The state aggregation degree of historical fault events is quantitatively analyzed using multi-dimensional state space, which can effectively screen out the characteristic warning indicator set that has a significant impact on the abnormality of the electric actuator, and improve the accuracy of fault prediction. At the same time, the spatial position correlation calculation method is used in real-time monitoring, which can dynamically evaluate the matching degree between the current state of the electric actuator and the fault warning indicator, and improve the real-time and accuracy of risk identification. Combined with the risk probability prediction model, it can predict the potential fault risk in advance and generate corresponding maintenance scheduling suggestions according to the change trend of the current operation status of the electric actuator, which can adapt to the maintenance needs of different types of electric actuators, significantly improve the reliability and safety of the operation of the electric actuator, reduce the risk of unplanned downtime, and effectively optimize the allocation of maintenance resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the intelligent maintenance method of the electric actuator based on fault prediction of the present invention; Figure 2 It is a structural schematic diagram of the intelligent maintenance system of electric actuator based on fault prediction of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Embodiment 1:

[0016] Figure 1The present invention provides an intelligent maintenance method for an electric actuator based on fault prediction, which comprises the following steps: Historical operation data of the electric actuator within a preset operation cycle, and generating a historical health data set of the electric actuator based on the historical operation data; Extract characteristic parameters from the historical health data set to obtain the historical characteristic parameter set of the electric actuator, and establish a trend change characteristic matrix based on the trend of historical characteristic parameters changing over time; A multidimensional state space is constructed based on the trend change feature matrix, the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space is calculated, and the characteristic warning indicator set is determined according to the state aggregation degree; Extract real-time characteristic parameters based on the real-time operation data of the electric actuator, establish a real-time characteristic state vector, and calculate the spatial position correlation with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor; Based on the real-time risk correlation factors, historical characteristic parameter sets and trend change characteristic matrices, a risk probability prediction model is generated to predict the failure probability of the electric actuator and generate maintenance scheduling recommendations.

[0017] Specifically, historical operation data of the electric actuator within a preset operation cycle is obtained, and a historical health data set of the electric actuator is generated according to the historical operation data, including: Collect historical operation data of the electric actuator within a preset operation cycle; Specifically, data of various parameters of the electric actuator during operation are collected within a fixed time period (such as daily, weekly or monthly). For example, the collected data includes: Motor operating parameters: motor current, motor voltage, motor temperature, etc.; Electric actuator motion parameters: actuator displacement, speed, etc.; Environmental vibration parameters: vibration signal, vibration frequency, etc.

[0018] Exemplarily, the preset operation cycle of the electric actuator is set to 24 hours. Within 24 hours, data is recorded every 1 minute, so 1440 data points will be obtained in one day, and each data point records the value of the above parameters.

[0019] According to the change range, change trend and fluctuation range of each parameter data in the historical operation data, data validity screening is carried out to remove parameter data that exceeds the preset reasonable range; Specifically, the collected data may contain abnormal values ​​due to sensor failure or external interference. It is necessary to set a reasonable value range, allowable change trend and fluctuation range to screen the data.

[0020] For example, under normal conditions, the motor current should be between 5A and 15A; if a certain record is 20A or 3A, it is judged as abnormal data and discarded.

[0021] The historical operation data after data validity screening is classified and integrated according to the parameter correspondence to generate a historical health data set of the electric actuator containing motor operation parameters, actuator motion parameters and environmental vibration parameters; Specifically, the historical operating data that has been screened for data validity is classified and integrated according to the attributes of each parameter to form a structured data set.

[0022] Exemplarily, motor parameters (such as current, voltage, and temperature) are classified into one category; The motion parameters of electric actuators (such as displacement and speed) are classified into another category; Environmental vibration parameters are classified as the third category.

[0023] Specifically, feature parameters are extracted from the historical health data set to obtain the historical feature parameter set of the electric actuator, and a trend change feature matrix is ​​established according to the trend of historical feature parameters changing over time, including: Extract historical characteristic parameters from historical health data sets; Specifically, key indicators that can reflect the state changes of the electric actuator, such as average value, peak value, standard deviation, change rate, etc., are extracted from the historical health data set.

[0024] Exemplarily, the average current value and fluctuation (standard deviation) are extracted from the motor current data; the temperature rise rate is extracted from the temperature data; the maximum displacement change is extracted from the displacement data, etc.

[0025] Establish a historical characteristic parameter set according to different dimensions of historical characteristic parameters; Specifically, the extracted characteristic parameters are sorted according to the three dimensions of motor parameters, actuator motion parameters, and environmental parameters to form a historical characteristic parameter set, for example: Motor parameters: current average value, current variance, voltage average value, voltage fluctuation rate, motor temperature average value, motor temperature rise rate; Electric actuator motion parameters: average valve displacement, average valve speed; Environmental vibration parameters: vibration frequency, average vibration frequency.

[0026] Calculate the change trend values ​​of different historical characteristic parameters in the historical characteristic parameter set within a specified time period; Specifically, the degree of change of each characteristic parameter within a certain time interval is calculated to quantify its change trend.

[0027] For example, to calculate the trend of the current average value: Current change trend = (average current of the next day - average current of the previous day) / average current of the previous day Similarly, the same calculation method is applied to other historical characteristic parameters to obtain their respective change trend values.

[0028] The change trend values ​​of the historical characteristic parameters are arranged in chronological order to form a trend change characteristic matrix; the rows of the trend change characteristic matrix represent different historical characteristic parameters, and the columns represent corresponding time series data.

[0029] Specifically, a multidimensional state space is constructed based on the trend change feature matrix, the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space is calculated, and a characteristic warning indicator set is determined according to the state aggregation degree, including: The dimension of the multidimensional state space is determined according to the number of historical characteristic parameters in the trend change characteristic matrix; the coordinate axes of the multidimensional state space are different historical characteristic parameters of the trend change characteristic matrix, and the characteristic position coordinates in the multidimensional state space are the change trend values ​​of the historical characteristic parameters; Specifically, each row in the trend change feature matrix is ​​regarded as an independent dimension to form a multidimensional space. For example, if three historical feature parameters, namely "motor current average value change trend", "voltage fluctuation rate change trend" and "motor temperature gradient change trend", are extracted from the trend change feature matrix, the dimension of the multidimensional state space is 3. The three coordinate axes of this state space represent the above three parameters respectively.

[0030] For a historical fault event, by calculating the changing trends of various parameters during the event (for example, the motor current changing trend is 2.5%, the voltage fluctuation rate changing trend is -0.4%, and the motor temperature gradient changing trend is 1.2%), the corresponding spatial coordinates can be obtained as (2.5, -0.4, 1.2).

[0031] Based on the coordinates of each characteristic position in the multidimensional state space, the aggregation degree of the state points when the electric actuator has a historical fault is calculated, and the state aggregation degree value of the historical fault event of the electric actuator in the multidimensional state space is obtained; Specifically, after mapping the historical fault events to the multidimensional state space, each fault event has a coordinate point in the space. In order to calculate the degree of aggregation of these fault event points, the local density or distance statistics method can be used.

[0032] For example, setting the radius (The unit is the same as that of each coordinate axis), count the number of fault event points within the radius The number of other fault event points within , and calculate the space volume corresponding to the radius (For example, in three-dimensional space ). Then the calculation formula of state aggregation can be defined as: ;in, Indicates the state aggregation degree, which is used to reflect the distribution and concentration of historical fault points in multidimensional space.

[0033] The warning threshold is set according to the state concentration value, and the historical characteristic parameters corresponding to the characteristic position coordinates whose state concentration value exceeds the warning threshold are selected as the characteristic warning indicator set; Specifically, a warning threshold is determined based on the calculated state aggregation of each fault event. For example, statistical analysis shows that when the state aggregation is greater than 8, the fault risk increases significantly.

[0034] At this time, for all fault events with a concentration degree exceeding 8, the historical characteristic parameters (such as current change trend, temperature gradient, etc.) corresponding to their coordinates in the multi-dimensional space are considered to have early warning significance.

[0035] For example, if the coordinates of a fault event in space are (2.5, -0.4, 1.2) and its concentration calculation result is 9.62, then the parameters represented by these three values ​​can be used as early warning indicators and constitute a characteristic early warning indicator set.

[0036] Specifically, the real-time characteristic parameters are extracted according to the real-time operation data of the electric actuator, and the real-time characteristic state vector is established. The spatial position correlation calculation is performed with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor, including: Real-time collection of operating data of the electric actuator in its current operating state; Specifically, during the normal operation of the electric actuator, various operating data at the current moment are collected in real time. These data are consistent with the parameters when the historical data was collected, such as motor current, motor temperature, actuator displacement, speed and vibration signals.

[0037] Exemplarily, during the operation of the electric actuator, data is collected every one minute, which is consistent with the historical data collection cycle to ensure the comparability of data comparison.

[0038] Extracting real-time characteristic parameters based on real-time operation data, and forming a real-time characteristic state vector according to the parameter sequence of the historical characteristic parameter set; Specifically, the feature parameters of the real-time collected data are extracted in the same processing method as the historical operation data processing. If the order of the historical feature parameter set is [motor current change trend, voltage fluctuation rate, temperature gradient, displacement change rate, speed change rate, vibration frequency change], the real-time feature state vector is also arranged in this order to obtain the real-time feature state vector.

[0039] Map the real-time feature state vector to the multidimensional state space, calculate the spatial distance between the real-time feature state vector and the feature position coordinates in the feature warning indicator set, calculate the spatial position correlation according to the spatial distance, and determine the real-time risk association factor of the current operating state of the electric actuator according to the spatial position correlation; Specifically, the real-time feature state vector is mapped to the multidimensional state space to obtain the coordinates of the current state in the space. Then, the coordinates are compared with the coordinates of each warning point in the feature warning indicator set, and the spatial distance is calculated using the Euclidean distance formula. The spatial position correlation is determined based on the inverse relationship of distance.

[0040] This spatial position correlation is used as a real-time risk association factor to reflect the degree of match between the current operating state and the historical fault warning characteristics. The larger the value of the spatial position correlation, the closer the current state is to the warning indicator and the higher the fault risk.

[0041] Specifically, based on the real-time risk correlation factors, historical characteristic parameter sets and trend change characteristic matrix, a risk probability prediction model is generated to predict the failure probability of the electric actuator and generate maintenance scheduling suggestions, including: Use the real-time risk correlation factors and historical characteristic parameter sets as input data for the risk probability prediction model; Specifically, when constructing the risk probability prediction model, firstly, the real-time risk correlation factors and various parameters in the historical characteristic parameter set (such as the motor current change trend, temperature gradient, etc.) are selected as the input of the risk probability prediction model.

[0042] Using the risk probability prediction model, the change trend values ​​of the historical characteristic parameters in the trend change characteristic matrix are calculated to predict the probability value of the electric actuator failing in the current operating state; Specifically, the risk probability prediction model can use weighted average or multiple regression methods to integrate the real-time risk correlation factors with historical trend data. The output of the risk probability prediction model is a failure probability value between 0 and 1, reflecting the probability of failure of the electric actuator under the current operating state.

[0043] Generate maintenance scheduling suggestions for electric actuators based on predicted failure probability values ​​and preset maintenance thresholds; Specifically, the failure probability value is compared with the preset maintenance threshold: When the failure probability value is greater than or equal to the preset maintenance threshold, it is recommended to immediately initiate maintenance measures, such as arranging maintenance in advance, adjusting working parameters, or replacing parts; When the failure probability value is less than the preset maintenance threshold, it is recommended to continue monitoring or schedule regular maintenance.

[0044] Embodiment 2:

[0045] The difference between Example 2 of the present invention and Example 1 is that this example introduces an intelligent maintenance system for electric actuators based on fault prediction.

[0046] Figure 2 The structural schematic diagram of the electric actuator intelligent maintenance system based on fault prediction of the present invention is given. The electric actuator intelligent maintenance system based on fault prediction includes a data acquisition module, a characteristic parameter extraction module, a state space construction module, a correlation calculation module, and a risk probability prediction module; The data acquisition module obtains the historical operation data of the electric actuator within a preset operation cycle, and generates a historical health data set of the electric actuator based on the historical operation data; The characteristic parameter extraction module extracts characteristic parameters from the historical health data set to obtain the historical characteristic parameter set of the electric actuator, and establishes a trend change characteristic matrix according to the trend of historical characteristic parameters changing over time; The state space construction module constructs a multidimensional state space based on the trend change feature matrix, calculates the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space, and determines the characteristic warning indicator set according to the state aggregation degree; The correlation calculation module extracts real-time characteristic parameters according to the real-time operation data of the electric actuator, establishes a real-time characteristic state vector, and performs spatial position correlation calculation with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor; The risk probability prediction module generates a risk probability prediction model based on real-time risk association factors, historical characteristic parameter sets and trend change characteristic matrices, predicts the failure probability of the electric actuator, and generates maintenance scheduling recommendations.

[0047] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0048] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0049] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0050] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0051] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0052] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0054] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0056] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent maintenance method for electric actuators based on fault prediction, characterized in that: The steps include: Acquire historical operation data of the electric actuator within a preset operation cycle, and generate a historical health data set of the electric actuator based on the historical operation data; Extract characteristic parameters from the historical health data set to obtain the historical characteristic parameter set of the electric actuator, and establish a trend change characteristic matrix based on the trend of historical characteristic parameters changing over time; A multidimensional state space is constructed based on the trend change feature matrix, the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space is calculated, and the characteristic warning indicator set is determined according to the state aggregation degree; Extract real-time characteristic parameters based on the real-time operation data of the electric actuator, establish a real-time characteristic state vector, and calculate the spatial position correlation with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor; Based on the real-time risk correlation factors, historical characteristic parameter sets and trend change characteristic matrices, a risk probability prediction model is generated to predict the failure probability of the electric actuator and generate maintenance scheduling recommendations.

2. The intelligent maintenance method for electric actuators based on fault prediction according to claim 1 is characterized in that: Obtain the historical operation data of the electric actuator within a preset operation cycle, and generate a historical health data set of the electric actuator based on the historical operation data, specifically: Collect historical operation data of the electric actuator within a preset operation cycle; According to the change range, change trend and fluctuation range of each parameter data in the historical operation data, data validity screening is carried out to remove parameter data that exceeds the preset reasonable range; The historical operating data that has been screened for data validity are classified and integrated according to the parameter correspondence to generate a historical health data set of the electric actuator that includes motor operating parameters, actuator motion parameters and environmental vibration parameters.

3. The intelligent maintenance method for electric actuators based on fault prediction according to claim 2 is characterized in that: The characteristic parameters of the historical health data set are extracted to obtain the historical characteristic parameter set of the electric actuator. The trend change characteristic matrix is ​​established according to the trend of historical characteristic parameters changing over time, which is as follows: Extract historical characteristic parameters from historical health data sets; Establish a historical characteristic parameter set according to different dimensions of historical characteristic parameters; Calculate the change trend values ​​of different historical characteristic parameters in the historical characteristic parameter set within a specified time period; The change trend values ​​of the historical characteristic parameters are arranged in chronological order to form a trend change characteristic matrix; the rows of the trend change characteristic matrix represent different historical characteristic parameters, and the columns represent corresponding time series data.

4. The intelligent maintenance method for electric actuators based on fault prediction according to claim 3 is characterized in that: A multidimensional state space is constructed based on the trend change feature matrix, the state aggregation of the historical fault events of the electric actuator in the multidimensional state space is calculated, and the characteristic warning indicator set is determined according to the state aggregation, which is specifically: The dimension of the multidimensional state space is determined according to the number of historical characteristic parameters in the trend change characteristic matrix; the coordinate axes of the multidimensional state space are different historical characteristic parameters of the trend change characteristic matrix, and the characteristic position coordinates in the multidimensional state space are the change trend values ​​of the historical characteristic parameters; Based on the coordinates of each characteristic position in the multidimensional state space, the aggregation degree of the state points when the electric actuator has a historical fault is calculated, and the state aggregation degree value of the historical fault event of the electric actuator in the multidimensional state space is obtained; The warning threshold is set according to the state concentration value, and the historical feature parameters corresponding to the feature position coordinates whose state concentration value exceeds the warning threshold are selected as the feature warning indicator set.

5. The intelligent maintenance method for electric actuators based on fault prediction according to claim 4 is characterized in that: According to the real-time operation data of the electric actuator, the real-time characteristic parameters are extracted, the real-time characteristic state vector is established, and the spatial position correlation calculation is performed with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor, which is specifically: Real-time collection of operating data of the electric actuator in its current operating state; Extracting real-time characteristic parameters based on real-time operation data, and forming a real-time characteristic state vector according to the parameter sequence of the historical characteristic parameter set; The real-time feature state vector is mapped to the multidimensional state space, the spatial distance between the real-time feature state vector and the feature position coordinates in the feature warning indicator set is calculated, the spatial position correlation is calculated according to the spatial distance, and the real-time risk association factor of the current operating state of the electric actuator is determined based on the spatial position correlation.

6. The intelligent maintenance method for electric actuators based on fault prediction according to claim 5 is characterized in that: Based on the real-time risk correlation factors, historical characteristic parameter sets and trend change characteristic matrix, a risk probability prediction model is generated to predict the failure probability of the electric actuator and generate maintenance scheduling suggestions, specifically: Use the real-time risk correlation factors and historical characteristic parameter sets as input data for the risk probability prediction model; Using the risk probability prediction model, the change trend values ​​of the historical characteristic parameters in the trend change characteristic matrix are calculated to predict the probability value of the electric actuator failing in the current operating state; Based on the predicted failure probability value and the preset maintenance threshold, maintenance scheduling recommendations for the electric actuator are generated.

7. An intelligent maintenance system for electric actuators based on fault prediction, used to implement the intelligent maintenance method for electric actuators based on fault prediction according to any one of claims 1 to 6, characterized in that: It includes data acquisition module, characteristic parameter extraction module, state space construction module, correlation calculation module and risk probability prediction module; The data acquisition module obtains the historical operation data of the electric actuator within a preset operation cycle, and generates a historical health data set of the electric actuator based on the historical operation data; The characteristic parameter extraction module extracts characteristic parameters from the historical health data set to obtain the historical characteristic parameter set of the electric actuator, and establishes a trend change characteristic matrix according to the trend of historical characteristic parameters changing over time; The state space construction module constructs a multidimensional state space based on the trend change feature matrix, calculates the state aggregation degree of the historical fault events of the electric actuator in the multidimensional state space, and determines the characteristic warning indicator set according to the state aggregation degree; The correlation calculation module extracts real-time characteristic parameters according to the real-time operation data of the electric actuator, establishes a real-time characteristic state vector, and performs spatial position correlation calculation with the characteristic warning indicator set in the multi-dimensional state space to determine the real-time risk correlation factor; The risk probability prediction module generates a risk probability prediction model based on real-time risk association factors, historical characteristic parameter sets and trend change characteristic matrices, predicts the failure probability of the electric actuator, and generates maintenance scheduling recommendations.

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