Method and system for obtaining stator winding end vibration fault diagnosis model

Abstract

The present application relates to a method and system for obtaining a stator winding end vibration fault diagnosis model, wherein the method for obtaining a stator winding end vibration fault diagnosis model includes: obtaining historical operating data, the historical operating data including operating parameters and the stator winding end amplitude at corresponding moments, determining target parameters from the operating parameters through the Kendall correlation coefficient, and determining training data based on the target parameters and the corresponding stator winding end amplitude; iteratively training a radial basis function generalized regression model based on the training data and a gradient descent method to obtain a theoretical value of the stator winding end amplitude, and calculating the error between the theoretical value and the true value; in response to the error being less than a first threshold or the number of iterations being equal to a preset number of iterations, stopping the iteration to obtain a stator winding end vibration fault diagnosis model.

Description

Technical Field

[0001] The present application relates to the field of large generator fault diagnosis, and in particular to a method and system for obtaining a stator winding end vibration fault diagnosis model. Background Art

[0002] As core equipment for traditional power generation, the safe and efficient operation of large steam turbine generators directly impacts the stability of the power system. The stator winding plays a crucial role in converting mechanical energy into electrical energy. Stator winding end vibration is a key parameter that requires monitoring and management during steam turbine generator operation. Under the influence of long-term electromagnetic forces, thermal stress, and mechanical stress, the stator winding ends inevitably become loose, causing abnormal vibration. Failure to promptly detect and address vibration failures can lead to equipment damage or accidents, potentially jeopardizing the stable operation of the power system.

[0003] Currently, generator stator end vibration fault diagnosis typically involves setting fixed thresholds and issuing alerts when these thresholds are exceeded. However, this approach has significant limitations. In the early stages of a fault, fixed thresholds cannot effectively address weak vibration amplitudes that could indicate a potential problem. This can lead to delayed or missed detection of potential problems, increasing the risk of unplanned downtime or equipment damage.

[0004] The existing generator stator end vibration fault diagnosis method has problems with real-time performance and poor diagnostic effect. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for obtaining a stator winding end vibration fault diagnosis model to at least solve the problems of poor real-time performance and poor diagnostic effect in related technologies.

[0006] In a first aspect, an embodiment of the present application provides a method for obtaining a stator winding end vibration fault diagnosis model, comprising:

[0007] Acquiring historical operating data, the historical operating data including operating parameters and stator winding end amplitudes at corresponding moments, determining target parameters from the operating parameters using a Kendall correlation coefficient, and determining training data based on the target parameters and the corresponding stator winding end amplitudes;

[0008] Iteratively training a radial basis function generalized regression model based on the training data and a gradient descent method to obtain a theoretical value of the stator winding end amplitude, and calculating an error between the theoretical value and the true value;

[0009] In response to the error being less than a first threshold or the number of iterations being equal to a preset number of iterations, the iteration is stopped to obtain a stator winding end vibration fault diagnosis model.

[0010] In one embodiment, the radial basis function generalized regression model is configured to use a Gaussian permutation function as a kernel function.

[0011] In one embodiment, the radial basis function generalized regression model is configured to: cluster features of training data through self-organizing maps, and determine the number of clusters, the center point and radius of each cluster.

[0012] In one embodiment, the radial basis function generalized regression model includes an input layer, a hidden layer, and an output layer. The hidden layer is configured to perform spatial mapping using a Gaussian permutation function as a kernel function. The Gaussian permutation function is expressed as:

[0013]

[0014] Among them, x i represents the i-th data in the training data, c j represents the center of the jth set, δ j represents the diffusion coefficient of the jth set.

[0015] In one embodiment, the Kendall correlation coefficient represents the correlation between the operating parameter and the amplitude. The calculation formula of the Kendall correlation coefficient is as follows:

[0016]

[0017] Wherein, τ represents the Kendall correlation coefficient, N represents the number of the historical operation data, C represents the number of element logarithms of each operating parameter being positively correlated with the change pattern of the amplitude, and D represents the number of element logarithms of each operating parameter being negatively correlated with the change pattern of the amplitude.

[0018] In one embodiment, the operating parameters include at least two types, and determining the target parameter from the operating parameters includes: selecting the operating parameter whose absolute value of the Kendall correlation coefficient is greater than a second threshold as the target parameter.

[0019] In a second aspect, an embodiment of the present application provides a method for diagnosing a stator winding end vibration fault, comprising:

[0020] Acquire real-time operating parameters of the steam turbine generator, and obtain a theoretical value of the stator winding end amplitude based on the real-time operating parameters using the stator winding end vibration fault diagnosis model described in the first aspect;

[0021] The deviation between the actual value of the amplitude monitored by the sensor and the theoretical value is calculated, and whether there is abnormality in the vibration of the stator winding end is determined based on the deviation.

[0022] In one embodiment, determining whether the vibration of the stator winding end is abnormal based on the deviation includes:

[0023] A ratio of the deviation to the theoretical value is calculated, and in response to the ratio being greater than a third threshold, it is determined that an abnormality exists.

[0024] In a third aspect, an embodiment of the present application provides a system for obtaining a stator winding end vibration fault diagnosis model, comprising:

[0025] An acquisition module is configured to acquire historical operating data, the historical operating data including operating parameters and stator winding end amplitudes at corresponding moments, determine target parameters from the operating parameters using the Kendall correlation coefficient, and determine training data based on the target parameters and the corresponding stator winding end amplitudes;

[0026] Training module: used for iteratively training the radial basis function generalized regression model based on the training data and the gradient descent method, obtaining the theoretical value of the stator winding end amplitude, and calculating the error between the theoretical value and the true value;

[0027] Model module: used to stop iteration in response to the error being less than a first threshold or the number of iterations being equal to a preset number of iterations, so as to obtain a stator winding end vibration fault diagnosis model.

[0028] In a fourth aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for obtaining a stator winding end vibration fault diagnosis model as described in the first aspect above is implemented.

[0029] The method and system for obtaining a stator winding end vibration fault diagnosis model provided in the embodiments of the present application have at least the following technical effects.

[0030] This application establishes a dynamic stator winding end vibration fault diagnosis model through a radial basis function generalized regression model. Through the Kendall correlation coefficient, the parameters with a high correlation with the stator winding end vibration are determined from the operating parameters, and redundant data is filtered out, thereby improving the working efficiency of the model. Compared with the traditional monitoring method that relies on sensors to collect vibration data and perform fixed threshold alarms, the diagnostic model provided by this application can more accurately predict the theoretical amplitude of the stator winding end under different working conditions and realize early fault warning. It helps to intervene and repair the problem before it expands, reduces the risk of unexpected downtime, and improves the accuracy and reliability of fault detection and the safety and stability of motor operation.

[0031] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 This is a flow chart of a method for obtaining a stator winding end vibration fault diagnosis model according to an embodiment of the present application;

[0034] Figure 2 is a flow chart of a stator winding end vibration fault diagnosis method provided according to an embodiment of the present application;

[0035] Figure 3 This is a structural block diagram of a stator winding end vibration fault diagnosis model acquisition system provided in an embodiment of the present application;

[0036] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0038] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0039] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0040] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0041] In a first aspect, an embodiment of the present application provides a method for obtaining a stator winding end vibration fault diagnosis model. Figure 1 Flowchart of the method for obtaining the stator winding end vibration fault diagnosis model according to the embodiment of the present application is provided. Figure 1 As shown, the method includes:

[0042] Step S101, obtain historical operating data, which includes operating parameters and stator winding end amplitudes at corresponding moments, determine target parameters from the operating parameters through the Kendall correlation coefficient, and determine training data based on the target parameters and the corresponding stator winding end amplitudes.

[0043] Optionally, operating parameters include stator current, stator voltage, active power, reactive power, load change rate, cooling water inlet temperature and flow rate, hydrogen pressure, hydrogen temperature, hydrogen flow rate, and oil temperature. Based on the Kendall correlation coefficient, target parameters with a high correlation with the stator winding end vibration amplitude at the corresponding moment are selected from the multiple operating parameters as training data. This method filters the operating parameters in the historical data, removes interference data with low correlation, improves the quality of the training data, and facilitates the improvement of the prediction accuracy of the subsequent stator winding end vibration fault diagnosis model.

[0044] In one example, the Kendall correlation coefficient represents the correlation between the operating parameter and the amplitude. The calculation formula of the Kendall correlation coefficient is as follows:

[0045]

[0046] Where, τ represents the Kendall correlation coefficient, N is the number of historical operating data, C represents the number of logarithms of elements with positive correlation between each operating parameter and the change pattern of amplitude, and D represents the number of logarithms of elements with negative correlation between each operating parameter and the change pattern of amplitude.

[0047] Alternatively, taking the stator current as an example, the stator current in the historical operation data is represented as x, and the corresponding stator winding end amplitude is represented as y. The stator current and the corresponding amplitude are a pair of data, and N is the number of data pairs in the historical operation data. C means that the stator current and the corresponding amplitude meet (x m -x n )(y m -y n )≥0, D represents the stator current and the corresponding amplitude satisfying (x m -x n )(y m -y n )<0. m 、x n Represent the mth and nth stator currents, y m 、y n represent the mth and nth amplitudes respectively.

[0048] Among them, the value range of Kendall correlation coefficient τ is [-1,1], where a positive value of τ indicates that the change pattern of the operating parameter and the amplitude is positively correlated, and a negative value of τ indicates that the change pattern of the operating parameter and the amplitude is negatively correlated. The larger the absolute value of τ, the stronger the correlation between the operating parameter and the amplitude, and the smaller the absolute value, the weaker the correlation between the operating parameter and the amplitude. τ is 0, which indicates that there is no correlation between the two parameters.

[0049] In one example, the operating parameters include at least two types, and step S101 includes: selecting an operating parameter whose absolute value of the Kendall correlation coefficient is greater than a second threshold as a target parameter.

[0050] Optionally, target parameters whose correlation coefficient with the stator winding end vibration amplitude is greater than a set threshold are selected from the operating parameters as training data. This allows filtering of the operating parameters in the historical data, removing interference data with low correlation, and improving the quality of the training data, which is beneficial for improving the prediction accuracy of subsequent stator winding end vibration fault diagnosis models.

[0051] Step S102 , iteratively training a radial basis function generalized regression model based on the training data and the gradient descent method, obtaining a theoretical value of the stator winding end amplitude, and calculating the error between the theoretical value and the true value.

[0052] Optionally, the radial basis function generalized regression model is trained and evaluated based on the training data obtained in step S101, and a training set and a validation set are divided according to a set ratio. The specific ratio is set according to the actual training needs. In this application, 80% of the training data is input into the radial basis function generalized regression model for training, and 20% of the training data is used to evaluate the model prediction effect. In this application, the gradient descent method is used as the training algorithm, and the learning rate and regularization parameters are dynamically adjusted during the training process, and the training error and convergence are monitored in real time.

[0053] In one example, the radial basis function generalized regression model uses a Gaussian permutation function as a kernel function.

[0054] Optionally, a Gaussian permutation function is used as the kernel function of the radial basis function generalized regression model, and the number, center, width and weight initialization method of the radial basis function are determined according to the characteristics and scale of the training data. Among them, the initialization number of radial basis functions needs to be determined according to the specific problem and data characteristics, usually set by a priori values ​​or by determining the center points of the radial basis functions in the input data through a clustering algorithm, thereby indirectly determining the number of functions. The initial center points of the radial basis functions can be determined by random selection, clustering algorithms, and self-organizing maps. The width of the radial basis function controls the coverage of the function, affecting the response speed of the function and the corresponding nonlinear characteristics. The initial width is generally set manually according to the priori value or using a data-based method, and is dynamically set according to the variance of the data or the distance information in the feature space. The initial weight can be initialized by random initialization and linear regression.

[0055] Preferably, in one example, the radial basis function generalized regression model clusters the features of the training data through self-organizing mapping to determine the number of clusters, the center point and radius of each cluster.

[0056] Optionally, a self-organizing map (SOM) can be used to cluster the input data, mapping similar data features to adjacent neurons. This allows for unsupervised determination of the radial basis function (RBF) centerpoints. Traditional RBF networks require manual configuration of the number of hidden layer neurons during construction, which impacts network performance and complexity. The introduction of SOMs allows for dynamic determination of RBF centers based on the natural structure of the data. Each cluster corresponds to a RBF centerpoint, and both the cluster centerpoint and radius are determined simultaneously. This dynamic adjustment allows for better adaptation to data complexity and avoids overfitting or underfitting.

[0057] In one example, a radial basis function generalized regression model includes an input layer, a hidden layer, and an output layer. The hidden layer uses a Gaussian permutation function as a kernel function for spatial mapping. The Gaussian permutation function is expressed as:

[0058]

[0059] Among them, x i represents the i-th data in the training data, c j represents the center of the jth set, δ j represents the diffusion coefficient of the jth set.

[0060] Step S103 , in response to the error being less than the first threshold or the number of iterations being equal to the preset number of iterations, the iteration is stopped to obtain a stator winding end vibration fault diagnosis model.

[0061] Optionally, a gradient descent method is used as a training algorithm, the learning rate and regularization parameters are dynamically adjusted during the training process, the training error and convergence are monitored in real time, and the training is stopped when the training error reaches a first threshold or the number of iterations reaches a preset number of iterations.

[0062] In summary, the present application establishes a dynamic stator winding end vibration fault diagnosis model through a radial basis function generalized regression model. Through the Kendall correlation coefficient, the parameters with a high correlation with the stator winding end vibration are determined from the operating parameters, and redundant data are filtered out, thereby reducing the complexity of the input information and the model and improving the working efficiency of the model. Compared with the traditional monitoring method that relies on sensors to collect vibration data and perform fixed threshold alarms, the diagnostic model provided by the present application can more accurately predict the theoretical amplitude of the stator winding end under different working conditions and realize early fault warning. It helps to intervene and repair the problem before it expands, reduce the risk of unexpected shutdown, reduce maintenance costs and downtime, optimize the process and resource allocation of operation and maintenance management, and improve the accuracy and reliability of fault detection and the safety and stability of motor operation.

[0063] In a second aspect, an embodiment of the present application provides a method for diagnosing a stator winding end vibration fault. Figure 2 Flowchart of the stator winding end vibration fault diagnosis method provided in accordance with the embodiment of the present application, as shown in FIG. Figure 2 As shown, the method includes:

[0064] Step S201 : acquiring real-time operating parameters of the steam turbine generator, and obtaining a theoretical value of the stator winding end amplitude based on the real-time operating parameters through the stator winding end vibration fault diagnosis model of the first aspect.

[0065] Step S202 , calculating the deviation between the actual value of the amplitude monitored by the sensor and the theoretical value, and judging whether there is any abnormality in the vibration of the stator winding end according to the deviation.

[0066] In one example, step S202 includes: calculating a ratio of the deviation to the theoretical value, and determining that an abnormality exists in response to the ratio being greater than a third threshold.

[0067] Optionally, the presence of an abnormality can be determined based on the ratio of the deviation to the theoretical value. For example, when the ratio of the deviation to the theoretical value exceeds 10%, it is considered that there is an abnormality in the vibration of the stator winding end. At the same time, the abnormality level can also be determined based on the ratio of the deviation to the theoretical value. For example, when the ratio of the deviation to the theoretical value is 10% to 20%, it is a first-level abnormality; when the ratio of the deviation to the theoretical value is 20% to 40%, it is a second-level abnormality; when the ratio of the deviation to the theoretical value is 40% to 50%, it is a third-level abnormality. The higher the abnormality level, the greater the corresponding risk. The third threshold and the division of the abnormality level can be set according to the data characteristics during actual application, and are not limited to the data and methods listed in this application. In addition, emergency abnormalities can be given priority feedback to relevant professionals based on the risk level, so that abnormalities with higher risk levels can be handled in a timely manner to avoid causing greater losses.

[0068] In summary, the present application establishes a dynamic stator winding end vibration fault diagnosis model through a radial basis function generalized regression model, replacing the traditional monitoring method that relies on sensors to collect vibration data and perform fixed threshold alarms. The diagnostic model provided by the present application can more accurately predict the theoretical amplitude of the stator winding end under different working conditions, and achieve early fault warning. It helps to intervene and repair problems before they expand, reduce the risk of unexpected downtime, reduce maintenance costs and downtime, optimize the process and resource allocation of operation and maintenance management, and improve the accuracy and reliability of fault detection and the safety and stability of motor operation.

[0069] In a third aspect, an embodiment of the present application provides a system for obtaining a stator winding end vibration fault diagnosis model. Figure 3 is a structural block diagram of a stator winding end vibration fault diagnosis model acquisition system provided in an embodiment of the present application, such as Figure 3 As shown, the system includes:

[0070] Acquisition module 100: used to acquire historical operating data, which includes operating parameters and the stator winding end amplitude at the corresponding time. The target parameters are determined from the operating parameters through the Kendall correlation coefficient, and the training data is determined based on the target parameters and the corresponding stator winding end amplitude.

[0071] Training module 200: used to iteratively train the radial basis function generalized regression model based on training data and gradient descent method, obtain the theoretical value of the stator winding end amplitude, and calculate the error between the theoretical value and the true value.

[0072] Model module 300: used to stop iteration in response to the error being less than a first threshold or the number of iterations being equal to a preset number of iterations, and obtain a stator winding end vibration fault diagnosis model.

[0073] In one example, the radial basis function generalized regression model in the training module 200 is configured to use a Gaussian permutation function as a kernel function.

[0074] In one example, the radial basis function generalized regression model in the training module 200 is configured to cluster the features of the training data through self-organizing maps, and determine the number of clusters, the center point and radius of each cluster.

[0075] In one example, the radial basis function generalized regression model in the training module 200 includes an input layer, a hidden layer, and an output layer. The hidden layer is configured to perform spatial mapping using a Gaussian permutation function as a kernel function. The Gaussian permutation function is expressed as:

[0076]

[0077] Among them, x i represents the i-th data in the training data, c j represents the center of the jth set, δ j represents the diffusion coefficient of the jth set.

[0078] In one example, the Kendall correlation coefficient in the acquisition module 100 represents the correlation between the operating parameter and the amplitude. The calculation formula of the Kendall correlation coefficient is as follows:

[0079]

[0080] Where τ represents the Kendall correlation coefficient, N represents the number of historical operating data, C represents the number of logarithms of elements with positive correlation between each operating parameter and the variation pattern of the amplitude, and D represents the number of logarithms of elements with negative correlation between each operating parameter and the variation pattern of the amplitude.

[0081] In one example, the operating parameters in the acquisition module 100 include at least two types, and the acquisition module 100 includes: selecting an operating parameter whose absolute value of the Kendall correlation coefficient is greater than a second threshold as a target parameter.

[0082] In summary, the present application establishes a dynamic stator winding end vibration fault diagnosis model through a radial basis function generalized regression model. Through the Kendall correlation coefficient, the parameters with a high correlation with the stator winding end vibration are determined from the operating parameters, and redundant data are filtered out, thereby reducing the complexity of the input information and the model and improving the working efficiency of the model. Compared with the traditional monitoring method that relies on sensors to collect vibration data and perform fixed threshold alarms, the diagnostic model provided by the present application can more accurately predict the theoretical amplitude of the stator winding end under different working conditions and realize early fault warning. It helps to intervene and repair the problem before it expands, reduce the risk of unexpected shutdown, reduce maintenance costs and downtime, optimize the process and resource allocation of operation and maintenance management, and improve the accuracy and reliability of fault detection and the safety and stability of motor operation.

[0083] In a fourth aspect, an embodiment of the present application provides an electronic device, Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for obtaining a stator winding end vibration fault diagnosis model provided in the first aspect is implemented. Figure 4 The electronic device 60 shown is only an example and should not limit the functions and scope of use of the embodiments of the present application.

[0084] The electronic device 60 may be a general-purpose computing device, such as a server device. Components of the electronic device 60 may include, but are not limited to, the at least one processor 61, the at least one memory 62, and a bus 63 connecting different system components (including the memory 62 and the processor 61).

[0085] The bus 63 includes a data bus, an address bus, and a control bus.

[0086] The memory 62 may include a volatile memory, such as a random access memory (RAM) 621 and / or a cache memory 622 , and may further include a read-only memory (ROM) 623 .

[0087] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624, such program modules 624 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0088] The processor 61 executes various functional applications and data processing by running the computer program stored in the memory 62, such as the method for obtaining the stator winding end vibration fault diagnosis model provided in the first aspect of the present application.

[0089] The electronic device 60 can also communicate with one or more external devices 64 (e.g., a keyboard, pointing device, etc.). This communication can occur via an input / output (I / O) interface 65. Furthermore, the model-generating device 60 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 66. As shown, the network adapter 66 communicates with other modules of the model-generating device 60 via a bus 63. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the model-generating device 60, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0090] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above may be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above may be further divided and embodied by multiple units / modules.

[0091] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for obtaining a stator winding end vibration fault diagnosis model, characterized in that: include: Acquiring historical operating data, the historical operating data including operating parameters and stator winding end amplitudes at corresponding moments, determining target parameters from the operating parameters using a Kendall correlation coefficient, and determining training data based on the target parameters and the corresponding stator winding end amplitudes; Iteratively training a radial basis function generalized regression model based on the training data and a gradient descent method to obtain a theoretical value of the stator winding end amplitude, and calculating an error between the theoretical value and the true value; In response to the error being less than a first threshold or the number of iterations being equal to a preset number of iterations, the iteration is stopped to obtain a stator winding end vibration fault diagnosis model.

2. The method for obtaining a stator winding end vibration fault diagnosis model according to claim 1, characterized in that: The radial basis function generalized regression model is configured to use a Gaussian permutation function as a kernel function.

3. The method for obtaining a stator winding end vibration fault diagnosis model according to claim 2, characterized in that: The radial basis function generalized regression model is configured to cluster the features of the training data through self-organizing mapping, and determine the number of clusters, the center point and radius of each cluster.

4. The method for obtaining a stator winding end vibration fault diagnosis model according to claim 3, characterized in that: The radial basis function generalized regression model includes an input layer, a hidden layer, and an output layer. The hidden layer is configured to perform spatial mapping using a Gaussian permutation function as a kernel function. The Gaussian permutation function is expressed as: Among them, x i represents the i-th data in the training data, c j represents the center of the jth set, δ j represents the diffusion coefficient of the jth set.

5. The method for obtaining a stator winding end vibration fault diagnosis model according to claim 1, characterized in that: The Kendall correlation coefficient represents the correlation between the operating parameter and the amplitude. The calculation formula of the Kendall correlation coefficient is as follows: Wherein, τ represents the Kendall correlation coefficient, N represents the number of the historical operation data, C represents the number of element logarithms of each operating parameter being positively correlated with the change pattern of the amplitude, and D represents the number of element logarithms of each operating parameter being negatively correlated with the change pattern of the amplitude.

6. The method for obtaining a stator winding end vibration fault diagnosis model according to claim 5, characterized in that: The operating parameters include at least two types, and determining the target parameters from the operating parameters includes: An operating parameter whose absolute value of the Kendall correlation coefficient is greater than a second threshold is selected as a target parameter.

7. A method for diagnosing stator winding end vibration faults, characterized in that: include: Acquire real-time operating parameters of the steam turbine generator, and obtain a theoretical value of the stator winding end amplitude based on the real-time operating parameters using the stator winding end vibration fault diagnosis model according to any one of claims 1 to 6; The deviation between the actual value of the amplitude monitored by the sensor and the theoretical value is calculated, and whether there is abnormality in the vibration of the stator winding end is determined based on the deviation.

8. The stator winding end vibration fault diagnosis method according to claim 7, characterized in that: The determining whether the vibration of the stator winding end is abnormal according to the deviation includes: A ratio of the deviation to the theoretical value is calculated, and in response to the ratio being greater than a third threshold, it is determined that an abnormality exists.

9. A stator winding end vibration fault diagnosis model acquisition system, characterized in that: include: An acquisition module is configured to acquire historical operating data, the historical operating data including operating parameters and stator winding end amplitudes at corresponding moments, determine target parameters from the operating parameters using the Kendall correlation coefficient, and determine training data based on the target parameters and the corresponding stator winding end amplitudes; Training module: used for iteratively training the radial basis function generalized regression model based on the training data and the gradient descent method, obtaining the theoretical value of the stator winding end amplitude, and calculating the error between the theoretical value and the true value; Model module: used to stop iteration in response to the error being less than a first threshold or the number of iterations being equal to a preset number of iterations, so as to obtain a stator winding end vibration fault diagnosis model.

10. An electronic device, characterized in that: include Memory, processor, and A computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for obtaining a stator winding end vibration fault diagnosis model according to any one of claims 1 to 6 is implemented.

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