Generator set reliability determination method and device, and storage medium

Abstract

The invention discloses a method and device for determining the reliability of a generator set, a storage medium, an electronic device and a computer program product, and relates to the technical field of power generation. Determining whether the generator set is abnormal according to the operation data; under the condition that it is determined that the generator set is abnormal, determining an abnormal target component of the generator set based on the abnormal operation data and a preset fault analysis model; according to the operation data of the target component and a preset reliability prediction model, the reliability of the generator set is determined, and the reliability is used for indicating the fault probability of the generator set. By adopting the method, the reliability of the generator set can be evaluated in real time, and the running state of the generator set can be monitored in real time.

Description

Technical Field

[0001] The present application relates to the field of power generation technology, and in particular, to a method and device for determining the reliability of a power generation set, a storage medium, an electronic device, and a computer program product. Background Art

[0002] In recent years, with the increasing severity of the global energy crisis and environmental pollution, wind power generation, as a clean, efficient and renewable green energy, has become increasingly popular in countries around the world. In order to ensure maximum power generation efficiency, wind turbines are exposed to harsh environments such as wind, rain, and dust for a long time, and their components are prone to aging and failure, so they need to be maintained.

[0003] In the related art, manual maintenance is usually adopted to perform regular maintenance on the generator set at regular intervals to ensure the normal operation of the generator set.

[0004] However, the related technical methods cannot evaluate the real-time reliability of wind turbines. Summary of the invention

[0005] Embodiments of the present application provide a method and device for determining the reliability of a generator set, a storage medium, an electronic device, and a computer program product.

[0006] According to one aspect of an embodiment of the present application, a method for determining the reliability of a generator set is provided, the method comprising: acquiring operating data of the generator set; determining whether the generator set is abnormal based on the operating data; in the case where it is determined that the generator set is abnormal, determining a target component of the generator set where the abnormality occurs based on the abnormal operating data and a preset fault analysis model; determining the reliability of the generator set based on the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

[0007] In an exemplary embodiment, determining the reliability of the generator set based on the operating data of the target component and a preset reliability prediction model includes: determining the failure probability of the target component based on the operating data of the target component and the fault prediction model of the target component; determining the impact of the target component on the generator set according to the reliability prediction model; determining the reliability of the generator set according to the impact of the target component on the generator set, the failure probability of the target component, and the reliability prediction model.

[0008] In an exemplary embodiment, before determining the failure probability of the target component based on the abnormal operating data of the target component and the fault prediction model of the target component, the method for determining the reliability of the generator set also includes: acquiring historical working data of the generator set, wherein the historical working data includes fault data and working time data of multiple components of the generator set; determining degradation trend data of each of the components based on the historical working data, wherein the degradation trend data is used to indicate the relationship between the fault data and the working time data; and establishing a fault prediction model of each of the components according to the degradation trend data of each of the components, wherein the fault prediction model is used to predict the failure probability of the component based on the working parameters of the component.

[0009] In an exemplary embodiment, before determining the influence of the target component on the generator set according to the reliability prediction model, the method for determining the reliability of the generator set includes: determining multiple components of the generator set and the working relationship between the components according to the structural principle of the generator set; constructing a reliability block diagram of the generator set based on the multiple components of the generator set and the working relationship between the components, wherein the reliability block diagram is used to indicate the influence of each component on the performance of the generator set; mapping the reliability block diagram of the generator set to a preset first probability reasoning model to establish the reliability prediction model, wherein the reliability prediction model is used to determine the failure probability of the generator set under a first target condition, wherein the first target condition includes the influence of the target component on the generator set and the failure probability of the target component.

[0010] In an exemplary embodiment, before determining the target component of the generator set having an abnormality based on the abnormal operating data and a preset fault analysis model, the method for determining the reliability of the generator set includes: acquiring operating data of multiple components in the generator set when an abnormality occurs; establishing a corresponding relationship between the multiple components and the corresponding abnormal operating data; mapping the corresponding relationship between the multiple components and the corresponding abnormal operating data to a preset second probability reasoning model to establish the fault analysis model, wherein the fault analysis model is used to determine the failure probability of each component under a second target condition, and the second target condition includes the operating data of the component.

[0011] In an exemplary embodiment, after determining whether the generator set has an abnormality based on the operating data, the method for determining the reliability of the generator set further includes: when it is determined that the generator set has no abnormality, determining the reliability of the generator set based on the operating data of each component of the generator set and a preset reliability prediction model.

[0012] According to another aspect of an embodiment of the present application, a device for determining reliability of a generator set is also provided, the device comprising:

[0013] A data acquisition module, used to acquire the operating data of the generator set;

[0014] An abnormality judgment module, used to determine whether the generator set has an abnormality according to the operating data;

[0015] A fault analysis module is used to determine the target component of the generator set where the abnormality occurs based on the abnormal operation data and a preset fault analysis model when it is determined that the generator set has an abnormality;

[0016] A reliability evaluation module is used to determine the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

[0017] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the reliability of the generator set when running.

[0018] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the reliability of a generator set through the computer program.

[0019] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0020] The above-mentioned method for determining the reliability of the generator set first obtains the operating data of the generator set. Then, it is determined whether the generator set is abnormal based on the operating data. In the case of determining that the generator set is abnormal, based on the abnormal operating data and the preset fault analysis model, the target component of the generator set with abnormality is determined, thereby realizing the analysis of the cause of the fault of the generator set and finding the target component with abnormality. Then, based on the operating data of the target component and the preset reliability prediction model, the reliability of the generator set is determined, and the reliability of the generator set is evaluated based on the operating status of the target component with abnormality and the reliability prediction model. Since the operating data of the generator set can be obtained in real time, the method can realize real-time evaluation of the reliability of the generator set and can monitor the operating status of the generator set in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 is a hardware structure block diagram of a method for determining reliability of a generator set according to an embodiment of the present application;

[0024] Figure 2 is a flow chart of a method for determining reliability of a generator set according to an embodiment of the present application;

[0025] Figure 3 is a second flow chart of a method for determining reliability of a generator set according to an embodiment of the present application;

[0026] Figure 4 is a flow chart of a method for establishing a fault prediction model for each component according to an embodiment of the present application;

[0027] Figure 5 is a flow chart of a method for establishing a reliability prediction model according to an embodiment of the present application;

[0028] Figure 6 is a flow chart of a method for establishing a fault analysis model according to an embodiment of the present application;

[0029] Figure 7 is a third flow chart of a method for determining reliability of a generator set according to an embodiment of the present application;

[0030] Figure 8 It is a structural block diagram of a device for determining the reliability of a generator set according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal of a method for determining the reliability of a generator set according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a microprocessor (Microprocessor Unit, referred to as MPU) or a programmable logic device (Programmable logic device, referred to as PLD)) and a memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0034] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the reliability of the generator set in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] In view of the above problems, a method for determining the reliability of a generator set is provided in this embodiment. Figure 2 : is a flow chart of an optional method for determining the reliability of a generator set according to an embodiment of the present application, the process includes the following steps S200-S230:

[0037] Step S200, obtaining the operating data of the generator set.

[0038] Among them, a key part of the generator set is the yaw system, which is responsible for keeping the wind rotor and the nacelle in the best wind direction to maximize the power generation efficiency of the generator set. The yaw system has a complex structure and is the part with the worst operating environment in the wind turbine generator set. The probability of its failure is much higher than other parts. Therefore, when evaluating the reliability of the generator set, the yaw system can be mainly considered. The main components of the yaw system include the yaw motor, gearbox, and brake. The operating data of the yaw motor, gearbox, and brake can be obtained, such as the current, temperature, torque, etc. of the yaw motor.

[0039] Step S210, determining whether the generator set is abnormal based on the operating data.

[0040] After the operation data is obtained, each operation data has a pre-calibrated normal range. For example, the temperature between T1 and T2 is the normal range. If the temperature of the yaw motor obtained exceeds this range, it means that an abnormality has occurred. The same is true for other types of operation data.

[0041] Step S220, when it is determined that the generator set is abnormal, based on the abnormal operation data and a preset fault analysis model, determine the target component of the generator set where the abnormality occurs.

[0042] Among them, when it is determined that the generator set is abnormal, the abnormal operating data (such as current, temperature, etc.) can be substituted into the fault analysis model to obtain the target component of the generator set with abnormality. The fault analysis model can analyze the cause and effect relationship based on the abnormal operating data (for example, the yaw motor failure may cause abnormal current and temperature increase), so as to determine the most likely cause of the abnormal operating data, that is, the target component with the highest probability of abnormality.

[0043] Step S230, determining the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model.

[0044] Among them, reliability is used to indicate the failure probability of a generator set.

[0045] Specifically, after the target component with an abnormality is determined, its operating status is substituted into the reliability prediction model, the system status of the entire generator set is updated, and then the reliability of the entire generator set is inferred and predicted.

[0046] In this embodiment, the operating data of the generator set is first obtained. Then, it is determined whether the generator set is abnormal based on the operating data. In the case of determining that the generator set is abnormal, based on the abnormal operating data and the preset fault analysis model, the target component of the generator set with abnormality is determined, thereby realizing the analysis of the cause of the fault of the generator set and finding the target component with abnormality. Then, based on the operating data of the target component and the preset reliability prediction model, the reliability of the generator set is determined, and the reliability of the generator set is evaluated based on the operating status of the target component with abnormality and the reliability prediction model. Since the operating data of the generator set can be obtained in real time, the method can realize real-time evaluation of the reliability of the generator set and can monitor the operating status of the generator set in real time.

[0047] In one embodiment, Figure 3 As shown, step S230 determines the reliability of the generator set according to the operating data of the target component and the preset reliability prediction model. It includes: steps S300-S320.

[0048] Step S300 , determining the failure probability of the target component based on the operating data of the target component and the failure prediction model of the target component.

[0049] Among them, a fault prediction model of the target component can be established in advance, and the fault prediction model can predict the failure probability of the target component based on the operating data of the component. It can be a statistical analysis of the operating degradation of the components of the yaw system in advance, and through the Weibull distribution fitting method, the degradation process of the equipment can be more accurately described, the degradation curve of the component can be established, and the key parameters affecting the degradation of the component, such as failure rate, shape parameters and characteristic life, can be calculated based on the degradation curve, and then the fault prediction model can be established.

[0050] Step S310: determining the impact of the target component on the generator set according to the reliability prediction model.

[0051] Among them, the component functions of each component of the generator set and the impact of failures on the overall performance of the generator set can be analyzed in advance from a system perspective, and then the impact of each component is recorded to obtain a reliability block diagram. The reliability prediction model includes a reliability block diagram, so the impact of the target component on the generator set can be directly determined by looking up the table. For example, the main function of the yaw system is to adjust the direction of the wind turbine to capture the best wind direction, so the impact of each component can be defined based on its importance in achieving this function.

[0052] Step S320, determining the reliability of the generator set according to the impact of the target component on the generator set, the failure probability of the target component, and the reliability prediction model.

[0053] Among them, the reliability prediction model can be a model based on a dynamic Bayesian network (DBN), which can represent and reason about probability relationships that change over time. DBN can process time series data and predict the probability of future events. Therefore, based on the failure probability of the target component and the impact of the target component on the generator set as the adjustment of the prediction, the probability of failure of the generator set under such conditions can be predicted, that is, the reliability of the generator set.

[0054] In this embodiment, the failure probability of the target component is first determined, and then the impact of the target component on the generator set is determined. Based on the reliability prediction model, the failure probability of the target component and the impact of the target component on the generator set are input into the model as conditions, the input state in the model is updated, and then the model is used to dynamically evaluate the reliability of the generator set according to the latest conditions. The evaluation result is accurate, so that the fault prediction of the generator set can be accurately realized, unnecessary maintenance costs can be reduced, and the operating efficiency and economy of the wind farm can be improved.

[0055] In one embodiment, Figure 4 As shown, in step S300, the failure probability of the target component is determined based on the operating data of the target component and the failure prediction model of the target component. Previously, the method for determining the reliability of the generator set also includes: steps S400-S420.

[0056] Step S400, obtaining historical operating data of the generator set.

[0057] The historical working data includes the fault data and working time data of multiple components of the generator set. The historical fault data of the yaw system can be obtained from the maintenance records of the wind farm, including the use time, fault time and fault cause of the yaw motor, gearbox and brake.

[0058] Step S410, determining degradation trend data of each component based on historical working data.

[0059] Among them, degradation trend data is used to indicate the relationship between fault data and working time data. The historical working data can be analyzed to identify the main causes of faults and their frequency of occurrence. At the same time, the degradation trend of the equipment can be observed through the relationship between the use time and the downtime. For example, through the analysis results, it can be concluded that the main causes of yaw system failures are damage to the yaw inverter, damage to the reducer, and the need to adjust the yaw torque. It can also be analyzed that as the use time increases, the downtime of the equipment also shows an increasing trend, indicating that the failure rate of the equipment increases after a long period of operation.

[0060] Step S420: establishing a fault prediction model for each component based on the degradation trend data of each component.

[0061] Among them, the fault prediction model is used to predict the failure probability of a component based on the working parameters of the component.

[0062] For example, a Weibull distribution may be used to fit the data and establish a degradation curve.

[0063] The Weibull distribution formula is as follows:

[0064]

[0065] Where F(t) is the probability of failure at time t, η is the scale parameter (characteristic lifetime), also known as the characteristic lifetime, which describes the scale of the distribution and represents what percentage of devices will fail. β is the shape number, which affects the shape of the distribution. The value of the shape number determines whether the failure rate increases, decreases, or remains the same over time.

[0066] Furthermore, key parameters affecting device degradation, such as failure rate, shape parameters, and characteristic life, can be identified and calculated.

[0067] Calculate key parameters such as failure rate, shape parameter and characteristic life according to the Weibull distribution formula. Failure rate function:

[0068]

[0069] Among them, the failure rate function represents the failure probability capsule of the equipment at time t, that is, the ratio of the probability of failure of the equipment between time t and time t+Δt to time Δt, and the limit when Δt tends to 0. The failure rate function is an important indicator of the reliability of the equipment. The β shape parameter determines the trend of the failure rate over time. When β>1, it means that the failure rate increases with time (wear failure); when β<1, it means that the failure rate decreases with time (initial failure reduction); when β=1, the failure rate is constant (exponential distribution). The η scale parameter, also known as the characteristic life, means that when t=n, the failure rate (t)=1. It reflects the average life of the equipment. By fitting the actual failure data to the Weibull distribution, the shape parameter β and the scale parameter η can be estimated. These parameters can help us understand the degradation behavior of the equipment and predict the future failure probability. Through these parameters. We can evaluate the reliability of the equipment and formulate corresponding maintenance strategies to extend the service life of the equipment and improve the reliability of the system.

[0070] In this embodiment, by acquiring the historical working data of each component and fitting the degradation trend data of each component, a fault prediction model of each component can be further established to facilitate accurate judgment and prediction of the failure conditions of each component. The reliability of the equipment can be evaluated and corresponding maintenance strategies can be formulated to extend the service life of the equipment and improve the reliability of the system.

[0071] In one embodiment, Figure 5 As shown, in step S310, the influence of the target component on the generator set is determined according to the reliability prediction model. Previously, the method for determining the reliability of the generator set includes: steps S500-S520.

[0072] Step S500, determining multiple components of the generator set and the working relationship between the components according to the structural principle of the generator set.

[0073] Among them, the structural composition and functional logic relationship of the yaw system of the wind turbine can be analyzed to determine the key components and their relationships.

[0074] Step S510: construct a reliability block diagram of the generator set based on multiple components of the generator set and the working relationships between the components.

[0075] Among them, it can be to draw a reliability block diagram of the wind turbine yaw system. First, obtain the element diagram of the device or system, and then analyze the impact of component functions and failures on device performance from a system perspective. Finally, the reliability block diagram (RBDGG) is determined by using blocks and general gates to represent components and influence relationships respectively. Blocks can be used in the diagram to represent the various components of the device, and connections can be used to represent the relationship between components. General gates (such as OR gates and AND gates) are introduced to represent the joint failure conditions of multiple components.

[0076] The reliability block diagram is used to indicate the impact of each component on the performance of the generator set.

[0077] After the RBDGG is established, the obtained model can be directly mapped to the corresponding DBN structure using a one-to-one matching strategy. The blocks (component nodes) of the RBDGG are converted into parent nodes of the DBN, the universal gates are converted into intermediate nodes, and the destination gates are converted into leaf nodes. The arcs in the DBN remain consistent with those in the RBDGG model.

[0078] Step S520, mapping the reliability block diagram of the generator set into a preset first probability reasoning model to establish a reliability prediction model.

[0079] The reliability prediction model is used to determine the failure probability of the generator set under the first target condition, and the first target condition includes the impact of the target component on the generator set and the failure probability of the target component.

[0080] Among them, the reliability prediction model can be constructed using the DBN model, and the parameters of the DBN include the conditional probability of events in each time slice and the conditional probability of events between two time slices. In the DBN model, the failure rate function of each node can be calculated using the Weibull distribution based on the degradation data of the component, and its conditional probability distribution between two time slices can be determined.

[0081] In this embodiment, the reliability prediction model can be used to dynamically adjust the reliability assessment parameters according to the latest component status and environmental conditions, so that the assessment results are closer to the actual working conditions, providing more reliable data support for the operation and maintenance decision-making of the wind farm. The application of this method can significantly improve the operating safety and economy of wind turbines, reduce downtime caused by component failures, improve the overall benefits of wind farms, provide comprehensive and accurate data support for the intelligent operation and maintenance of wind farms, and ensure the efficient and safe operation of wind farms.

[0082] In one embodiment, Figure 6As shown, in step S220, when it is determined that the generator set is abnormal, based on the abnormal operation data and the preset fault analysis model, the target component of the generator set with abnormality is determined. Previously, the method for determining the reliability of the generator set includes: steps S600-S620.

[0083] Step S600, obtaining operation data when multiple components in the generator set are abnormal.

[0084] Among them, the historical operation data of multiple components of the generator set when abnormalities occur can be obtained, such as abnormal current, temperature increase, torque increase, etc. when the yaw motor fails. Specifically, key components that may fail in the equipment (such as yaw motor, gearbox, brake) can be determined as fault nodes, and fault symptoms that can be monitored (such as abnormal current, temperature increase, torque increase) can be determined as symptom nodes.

[0085] Step S610: establishing corresponding relationships between multiple components and corresponding abnormal operation data.

[0086] Specifically, the causal relationship between the fault node and the symptom node can be analyzed based on expert experience and actual data. For example, a yaw motor failure may cause abnormal current and increased temperature.

[0087] Step S620, mapping the corresponding relationships between the multiple components and the corresponding abnormal operation data into a preset second probability reasoning model to establish a fault analysis model.

[0088] Specifically, the fault nodes and symptom nodes and their causal relationships are mapped into a second probabilistic reasoning model (DBN) to form a comprehensive fault inference network, and a fault analysis model is established based on the DBN model.

[0089] The fault analysis model is used to determine the fault probability of each component under the second target condition, and the second target condition includes the operation data of the component. By using the fault analysis model, the fault probability of the component can be predicted when the real-time operation data of the component is input.

[0090] Exemplarily, the conditional probability between the faulty node and the symptomatic node can be determined using the leaky Noisy-OR model. 0 ) and other probabilities (p i ) can be determined through expert consultation or historical data.

[0091] Leakage Noisy-OR model formula:

[0092]

[0093] Among them, Y is the symptom node, X iis the faulty node, p 0 is the leakage probability, p i Is the faulty node X i probability.

[0094] In this embodiment, a dynamic Bayesian network is constructed based on functional logical relationships for inferring the root cause of faults. This method can more comprehensively consider the mutual influence between devices and improve the accuracy and reliability of inferring the root cause of faults. In the fault diagnosis system of a wind farm, the application of a dynamic Bayesian network can significantly improve the fault diagnosis capability of the system, accurately determine the cause of the fault, and reduce misdiagnosis and missed diagnosis.

[0095] In one embodiment, Figure 7 As shown, the method for determining the reliability of the generator set also includes: step S240, when it is determined that the generator set has no abnormal situation, determining the reliability of the generator set according to the operating data of each component of the generator set and a preset reliability prediction model.

[0096] In this embodiment, when there is no abnormal situation in the generator set, the reliability of the generator set can still be determined based on the operating data of the various components of the generator set and the preset reliability prediction model, thereby realizing real-time monitoring of the reliability of the generator set. The reliability of the generator set can be evaluated in real time when the generator set is normal, and potential problems can be discovered and handled in time to avoid the expansion of faults and reduce economic losses.

[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0098] In this embodiment, a device for determining the reliability of a generator set is also provided. The device for determining the reliability of a generator set is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0099] Figure 8is a structural block diagram of an optional device for determining the reliability of a generator set according to an embodiment of the present application; Figure 8 As shown, including:

[0100] The data acquisition module 801 is used to acquire the operating data of the generator set;

[0101] The abnormality judgment module 802 is used to determine whether the generator set has an abnormality according to the operation data;

[0102] The fault analysis module 803 is used to determine the target component of the generator set where the abnormality occurs based on the abnormal operation data and a preset fault analysis model when it is determined that the generator set has an abnormality;

[0103] The reliability evaluation module 804 is used to determine the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

[0104] Through the above device, the operating data of the generator set is first obtained. Then, it is determined whether the generator set is abnormal based on the operating data. In the case of determining that the generator set is abnormal, based on the abnormal operating data and the preset fault analysis model, the target component of the generator set with abnormality is determined, thereby realizing the analysis of the cause of the fault of the generator set and finding the target component with abnormality. Then, based on the operating data of the target component and the preset reliability prediction model, the reliability of the generator set is determined, and the reliability of the generator set is evaluated based on the operating state of the target component with abnormality and the reliability prediction model. Since the operating data of the generator set can be obtained in real time, the method can realize real-time evaluation of the reliability of the generator set and can monitor the operating state of the generator set in real time.

[0105] In an exemplary embodiment, the reliability assessment module 804 is further used to determine the failure probability of the target component based on the operation data of the target component and the failure prediction model of the target component;

[0106] Determine the impact of the target component on the generator set based on the reliability prediction model;

[0107] The reliability of the generator set is determined based on the impact of the target component on the generator set, the failure probability of the target component, and the reliability prediction model.

[0108] In an exemplary embodiment, the above device is also used to obtain historical operating data of the generator set, wherein the historical operating data includes fault data and operating time data of multiple components of the generator set;

[0109] Determine degradation trend data of each component based on the historical working data, wherein the degradation trend data is used to indicate the relationship between the failure data and the working time data;

[0110] According to the degradation trend data of each component, a fault prediction model of each component is established, wherein the fault prediction model is used to predict the failure probability of the component based on the working parameters of the component.

[0111] In an exemplary embodiment, the above-mentioned device is also used to determine multiple components of the generator set and the working relationship between the components according to the structural principle of the generator set; construct a reliability block diagram of the generator set based on the multiple components of the generator set and the working relationship between the components, wherein the reliability block diagram is used to indicate the influence of each component on the performance of the generator set; map the reliability block diagram of the generator set to a preset first probability reasoning model to establish a reliability prediction model, wherein the reliability prediction model is used to determine the failure probability of the generator set under a first target condition, and the first target condition includes the influence of the target component on the generator set and the failure probability of the target component.

[0112] In an exemplary embodiment, the above device is also used to obtain operating data when multiple components in the generator set are abnormal;

[0113] Establishing corresponding relationships between multiple components and corresponding abnormal operation data;

[0114] The corresponding relationships between multiple components and corresponding abnormal operation data are mapped to a preset second probability reasoning model to establish a fault analysis model, wherein the fault analysis model is used to determine the failure probability of each component under a second target condition, and the second target condition includes the operation data of the component.

[0115] In an exemplary embodiment, the above-mentioned device is also used to determine the reliability of the generator set based on the operating data of each component of the generator set and a preset reliability prediction model when it is determined that the generator set has no abnormal situation.

[0116] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0117] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0118] S1, obtain the operating data of the generator set.

[0119] S2, determining whether the generator set is abnormal based on the operating data.

[0120] S3, when it is determined that the generator set is abnormal, based on the abnormal operation data and a preset fault analysis model, determine the target component of the generator set where the abnormality occurs.

[0121] S4, determining the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

[0122] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0123] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0124] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0125] S1, obtain the operating data of the generator set.

[0126] S2, determining whether the generator set is abnormal based on the operating data.

[0127] S3, when it is determined that the generator set is abnormal, based on the abnormal operation data and a preset fault analysis model, determine the target component of the generator set where the abnormality occurs.

[0128] S4, determining the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

[0129] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0130] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores the computer program product, and when the computer program is executed by a processor, the steps of the method in each embodiment of the present application are implemented.

[0131] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0132] S1, obtain the operating data of the generator set.

[0133] S2, determining whether the generator set is abnormal based on the operating data.

[0134] S3, when it is determined that the generator set is abnormal, based on the abnormal operation data and a preset fault analysis model, determine the target component of the generator set where the abnormality occurs.

[0135] S4, determining the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

[0136] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0137] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0138] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining the reliability of a generator set, characterized in that: The method comprises: Acquiring operating data of the generator set; Determine whether the generator set is abnormal according to the operating data; In the case where it is determined that the generator set is abnormal, based on the abnormal operation data and a preset fault analysis model, determining a target component of the generator set where the abnormality occurs; The reliability of the generator set is determined according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

2. The method for determining the reliability of a generator set according to claim 1, characterized in that: Determining the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model includes: Determining a failure probability of the target component based on the operating data of the target component and a failure prediction model of the target component; Determining the influence of the target component on the generator set according to the reliability prediction model; The reliability of the generator set is determined according to the influence of the target component on the generator set, the failure probability of the target component, and the reliability prediction model.

3. The method for determining the reliability of a generator set according to claim 2, characterized in that: Before determining the failure probability of the target component based on the abnormal operation data of the target component and the failure prediction model of the target component, the method further includes: Acquiring historical operating data of the generator set, wherein the historical operating data includes fault data and operating time data of multiple components of the generator set; Determining degradation trend data of each of the components based on the historical working data, wherein the degradation trend data is used to indicate a relationship between the fault data and the working time data; A fault prediction model for each of the components is established according to the degradation trend data of each of the components, wherein the fault prediction model is used to predict the failure probability of the component based on the working parameters of the component.

4. The method for determining the reliability of a generator set according to claim 2, characterized in that: Before determining the influence of the target component on the generator set according to the reliability prediction model, the method includes: According to the structural principle of the generator set, determining multiple components of the generator set and the working relationship between the components; Based on the multiple components of the generator set and the working relationship between the components, a reliability block diagram of the generator set is constructed, wherein the reliability block diagram is used to indicate the influence of each component on the performance of the generator set; The reliability block diagram of the generator set is mapped into a preset first probability reasoning model to establish the reliability prediction model, wherein the reliability prediction model is used to determine the failure probability of the generator set under a first target condition, and the first target condition includes the impact of the target component on the generator set and the failure probability of the target component.

5. The method for determining the reliability of a generator set according to claim 1, characterized in that: Before determining the target component of the generator set having an abnormality based on the abnormal operation data and the preset fault analysis model, the method includes: Acquiring operation data when multiple components in the generator set are abnormal; Establishing corresponding relationships between the plurality of components and corresponding abnormal operation data; The corresponding relationships between the multiple components and the corresponding abnormal operating data are mapped into a preset second probability reasoning model to establish the fault analysis model, wherein the fault analysis model is used to determine the failure probability of each component under a second target condition, and the second target condition includes the operating data of the component.

6. The method for determining the reliability of a generator set according to claim 1, characterized in that: After determining whether the generator set is abnormal according to the operating data, the method further includes: When it is determined that the generator set has no abnormal condition, the reliability of the generator set is determined based on the operating data of each component of the generator set and a preset reliability prediction model.

7. A device for determining the reliability of a generator set, characterized in that: The device comprises: A data acquisition module, used to acquire the operating data of the generator set; An abnormality judgment module, used to determine whether the generator set has an abnormality according to the operating data; A fault analysis module is used to determine the target component of the generator set where the abnormality occurs based on the abnormal operation data and a preset fault analysis model when it is determined that the generator set has an abnormality; A reliability evaluation module is used to determine the reliability of the generator set according to the operating data of the target component and a preset reliability prediction model, wherein the reliability is used to indicate the failure probability of the generator set.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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